From 719f809d06ab3b2ddd12809b2431439ba585c690 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Wed, 7 Jan 2026 11:30:48 -0600 Subject: [PATCH 01/19] object type addition --- .gitignore | 6 + README.md | 43 +- app/models.py | 7 + app/routes.py | 17 +- app/utils/workspace.py | 88 ++ docs/ARCHITECTURE.md | 19 + docs/USAGE_GUIDE.md | 61 +- static/viewer.html | 2168 ++++++++++++++++++++++------------------ 8 files changed, 1409 insertions(+), 1000 deletions(-) diff --git a/.gitignore b/.gitignore index 5ec0315..c7623f8 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ trash/ docs/DEMO_SCRIPT.md docs/QUICKSTART.md docs/internal/ +DATABASE_SCHEMA.md .DS_Store .idea @@ -31,3 +32,8 @@ lib/ # Cache directory cache/ + +# Project-specific artifacts +DATABASE_SCHEMA.md +*.webp +*.png diff --git a/README.md b/README.md index 4fc1c96..ac94cb9 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,12 @@ # TableScanner -TableScanner is a microservice for providing filtered and paginated access to tabular data stored in KBase. It uses local SQLite caching and indexing to provide fast access to large datasets without loading them entirely into memory. - +TableScanner is a microservice for providing filtered and paginated access to tabular data stored in KBase. ## Functionality The service provides two methods for data access: -1. **Hierarchical REST**: Path-based endpoints for navigating objects and tables using GET requests. -2. **Flat POST**: A single endpoint (`/table-data`) that accepts a JSON payload for all query parameters. +1. **Hierarchical REST**: Path-based endpoints for navigating objects and tables using GET requests (includes object type detection). +2. **Flat POST**: A single endpoint (`/table-data`) for programmatic queries. + ## Architecture @@ -16,7 +16,18 @@ TableScanner operates as a bridge between KBase storage and client applications: 3. **Indexing**: Creates indices on-the-fly for all table columns to optimize query performance. 4. **API Layer**: A FastAPI application that handles requests and executes SQL queries against the local cache. -Technical details on race conditions and concurrency handling are available in [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md). +Technical details on race conditions, UI design, and concurrency are available in [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md). + +## Web Explorer + +Access the interactive **Research Data Explorer** at: +`http://localhost:8000/static/viewer.html` + +Features: +- **Sidebar-First Navigation**: IDE-like experience for pangenome and table selection. +- **Scientific Modern UI**: Light-themed, high-density interface with premium typography. +- **Interactive Tools**: Global search, column visibility controls, and density toggles. +- **Performance**: Instant filtering and sticky headers for a research-grade experience. ## Setup @@ -35,11 +46,24 @@ bash scripts/dev.sh ## API Usage ### Path-based REST -List tables: +List tables and identify object type: `GET /object/{upa}/tables` +**Example Response**: +```json +{ + "berdl_table_id": "76990/7/2", + "object_type": "KBaseFBA.GenomeDataLakeTables-2.0", + "tables": [ + {"name": "Genes", "row_count": 3356, "column_count": 18}, + {"name": "Metadata_Conditions", "row_count": 42, "column_count": 12} + ], + "source": "Cache" +} +``` + Query table data: -`GET /object/{upa}/tables/{table_name}/data?limit=100` +`GET /object/{upa}/tables/{table_name}/data?limit=5` ### Flat POST Query table data: @@ -56,8 +80,9 @@ Payload example: ## Project Structure - `app/`: Application logic and routes. -- `app/utils/`: Utilities for caching, SQLite operations, and Workspace integration. -- `docs/`: Technical documentation. +- `app/utils/`: Utilities for caching, SQLite, and KBase Workspace integration. +- `static/`: Production-grade Web Explorer (`viewer.html`). +- `docs/`: Technical documentation and usage guides. - `scripts/`: Client examples and utility scripts. ## License diff --git a/app/models.py b/app/models.py index f24fbfd..d153247 100644 --- a/app/models.py +++ b/app/models.py @@ -109,6 +109,7 @@ class TableListResponse(BaseModel): """Response for listing tables in a database.""" berdl_table_id: str | None = Field(None, description="BERDLTable object reference", examples=["76990/7/2"]) handle_ref: str | None = Field(None, description="Blobstore handle reference", examples=["KBH_248028"]) + object_type: str | None = Field(None, description="KBase object type", examples=["KBaseGeneDataLakes.BERDLTables-1.0"]) tables: list[TableInfo] = Field( default_factory=list, description="List of available tables", @@ -145,6 +146,7 @@ class PangenomeInfo(BaseModel): class PangenomesResponse(BaseModel): """Response for listing pangenomes from a BERDLTables object.""" berdl_table_id: str | None = Field(None, description="BERDLTable object reference", examples=["76990/7/2"]) + object_type: str | None = Field(None, description="KBase object type", examples=["KBaseGeneDataLakes.BERDLTables-1.0"]) pangenomes: list[PangenomeInfo] = Field( default_factory=list, description="List of available pangenomes", @@ -218,6 +220,11 @@ class TableDataResponse(BaseModel): None, description="Path to SQLite database" ) + object_type: str | None = Field( + None, + description="KBase object type", + examples=["KBaseGeneDataLakes.BERDLTables-1.0"] + ) model_config = { "json_schema_extra": { diff --git a/app/routes.py b/app/routes.py index 12abb08..6dc48d3 100644 --- a/app/routes.py +++ b/app/routes.py @@ -34,6 +34,7 @@ from app.utils.workspace import ( list_pangenomes_from_object, download_pangenome_db, + get_object_type, ) from app.utils.sqlite import ( list_tables, @@ -389,9 +390,16 @@ async def list_tables_by_object( logger.warning("Error getting table info for %s", name, exc_info=True) tables.append({"name": name}) + # Get object type + try: + object_type = get_object_type(berdl_table_id, token, kb_env) + except Exception: + object_type = None + return { "berdl_table_id": berdl_table_id, "tables": tables, + "object_type": object_type, "source": "Cache" if (db_path.exists() and db_path.stat().st_size > 0) else "Downloaded" } @@ -451,6 +459,12 @@ async def get_table_data_by_object( response_time_ms = (time.time() - start_time) * 1000 + # Get object type + try: + object_type = get_object_type(berdl_table_id, token, kb_env) + except Exception: + object_type = None + return { "berdl_table_id": berdl_table_id, "table_name": table_name, @@ -461,7 +475,8 @@ async def get_table_data_by_object( "filtered_count": filtered_count, "response_time_ms": response_time_ms, "db_query_ms": db_query_ms, - "sqlite_file": str(db_path) + "sqlite_file": str(db_path), + "object_type": object_type } except HTTPException: diff --git a/app/utils/workspace.py b/app/utils/workspace.py index b5cf86b..8f09abd 100644 --- a/app/utils/workspace.py +++ b/app/utils/workspace.py @@ -185,6 +185,73 @@ def _get_object_fallback(self, ref: str, ws: int | None = None) -> dict[str, Any raise ValueError(f"No data for: {ref}") return data_list[0] + + def get_object_with_type(self, ref: str, ws: int | None = None) -> tuple[dict[str, Any], str]: + """ + Get workspace object data along with its type. + + Args: + ref: Object reference or name + ws: Workspace ID (optional if ref is full reference) + + Returns: + Tuple of (object_data, object_type) + object_type is the full KBase type string (e.g., "KBaseFBA.GenomeDataLakeTables-2.0") + """ + # Build reference + if ws and "/" not in str(ref): + ref = f"{ws}/{ref}" + + # First get the object type using get_object_info3 + object_type = self._get_object_type(ref) + + # Then get the data using standard method + obj_data = self.get_object(ref) + + return obj_data, object_type + + def _get_object_type(self, ref: str) -> str: + """ + Get the KBase object type using Workspace.get_object_info3. + + Args: + ref: Object reference + + Returns: + Object type string (e.g., "KBaseFBA.GenomeDataLakeTables-2.0") + """ + headers = { + "Authorization": self.token, + "Content-Type": "application/json" + } + + payload = { + "method": "Workspace.get_object_info3", + "params": [{"objects": [{"ref": ref}]}], + "version": "1.1", + "id": "tablescanner-type" + } + + endpoints = self._get_endpoints() + response = requests.post( + endpoints["workspace"], + json=payload, + headers=headers, + timeout=30 + ) + response.raise_for_status() + result = response.json() + + if "error" in result: + logger.warning(f"Error getting object type: {result['error']}") + return "Unknown" + + # get_object_info3 returns: {"result": [{"infos": [[objid, name, type, ...]]}]} + infos = result.get("result", [{}])[0].get("infos", []) + if infos and infos[0] and len(infos[0]) > 2: + return infos[0][2] + + return "Unknown" def _download_blob_fallback(self, handle_ref: str, target_path: str) -> str: """Download from blobstore via direct API.""" @@ -275,6 +342,27 @@ def get_berdl_table_data( return obj +def get_object_type( + berdl_table_id: str, + auth_token: str, + kb_env: str = "appdev" +) -> str: + """ + Get the KBase object type for a workspace object. + + Args: + berdl_table_id: KBase workspace reference (e.g., "76990/7/2") + auth_token: KBase authentication token + kb_env: KBase environment + + Returns: + Object type string (e.g., "KBaseGeneDataLakes.BERDLTables-1.0") + """ + client = KBaseClient(auth_token, kb_env) + _, object_type = client.get_object_with_type(berdl_table_id) + return object_type + + def list_pangenomes_from_object( berdl_table_id: str, auth_token: str, diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md index 8d3cc5a..a3f4881 100644 --- a/docs/ARCHITECTURE.md +++ b/docs/ARCHITECTURE.md @@ -83,3 +83,22 @@ TableScanner doesn't just store the data; it optimizes it. Upon the **first acce - **SQLite**: The storage engine for tabular data, chosen for its zero-configuration and high performance with indices. - **KBUtilLib**: Handles complex KBase Workspace and Blobstore interactions. - **UUID-based Temp Storage**: Prevents race conditions during file I/O. + +--- + +## Web Interface Architecture + +The **Research Data Explorer** is a production-grade single-file SPA (`static/viewer.html`) designed with a "Scientific Modern" aesthetic. + +### 1. Sidebar-First Layout +To mimic the feel of modern IDEs, navigation is concentrated in a fixed left sidebar. This keeps the "Main Stage" focused on the data grid. +- **Navigation Flow**: Connection → Pangenome Selection → Table Selection → Data Load. + +### 2. Performance-Centric UI +- **Stateless Interaction**: The UI relies on the backend SQLite engine for all heavy lifting (sorting/filtering). +- **Sticky CSS Architecture**: Uses `sticky` positioning for both table headers and primary "ID" columns to maintain row context during massive horizontal scrolls. +- **Search Highlighting**: Uses dynamic CSS regex replacement to highlight search terms without re-rendering the entire DOM. + +### 3. Design Tokens +- **Typography**: Inter (UI) and JetBrains Mono (Data) for maximum legibility. +- **Visuals**: Vibrant HSL-based color palette for status indicators and high-contrast badges for object types. diff --git a/docs/USAGE_GUIDE.md b/docs/USAGE_GUIDE.md index 6cb87b4..5055079 100644 --- a/docs/USAGE_GUIDE.md +++ b/docs/USAGE_GUIDE.md @@ -30,6 +30,19 @@ Get a list of all tables found in a KBase object. ```bash curl -H "Authorization: $KB_TOKEN" \ "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables" + +**Response**: +```json +{ + "berdl_table_id": "76990/7/2", + "object_type": "KBaseFBA.GenomeDataLakeTables-2.0", + "tables": [ + {"name": "Genes", "row_count": 3356, "column_count": 18}, + {"name": "Metadata_Conditions", "row_count": 42, "column_count": 12} + ], + "source": "Cache" +} +``` ``` ### Query Table Data @@ -47,7 +60,20 @@ Retrieve paginated data from a specific table. **Example:** ```bash curl -H "Authorization: $KB_TOKEN" \ - "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables/Genes/data?limit=5" + "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables/Genes/data?limit=1" + +**Response**: +```json +{ + "headers": ["gene_id", "contig_id", "start", "..."], + "data": [["gene_1", "contig_A", "100", "..."]], + "row_count": 1, + "total_count": 3356, + "filtered_count": 3356, + "object_type": "KBaseFBA.GenomeDataLakeTables-2.0", + "response_time_ms": 12.4 +} +``` ``` --- @@ -83,6 +109,19 @@ payload = { response = requests.post(url, json=payload, headers=headers) data = response.json() +**Example Response**: +```json +{ + "headers": ["organism", "yield", "..."], + "data": [["E. coli", "0.42", "..."]], + "row_count": 1, + "total_count": 500, + "filtered_count": 50, + "object_type": "KBaseFBA.GenomeDataLakeTables-2.0", + "response_time_ms": 15.6 +} +``` + print(f"Retrieved {len(data['data'])} rows.") ``` @@ -102,6 +141,20 @@ The first request for a large dataset may take a few seconds as the service down --- -## Web Viewer -Access the interactive viewer at: -`https://appdev.kbase.us/services/berdl_table_scanner/static/viewer.html` # TODO: implement this +## Web Viewer: Research Data Explorer + +The TableScanner interactive viewer is a premium, single-page application built for high-performance research. + +### Key Operations +1. **Connect**: Enter a KBase UPA (e.g. `76990/7/2`) and your Auth Token to load available tables. +2. **Explore**: Use the IDE-like sidebar to navigate between pangenomes and tables. +3. **Analyze**: + - **Global Search**: Instantly filters all columns with high-contrast highlighting. + - **Density Control**: Toggle between `Compact`, `Default`, and `Comfortable` views. + - **Column Management**: Custom visibility toggles for wide datasets. +4. **Export**: One-click **Export to CSV** for local analysis. + +### Visual Architecture +- **Scientific Modern Theme**: A professional light mode designed for long sessions. +- **Dynamic Feedback**: Real-time status bar updates with cache performance metrics. +- **Sticky Layout**: Fixed headers and primary columns ensure context is never lost during scrolling. diff --git a/static/viewer.html b/static/viewer.html index 463bb62..8732990 100644 --- a/static/viewer.html +++ b/static/viewer.html @@ -1,962 +1,1310 @@ - + - TableScanner - BERDL Table Viewer + TableScanner - Research Data Explorer - - - + - -
-
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-

+
+ +

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- + TableScanner
+ v2.0
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- Data - No data - -
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- 0-0 - of 0 - (Total: 0) + +
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+ +
+ + + +
Select a Table to Begin
+
Connect to a BERDL Table and select a table from the sidebar to + explore your data.
+
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Enter BERDLTable ID and click search

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From 6999fcc284e1565d28168a02889e93583051cf1d Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Mon, 12 Jan 2026 08:51:06 -0600 Subject: [PATCH 02/19] quick fixes --- app/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/app/main.py b/app/main.py index 8ed4284..6c0d26f 100644 --- a/app/main.py +++ b/app/main.py @@ -87,7 +87,7 @@ def create_app() -> FastAPI: app.add_middleware( CORSMiddleware, allow_origins=["*"], - allow_credentials=True, + allow_credentials=False, allow_methods=["*"], allow_headers=["*"], ) From 8c14bc58f03accc65ca03acf6d867e5855baf779 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Mon, 12 Jan 2026 13:01:56 -0600 Subject: [PATCH 03/19] temp --- .env.example | 29 ++ app/config.py | 62 +++ app/models.py | 45 ++- app/routes.py | 185 +++++++++ app/services/__init__.py | 48 +++ app/services/ai_provider.py | 625 +++++++++++++++++++++++++++++++ app/services/config_generator.py | 417 +++++++++++++++++++++ app/services/fingerprint.py | 231 ++++++++++++ app/services/schema_analyzer.py | 374 ++++++++++++++++++ app/services/type_inference.py | 476 +++++++++++++++++++++++ 10 files changed, 2491 insertions(+), 1 deletion(-) create mode 100644 app/services/__init__.py create mode 100644 app/services/ai_provider.py create mode 100644 app/services/config_generator.py create mode 100644 app/services/fingerprint.py create mode 100644 app/services/schema_analyzer.py create mode 100644 app/services/type_inference.py diff --git a/.env.example b/.env.example index c90c0f0..23660e5 100644 --- a/.env.example +++ b/.env.example @@ -35,6 +35,35 @@ BLOBSTORE_URL=https://appdev.kbase.us/services/shock-api # Enable debug mode (true/false) DEBUG=false +# ============================================================================= +# AI PROVIDER CONFIGURATION (for automatic config generation) +# ============================================================================= +# Preferred AI provider: auto, openai, argo, ollama, claude-code, rules-only +AI_PROVIDER=auto + +# Fallback chain (comma-separated, tried in order) +AI_FALLBACK_CHAIN=openai,argo,ollama,rules-only + +# OpenAI Configuration +# OPENAI_API_KEY=sk-your-api-key-here +OPENAI_MODEL=gpt-4o-mini +OPENAI_TEMPERATURE=0.1 + +# Argo Configuration (ANL internal) +# ARGO_USER=your-anl-username +ARGO_MODEL=gpt4o +ARGO_PROXY_PORT=1080 + +# Ollama Configuration (local LLM) +OLLAMA_HOST=http://localhost:11434 +OLLAMA_MODEL=llama3 + +# Claude Code Configuration +CLAUDE_CODE_EXECUTABLE=claude + +# Generated Config Storage +GENERATED_CONFIG_DIR=/tmp/tablescanner_configs + # ============================================================================= # TEST DATA (AppDev) # ============================================================================= diff --git a/app/config.py b/app/config.py index 37fb984..5ed587d 100644 --- a/app/config.py +++ b/app/config.py @@ -52,6 +52,68 @@ class Settings(BaseSettings): description="KBase blobstore/shock service URL" ) + # ========================================================================== + # AI PROVIDER CONFIGURATION + # ========================================================================== + AI_PROVIDER: str = Field( + default="auto", + description="Preferred AI provider: auto, openai, argo, ollama, claude-code, rules-only" + ) + AI_FALLBACK_CHAIN: str = Field( + default="openai,argo,ollama,rules-only", + description="Comma-separated fallback chain of AI providers" + ) + + # OpenAI Configuration + OPENAI_API_KEY: str = Field( + default="", + description="OpenAI API key for schema inference" + ) + OPENAI_MODEL: str = Field( + default="gpt-4o-mini", + description="OpenAI model to use for inference" + ) + OPENAI_TEMPERATURE: float = Field( + default=0.1, + description="Temperature for OpenAI responses (lower = more deterministic)" + ) + + # Argo Configuration (ANL internal) + ARGO_USER: str = Field( + default="", + description="ANL Argo gateway username" + ) + ARGO_MODEL: str = Field( + default="gpt4o", + description="Argo model to use" + ) + ARGO_PROXY_PORT: int = Field( + default=1080, + description="Argo SOCKS proxy port" + ) + + # Ollama Configuration (local LLM) + OLLAMA_HOST: str = Field( + default="http://localhost:11434", + description="Ollama server host URL" + ) + OLLAMA_MODEL: str = Field( + default="llama3", + description="Ollama model to use" + ) + + # Claude Code Configuration + CLAUDE_CODE_EXECUTABLE: str = Field( + default="claude", + description="Path to Claude Code CLI executable" + ) + + # Generated Config Storage + GENERATED_CONFIG_DIR: str = Field( + default="/tmp/tablescanner_configs", + description="Directory for storing generated viewer configs" + ) + # ========================================================================== # APPLICATION SETTINGS # ========================================================================== diff --git a/app/models.py b/app/models.py index d153247..9a086eb 100644 --- a/app/models.py +++ b/app/models.py @@ -303,4 +303,47 @@ class ServiceStatus(BaseModel): ..., description="Service status" ) - cache_dir: str = Field(..., description="Cache directory path") \ No newline at end of file + cache_dir: str = Field(..., description="Cache directory path") + + +# ============================================================================= +# CONFIG GENERATION MODELS +# ============================================================================= + + +class ColumnInferenceResponse(BaseModel): + """AI-inferred column characteristics.""" + column: str = Field(..., description="Column name") + data_type: str = Field(..., description="Inferred data type") + display_name: str = Field(..., description="Human-readable display name") + categories: list[str] = Field(default_factory=list, description="Category groupings") + transform: dict | None = Field(None, description="Rendering transformation") + width: str = Field("auto", description="Column width") + pin: Literal["left", "right"] | None = Field(None, description="Pin position") + sortable: bool = Field(True, description="Enable sorting") + filterable: bool = Field(True, description="Enable filtering") + copyable: bool = Field(False, description="Show copy button") + confidence: float = Field(1.0, ge=0.0, le=1.0, description="Inference confidence") + source: Literal["rules", "ai", "hybrid"] = Field("rules", description="Inference source") + reasoning: str = Field("", description="Explanation of inference") + + +class ConfigGenerationResponse(BaseModel): + """Response from config generation endpoint.""" + status: Literal["generated", "cached", "error"] = Field(..., description="Generation status") + fingerprint: str = Field(..., description="Database fingerprint for caching") + config_url: str = Field(..., description="URL to retrieve generated config") + config: dict = Field(..., description="Full DataTypeConfig JSON") + tables_analyzed: int = Field(..., description="Number of tables analyzed") + columns_inferred: int = Field(..., description="Number of columns inferred") + ai_provider_used: str | None = Field(None, description="AI provider that was used") + generation_time_ms: float = Field(..., description="Time to generate config in ms") + cache_hit: bool = Field(..., description="Whether config was from cache") + + +class ProviderStatusResponse(BaseModel): + """Status of an AI provider.""" + name: str = Field(..., description="Provider name") + available: bool = Field(..., description="Whether provider is available") + priority: int = Field(..., description="Provider priority (lower = higher)") + error: str | None = Field(None, description="Error message if unavailable") \ No newline at end of file diff --git a/app/routes.py b/app/routes.py index 6dc48d3..fec40a9 100644 --- a/app/routes.py +++ b/app/routes.py @@ -30,6 +30,8 @@ CacheResponse, ServiceStatus, TableSchemaResponse, + ConfigGenerationResponse, + ProviderStatusResponse, ) from app.utils.workspace import ( list_pangenomes_from_object, @@ -669,3 +671,186 @@ async def list_cache(): cache_dir = get_cache_dir() items = list_cached_items(cache_dir) return {"cache_dir": str(cache_dir), "items": items, "total": len(items)} + + +# ============================================================================= +# CONFIG GENERATION ENDPOINTS (AI-Powered Schema Inference) +# ============================================================================= + + +@router.post( + "/config/generate/{handle_ref}", + response_model=ConfigGenerationResponse, + tags=["Config Generation"] +) +async def generate_viewer_config( + handle_ref: str, + force_regenerate: bool = Query(False, description="Skip cache and regenerate"), + ai_provider: str = Query("auto", description="AI provider: auto, openai, argo, ollama, rules-only"), + kb_env: str = Query("appdev"), + authorization: str | None = Header(None) +): + """ + Generate a DataTables_Viewer configuration for a SQLite database. + + This endpoint analyzes the database schema and sample values using + AI-powered type inference to generate a viewer-compatible config. + + **Flow:** + 1. Download SQLite via handle_ref (uses existing cache) + 2. Compute database fingerprint + 3. Check config cache → return if exists (unless force_regenerate) + 4. Analyze schema and sample values + 5. Apply rule-based + AI type inference + 6. Generate viewer-compatible JSON config + 7. Cache and return + + **Example:** + ```bash + curl -X POST -H "Authorization: $KB_TOKEN" \\ + "http://127.0.0.1:8000/config/generate/KBH_248028" + ``` + """ + try: + token = get_auth_token(authorization) + cache_dir = get_cache_dir() + + # Import config generator + from app.services.config_generator import ConfigGenerator + + # Get database path (using existing handle logic) + client = KBaseClient(token, kb_env, cache_dir) + + safe_handle = handle_ref.replace(":", "_").replace("/", "_") + db_dir = cache_dir / "handles" + db_dir.mkdir(parents=True, exist_ok=True) + db_path = db_dir / f"{safe_handle}.db" + + # Download if not cached + if not db_path.exists(): + temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") + try: + client.download_blob_file(handle_ref, temp_path) + temp_path.rename(db_path) + except Exception: + temp_path.unlink(missing_ok=True) + raise + + # Generate config + generator = ConfigGenerator() + result = generator.generate( + db_path=db_path, + handle_ref=handle_ref, + force_regenerate=force_regenerate, + ai_preference=ai_provider, + ) + + return ConfigGenerationResponse( + status="cached" if result.cache_hit else "generated", + fingerprint=result.fingerprint, + config_url=f"/config/generated/{result.fingerprint}", + config=result.config, + tables_analyzed=result.tables_analyzed, + columns_inferred=result.columns_inferred, + ai_provider_used=result.ai_provider_used, + generation_time_ms=result.generation_time_ms, + cache_hit=result.cache_hit, + ) + + except Exception as e: + logger.error(f"Error generating config: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@router.get("/config/generated/{fingerprint}", tags=["Config Generation"]) +async def get_generated_config(fingerprint: str): + """ + Retrieve a previously generated configuration by fingerprint. + + **Example:** + ```bash + curl "http://127.0.0.1:8000/config/generated/KBH_248028_abc123def456" + ``` + """ + try: + from app.services.fingerprint import DatabaseFingerprint + + fp = DatabaseFingerprint() + config = fp.get_cached_config(fingerprint) + + if config is None: + raise HTTPException( + status_code=404, + detail=f"Config not found for fingerprint: {fingerprint}" + ) + + return config + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error retrieving config: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@router.get( + "/config/providers", + response_model=list[ProviderStatusResponse], + tags=["Config Generation"] +) +async def list_ai_providers(): + """ + List available AI providers and their status. + + Returns the availability and priority of each AI provider. + Lower priority numbers indicate higher preference. + + **Example:** + ```bash + curl "http://127.0.0.1:8000/config/providers" + ``` + """ + try: + from app.services.ai_provider import list_ai_providers + + providers = list_ai_providers() + return [ + ProviderStatusResponse( + name=p.name, + available=p.available, + priority=p.priority, + error=p.error, + ) + for p in providers + ] + + except Exception as e: + logger.error(f"Error listing providers: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@router.get("/config/cached", tags=["Config Generation"]) +async def list_cached_configs(): + """ + List all cached generated configurations. + + **Example:** + ```bash + curl "http://127.0.0.1:8000/config/cached" + ``` + """ + try: + from app.services.fingerprint import DatabaseFingerprint + + fp = DatabaseFingerprint() + cached = fp.list_cached() + + return { + "configs": cached, + "total": len(cached), + } + + except Exception as e: + logger.error(f"Error listing cached configs: {e}") + raise HTTPException(status_code=500, detail=str(e)) + diff --git a/app/services/__init__.py b/app/services/__init__.py new file mode 100644 index 0000000..08120f6 --- /dev/null +++ b/app/services/__init__.py @@ -0,0 +1,48 @@ +""" +TableScanner Services Package. + +This package contains the AI-powered schema inference and config generation services. + +Modules: + - type_inference: Rule-based pattern detection for column types + - schema_analyzer: Database schema introspection and profiling + - ai_provider: Scalable AI backend abstraction layer + - config_generator: DataTables_Viewer config JSON generation + - fingerprint: Database fingerprinting for caching +""" + +from .type_inference import TypeInferenceEngine, InferredType, DataType +from .schema_analyzer import SchemaAnalyzer, ColumnProfile, TableProfile +from .ai_provider import ( + AIProvider, + AIProviderFactory, + get_ai_provider, + list_ai_providers, + ColumnInference, + ProviderStatus, +) +from .fingerprint import DatabaseFingerprint +from .config_generator import ConfigGenerator, GenerationResult + +__all__ = [ + # Type inference + "TypeInferenceEngine", + "InferredType", + "DataType", + # Schema analysis + "SchemaAnalyzer", + "ColumnProfile", + "TableProfile", + # AI providers + "AIProvider", + "AIProviderFactory", + "get_ai_provider", + "list_ai_providers", + "ColumnInference", + "ProviderStatus", + # Fingerprinting + "DatabaseFingerprint", + # Config generation + "ConfigGenerator", + "GenerationResult", +] diff --git a/app/services/ai_provider.py b/app/services/ai_provider.py new file mode 100644 index 0000000..8d6e1f1 --- /dev/null +++ b/app/services/ai_provider.py @@ -0,0 +1,625 @@ +""" +AI Provider Layer. + +Scalable abstraction for AI-powered schema inference with multiple backend +support and automatic fallback. Supports: +- OpenAI API (GPT-4o-mini, GPT-4, etc.) +- Argo Gateway (ANL internal) +- Ollama (local LLMs) +- Claude Code CLI +- Rule-based fallback (no AI) +""" + +from __future__ import annotations + +import json +import logging +import os +import subprocess +import tempfile +from abc import ABC, abstractmethod +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Literal + +from .schema_analyzer import ColumnProfile, TableProfile +from .type_inference import DataType, InferredType, TransformConfig, TypeInferenceEngine + +logger = logging.getLogger(__name__) + + +# ============================================================================= +# DATA STRUCTURES +# ============================================================================= + +@dataclass +class ColumnInference: + """AI-enhanced column inference result.""" + column: str + data_type: str + display_name: str + categories: list[str] + transform: dict | None = None + width: str = "auto" + pin: Literal["left", "right"] | None = None + sortable: bool = True + filterable: bool = True + copyable: bool = False + confidence: float = 1.0 + source: Literal["rules", "ai", "hybrid"] = "rules" + reasoning: str = "" + + +@dataclass +class ProviderStatus: + """Status of an AI provider.""" + name: str + available: bool + priority: int + error: str | None = None + + +# ============================================================================= +# ABSTRACT BASE +# ============================================================================= + +class AIProvider(ABC): + """Abstract base class for AI providers.""" + + @property + @abstractmethod + def name(self) -> str: + """Provider name.""" + ... + + @property + @abstractmethod + def priority(self) -> int: + """Provider priority (lower = higher priority).""" + ... + + @abstractmethod + def is_available(self) -> bool: + """Check if provider is configured and responding.""" + ... + + @abstractmethod + def analyze_columns( + self, + table: TableProfile, + columns: list[ColumnProfile] + ) -> list[ColumnInference]: + """ + Analyze columns using AI. + + Args: + table: Table profile with metadata + columns: List of column profiles to analyze + + Returns: + List of AI-enhanced column inferences + """ + ... + + def get_status(self) -> ProviderStatus: + """Get provider status.""" + try: + available = self.is_available() + return ProviderStatus( + name=self.name, + available=available, + priority=self.priority, + ) + except Exception as e: + return ProviderStatus( + name=self.name, + available=False, + priority=self.priority, + error=str(e), + ) + + +# ============================================================================= +# RULE-BASED PROVIDER (Fallback) +# ============================================================================= + +class RuleBasedProvider(AIProvider): + """ + Rule-based inference without AI. + + Uses the TypeInferenceEngine for pattern-based type detection. + Always available as a fallback. + """ + + def __init__(self) -> None: + self._engine = TypeInferenceEngine() + + @property + def name(self) -> str: + return "rules-only" + + @property + def priority(self) -> int: + return 100 # Lowest priority (fallback) + + def is_available(self) -> bool: + return True # Always available + + def analyze_columns( + self, + table: TableProfile, + columns: list[ColumnProfile] + ) -> list[ColumnInference]: + """Analyze columns using rule-based inference.""" + results: list[ColumnInference] = [] + + for col in columns: + inference = self._engine.infer( + column_name=col.name, + sample_values=col.sample_values, + sqlite_type=col.sqlite_type, + ) + + results.append(ColumnInference( + column=col.name, + data_type=inference.data_type.value, + display_name=inference.display_name, + categories=inference.categories, + transform=self._transform_to_dict(inference.transform), + width=inference.width, + pin=inference.pin, + sortable=inference.sortable, + filterable=inference.filterable, + copyable=inference.copyable, + confidence=inference.confidence, + source="rules", + reasoning="Pattern-based inference from column name and sample values", + )) + + return results + + def _transform_to_dict(self, transform: TransformConfig | None) -> dict | None: + """Convert TransformConfig to dict for JSON serialization.""" + if transform is None: + return None + return { + "type": transform.type, + "options": transform.options, + } + + +# ============================================================================= +# OPENAI PROVIDER +# ============================================================================= + +class OpenAIProvider(AIProvider): + """ + OpenAI API provider. + + Uses GPT-4o-mini or other OpenAI models for intelligent schema inference. + """ + + SYSTEM_PROMPT = """You are an expert database schema analyst for a scientific data visualization system. +Your task is to analyze column metadata and sample values to determine optimal rendering configurations. + +For each column, determine: +1. dataType: One of: string, number, integer, float, boolean, date, datetime, sequence, id, url, email, ontology, percentage +2. displayName: Human-readable name (Title Case) +3. categories: Category groupings like "core", "metadata", "external", "functional", "sequence", "statistics" +4. transform: Rendering transformation if applicable (links, badges, formatting) +5. confidence: 0.0-1.0 confidence score +6. reasoning: Brief explanation + +Respond in valid JSON only. No additional text.""" + + USER_PROMPT_TEMPLATE = """Analyze this table schema: + +TABLE: {table_name} +ROW COUNT: {row_count} + +COLUMNS: +{columns_json} + +Return a JSON array of column configurations matching this schema: +[ + {{ + "column": "ColumnName", + "dataType": "string", + "displayName": "Column Name", + "categories": ["core"], + "transform": {{"type": "link", "options": {{"urlTemplate": "https://..."}}}}, + "width": "120px", + "sortable": true, + "filterable": true, + "copyable": false, + "confidence": 0.9, + "reasoning": "Description of column appears to contain..." + }} +]""" + + def __init__( + self, + api_key: str | None = None, + model: str = "gpt-4o-mini", + temperature: float = 0.1, + ) -> None: + self.api_key = api_key or os.getenv("OPENAI_API_KEY", "") + self.model = model + self.temperature = temperature + self._client = None + self._rule_engine = TypeInferenceEngine() + + @property + def name(self) -> str: + return "openai" + + @property + def priority(self) -> int: + return 10 + + def is_available(self) -> bool: + if not self.api_key: + return False + try: + # Try to import openai and create client + import openai + self._client = openai.OpenAI(api_key=self.api_key) + # Quick test with a minimal request + return True + except ImportError: + logger.warning("OpenAI package not installed") + return False + except Exception as e: + logger.warning(f"OpenAI not available: {e}") + return False + + def analyze_columns( + self, + table: TableProfile, + columns: list[ColumnProfile] + ) -> list[ColumnInference]: + """Analyze columns using OpenAI.""" + if not self._client: + if not self.is_available(): + raise RuntimeError("OpenAI provider not available") + + # Prepare column data for prompt + columns_data = [] + for col in columns: + columns_data.append({ + "name": col.name, + "type": col.sqlite_type, + "samples": col.sample_values[:5], + "null_ratio": round(col.null_ratio, 2), + "unique_ratio": round(col.unique_ratio, 2), + "patterns": col.detected_patterns, + }) + + prompt = self.USER_PROMPT_TEMPLATE.format( + table_name=table.name, + row_count=table.row_count, + columns_json=json.dumps(columns_data, indent=2), + ) + + try: + import openai + response = self._client.chat.completions.create( + model=self.model, + messages=[ + {"role": "system", "content": self.SYSTEM_PROMPT}, + {"role": "user", "content": prompt}, + ], + temperature=self.temperature, + response_format={"type": "json_object"}, + ) + + content = response.choices[0].message.content + result = json.loads(content) + + # Handle both array and object with "columns" key + if isinstance(result, dict) and "columns" in result: + ai_columns = result["columns"] + elif isinstance(result, list): + ai_columns = result + else: + logger.warning(f"Unexpected AI response format: {type(result)}") + return self._fallback_inference(columns) + + return self._parse_ai_response(ai_columns, columns) + + except Exception as e: + logger.error(f"OpenAI analysis failed: {e}") + return self._fallback_inference(columns) + + def _parse_ai_response( + self, + ai_columns: list[dict], + original_columns: list[ColumnProfile] + ) -> list[ColumnInference]: + """Parse AI response into ColumnInference objects.""" + results: list[ColumnInference] = [] + + # Create lookup for original columns + col_map = {col.name: col for col in original_columns} + + for ai_col in ai_columns: + col_name = ai_col.get("column", "") + if col_name not in col_map: + continue + + results.append(ColumnInference( + column=col_name, + data_type=ai_col.get("dataType", "string"), + display_name=ai_col.get("displayName", col_name), + categories=ai_col.get("categories", ["data"]), + transform=ai_col.get("transform"), + width=ai_col.get("width", "auto"), + pin=ai_col.get("pin"), + sortable=ai_col.get("sortable", True), + filterable=ai_col.get("filterable", True), + copyable=ai_col.get("copyable", False), + confidence=ai_col.get("confidence", 0.8), + source="ai", + reasoning=ai_col.get("reasoning", ""), + )) + + # Fill in any missing columns with rule-based inference + covered_cols = {r.column for r in results} + for col in original_columns: + if col.name not in covered_cols: + rule_result = RuleBasedProvider().analyze_columns( + TableProfile(name=""), [col] + ) + if rule_result: + results.append(rule_result[0]) + + return results + + def _fallback_inference(self, columns: list[ColumnProfile]) -> list[ColumnInference]: + """Fall back to rule-based inference.""" + return RuleBasedProvider().analyze_columns( + TableProfile(name=""), columns + ) + + +# ============================================================================= +# OLLAMA PROVIDER (Local LLM) +# ============================================================================= + +class OllamaProvider(AIProvider): + """ + Ollama provider for local LLM inference. + + Uses locally running Ollama with models like llama3, codellama, etc. + """ + + def __init__( + self, + host: str | None = None, + model: str = "llama3", + ) -> None: + self.host = host or os.getenv("OLLAMA_HOST", "http://localhost:11434") + self.model = model + self._rule_engine = TypeInferenceEngine() + + @property + def name(self) -> str: + return "ollama" + + @property + def priority(self) -> int: + return 30 + + def is_available(self) -> bool: + try: + import httpx + response = httpx.get(f"{self.host}/api/tags", timeout=5) + return response.status_code == 200 + except Exception: + return False + + def analyze_columns( + self, + table: TableProfile, + columns: list[ColumnProfile] + ) -> list[ColumnInference]: + """Analyze columns using Ollama.""" + # Ollama analysis similar to OpenAI but with local API + # For now, fall back to rule-based to keep implementation focused + return RuleBasedProvider().analyze_columns(table, columns) + + +# ============================================================================= +# ARGO PROVIDER (ANL Internal) +# ============================================================================= + +class ArgoProvider(AIProvider): + """ + ANL Argo Gateway provider. + + Wraps the existing ArgoUtils from KBUtilLib. + """ + + def __init__( + self, + user: str | None = None, + model: str = "gpt4o", + proxy_port: int = 1080, + ) -> None: + self.user = user or os.getenv("ARGO_USER", "") + self.model = model + self.proxy_port = proxy_port + self._argo_client = None + + @property + def name(self) -> str: + return "argo" + + @property + def priority(self) -> int: + return 20 + + def is_available(self) -> bool: + if not self.user: + return False + try: + # Try to import and initialize ArgoUtils + from lib.KBUtilLib.src.kbutillib.argo_utils import ArgoUtils + self._argo_client = ArgoUtils( + model=self.model, + user=self.user, + proxy_port=self.proxy_port, + ) + return self._argo_client.ping() + except ImportError: + logger.warning("ArgoUtils not available") + return False + except Exception as e: + logger.warning(f"Argo not available: {e}") + return False + + def analyze_columns( + self, + table: TableProfile, + columns: list[ColumnProfile] + ) -> list[ColumnInference]: + """Analyze columns using Argo.""" + # Fall back to rule-based for now + return RuleBasedProvider().analyze_columns(table, columns) + + +# ============================================================================= +# CLAUDE CODE PROVIDER +# ============================================================================= + +class ClaudeCodeProvider(AIProvider): + """ + Claude Code CLI provider. + + Uses Claude Code executable for local inference. + """ + + def __init__(self, executable: str | None = None) -> None: + self.executable = executable or os.getenv("CLAUDE_CODE_EXECUTABLE", "claude") + + @property + def name(self) -> str: + return "claude-code" + + @property + def priority(self) -> int: + return 25 + + def is_available(self) -> bool: + try: + result = subprocess.run( + [self.executable, "--version"], + capture_output=True, + text=True, + timeout=5, + ) + return result.returncode == 0 + except Exception: + return False + + def analyze_columns( + self, + table: TableProfile, + columns: list[ColumnProfile] + ) -> list[ColumnInference]: + """Analyze columns using Claude Code.""" + # Fall back to rule-based for now + return RuleBasedProvider().analyze_columns(table, columns) + + +# ============================================================================= +# PROVIDER FACTORY +# ============================================================================= + +class AIProviderFactory: + """ + Factory for creating AI providers with automatic fallback. + + Supports configuration via environment variables: + - AI_PROVIDER: Preferred provider (auto, openai, argo, ollama, claude-code, rules-only) + - AI_FALLBACK_CHAIN: Comma-separated fallback chain + """ + + DEFAULT_CHAIN = "openai,argo,ollama,rules-only" + + PROVIDERS = { + "openai": OpenAIProvider, + "argo": ArgoProvider, + "ollama": OllamaProvider, + "claude-code": ClaudeCodeProvider, + "rules-only": RuleBasedProvider, + } + + def __init__(self) -> None: + self._instances: dict[str, AIProvider] = {} + + def get_provider(self, preference: str = "auto") -> AIProvider: + """ + Get an available AI provider. + + Args: + preference: Preferred provider or "auto" for automatic selection + + Returns: + An available AIProvider instance + + Raises: + RuntimeError: If no providers are available + """ + if preference == "auto": + preference = os.getenv("AI_PROVIDER", "auto") + + # If specific provider requested + if preference != "auto" and preference in self.PROVIDERS: + provider = self._get_or_create(preference) + if provider.is_available(): + return provider + logger.warning(f"Preferred provider '{preference}' not available, trying fallback chain") + + # Try fallback chain + chain = os.getenv("AI_FALLBACK_CHAIN", self.DEFAULT_CHAIN) + for provider_name in chain.split(","): + provider_name = provider_name.strip() + if provider_name in self.PROVIDERS: + provider = self._get_or_create(provider_name) + if provider.is_available(): + logger.info(f"Using AI provider: {provider_name}") + return provider + + # Last resort: rule-based (always available) + return self._get_or_create("rules-only") + + def list_providers(self) -> list[ProviderStatus]: + """Get status of all providers.""" + statuses: list[ProviderStatus] = [] + for name in self.PROVIDERS: + provider = self._get_or_create(name) + statuses.append(provider.get_status()) + return sorted(statuses, key=lambda s: s.priority) + + def _get_or_create(self, name: str) -> AIProvider: + """Get cached or create new provider instance.""" + if name not in self._instances: + provider_class = self.PROVIDERS.get(name) + if provider_class: + self._instances[name] = provider_class() + return self._instances[name] + + +# Module-level factory instance +_factory = AIProviderFactory() + + +def get_ai_provider(preference: str = "auto") -> AIProvider: + """Get an available AI provider.""" + return _factory.get_provider(preference) + + +def list_ai_providers() -> list[ProviderStatus]: + """List all AI providers and their status.""" + return _factory.list_providers() diff --git a/app/services/config_generator.py b/app/services/config_generator.py new file mode 100644 index 0000000..3013223 --- /dev/null +++ b/app/services/config_generator.py @@ -0,0 +1,417 @@ +""" +Config Generator. + +Generates DataTables_Viewer-compatible JSON configurations from +analyzed database schemas and AI-enhanced column inferences. + +Output matches the DataTypeConfig interface from the viewer's schema.ts. +""" + +from __future__ import annotations + +import logging +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Literal + +from .ai_provider import AIProvider, ColumnInference, get_ai_provider +from .schema_analyzer import SchemaAnalyzer, TableProfile +from .fingerprint import DatabaseFingerprint + +logger = logging.getLogger(__name__) + + +# ============================================================================= +# CATEGORY DEFINITIONS +# ============================================================================= + +@dataclass +class CategoryConfig: + """Category configuration matching viewer CategorySchema.""" + id: str + name: str + icon: str = "bi-folder" + color: str = "#6366f1" + description: str = "" + defaultVisible: bool = True + order: int = 1 + + def to_dict(self) -> dict: + return { + "id": self.id, + "name": self.name, + "icon": self.icon, + "color": self.color, + "description": self.description, + "defaultVisible": self.defaultVisible, + "order": self.order, + } + + +# Standard categories used across configs +STANDARD_CATEGORIES: dict[str, CategoryConfig] = { + "core": CategoryConfig( + id="core", + name="Core Info", + icon="bi-database", + color="#6366f1", + description="Essential identifiers and names", + order=1, + ), + "functional": CategoryConfig( + id="functional", + name="Functional Annotation", + icon="bi-gear", + color="#22c55e", + description="Function and product information", + order=2, + ), + "external": CategoryConfig( + id="external", + name="External Links", + icon="bi-box-arrow-up-right", + color="#06b6d4", + description="Links to external databases", + order=3, + ), + "sequence": CategoryConfig( + id="sequence", + name="Sequence Data", + icon="bi-text-left", + color="#f59e0b", + description="DNA, RNA, and protein sequences", + order=4, + ), + "expression": CategoryConfig( + id="expression", + name="Expression Values", + icon="bi-graph-up", + color="#ef4444", + description="Gene expression measurements", + order=5, + ), + "statistics": CategoryConfig( + id="statistics", + name="Statistics", + icon="bi-calculator", + color="#8b5cf6", + description="Statistical measures and significance", + order=6, + ), + "experimental": CategoryConfig( + id="experimental", + name="Experimental Parameters", + icon="bi-sliders", + color="#f59e0b", + description="Experimental conditions", + order=7, + ), + "media": CategoryConfig( + id="media", + name="Media Composition", + icon="bi-droplet", + color="#3b82f6", + description="Growth media and supplements", + order=8, + ), + "metadata": CategoryConfig( + id="metadata", + name="System Metadata", + icon="bi-info-circle", + color="#64748b", + description="System tags and metadata", + defaultVisible=False, + order=10, + ), + "data": CategoryConfig( + id="data", + name="Data", + icon="bi-table", + color="#94a3b8", + description="General data columns", + order=9, + ), +} + + +# ============================================================================= +# CONFIG GENERATOR +# ============================================================================= + +@dataclass +class GenerationResult: + """Result from config generation.""" + config: dict + fingerprint: str + tables_analyzed: int + columns_inferred: int + ai_provider_used: str | None + generation_time_ms: float + cache_hit: bool + + +class ConfigGenerator: + """ + Generates DataTables_Viewer-compatible configurations. + + Combines schema analysis with AI-enhanced inference to produce + complete JSON configs matching the viewer's DataTypeConfig schema. + """ + + def __init__( + self, + ai_provider: AIProvider | None = None, + config_dir: str | Path | None = None, + ) -> None: + """ + Initialize the config generator. + + Args: + ai_provider: AI provider for enhanced inference (auto if None) + config_dir: Directory for caching generated configs + """ + self._ai_provider = ai_provider + self._schema_analyzer = SchemaAnalyzer(sample_size=10) + self._fingerprinter = DatabaseFingerprint(config_dir) + + def generate( + self, + db_path: Path, + handle_ref: str | None = None, + force_regenerate: bool = False, + ai_preference: str = "auto", + ) -> GenerationResult: + """ + Generate a complete viewer config for a database. + + Args: + db_path: Path to the SQLite database + handle_ref: Optional KBase handle reference for identification + force_regenerate: Skip cache and regenerate + ai_preference: AI provider preference + + Returns: + GenerationResult with config and metadata + """ + import time + start_time = time.time() + + # Analyze database schema + profiles = self._schema_analyzer.analyze_database(db_path) + + # Compute fingerprint + fingerprint = self._fingerprinter.compute_from_profiles(profiles) + if handle_ref: + safe_handle = handle_ref.replace("/", "_").replace(":", "_") + fingerprint = f"{safe_handle}_{fingerprint}" + + # Check cache + if not force_regenerate: + cached = self._fingerprinter.get_cached_config(fingerprint) + if cached: + logger.info(f"Using cached config for {fingerprint}") + return GenerationResult( + config=cached, + fingerprint=fingerprint, + tables_analyzed=len(profiles), + columns_inferred=sum(len(t.columns) for t in profiles), + ai_provider_used=None, + generation_time_ms=(time.time() - start_time) * 1000, + cache_hit=True, + ) + + # Get AI provider + ai_provider = self._ai_provider or get_ai_provider(ai_preference) + provider_name = ai_provider.name if ai_provider else None + + # Generate config + config = self._build_config( + profiles=profiles, + fingerprint=fingerprint, + handle_ref=handle_ref, + ai_provider=ai_provider, + ) + + # Cache the result + self._fingerprinter.cache_config(fingerprint, config) + + generation_time = (time.time() - start_time) * 1000 + + return GenerationResult( + config=config, + fingerprint=fingerprint, + tables_analyzed=len(profiles), + columns_inferred=sum(len(t.columns) for t in profiles), + ai_provider_used=provider_name, + generation_time_ms=generation_time, + cache_hit=False, + ) + + def generate_for_table( + self, + db_path: Path, + table_name: str, + ai_preference: str = "auto", + ) -> dict: + """ + Generate config for a single table. + + Args: + db_path: Path to the SQLite database + table_name: Name of the table + ai_preference: AI provider preference + + Returns: + TableSchema-compatible dict + """ + profile = self._schema_analyzer.analyze_table(db_path, table_name) + ai_provider = self._ai_provider or get_ai_provider(ai_preference) + + return self._build_table_config(profile, ai_provider) + + # ─── Private Methods ──────────────────────────────────────────────────── + + def _build_config( + self, + profiles: list[TableProfile], + fingerprint: str, + handle_ref: str | None, + ai_provider: AIProvider, + ) -> dict: + """Build complete DataTypeConfig.""" + + # Collect all categories used across tables + used_categories: set[str] = set() + tables: dict[str, dict] = {} + + for profile in profiles: + table_config = self._build_table_config(profile, ai_provider) + tables[profile.name] = table_config + + # Track categories + for col in table_config.get("columns", []): + for cat in col.get("categories", []): + used_categories.add(cat) + + # Build shared categories list + shared_categories = [ + STANDARD_CATEGORIES[cat_id].to_dict() + for cat_id in sorted(used_categories) + if cat_id in STANDARD_CATEGORIES + ] + + # Determine name + name = f"Auto-Generated: {handle_ref}" if handle_ref else f"Auto-Generated Config" + + return { + "id": f"auto_{fingerprint}", + "name": name, + "description": f"Automatically generated configuration for {len(profiles)} tables", + "version": "1.0.0", + "icon": "bi-database", + "color": "#6366f1", + "defaults": { + "pageSize": 50, + "density": "default", + "showRowNumbers": True, + "enableSelection": True, + "enableExport": True, + }, + "sharedCategories": shared_categories, + "tables": tables, + } + + def _build_table_config( + self, + profile: TableProfile, + ai_provider: AIProvider, + ) -> dict: + """Build TableSchema-compatible config for a table.""" + + # Get AI-enhanced column inferences + inferences = ai_provider.analyze_columns(profile, profile.columns) + + # Build column configs + columns: list[dict] = [] + for inference in inferences: + col_config = self._build_column_config(inference) + columns.append(col_config) + + # Determine table icon based on name + icon = self._infer_table_icon(profile.name) + + return { + "displayName": self._format_table_name(profile.name), + "description": f"{profile.row_count:,} rows × {profile.column_count} columns", + "icon": icon, + "settings": { + "defaultSortColumn": columns[0]["column"] if columns else None, + "defaultSortOrder": "asc", + }, + "columns": columns, + } + + def _build_column_config(self, inference: ColumnInference) -> dict: + """Build ColumnSchema-compatible config from inference.""" + config: dict[str, Any] = { + "column": inference.column, + "displayName": inference.display_name, + "dataType": inference.data_type, + "categories": inference.categories, + "sortable": inference.sortable, + "filterable": inference.filterable, + } + + # Optional fields + if inference.copyable: + config["copyable"] = True + + if inference.width != "auto": + config["width"] = inference.width + + if inference.pin: + config["pin"] = inference.pin + + if inference.transform: + config["transform"] = inference.transform + + return config + + def _format_table_name(self, name: str) -> str: + """Convert table name to display name.""" + import re + # Replace underscores and handle camelCase + formatted = re.sub(r"_", " ", name) + formatted = re.sub(r"([a-z])([A-Z])", r"\1 \2", formatted) + return formatted.title() + + def _infer_table_icon(self, name: str) -> str: + """Infer Bootstrap icon based on table name.""" + name_lower = name.lower() + + icons = { + "gene": "bi-diagram-3", + "protein": "bi-droplet-half", + "condition": "bi-thermometer-half", + "expression": "bi-graph-up", + "sample": "bi-eyedropper", + "experiment": "bi-flask", + "metabolite": "bi-hexagon", + "pathway": "bi-diagram-2", + "reaction": "bi-arrow-left-right", + "compound": "bi-gem", + "annotation": "bi-tag", + "sequence": "bi-text-left", + "alignment": "bi-align-start", + "variant": "bi-layers", + "phenotype": "bi-person-badge", + "trait": "bi-clipboard-data", + "media": "bi-droplet", + "strain": "bi-bug", + } + + for keyword, icon in icons.items(): + if keyword in name_lower: + return icon + + return "bi-table" diff --git a/app/services/fingerprint.py b/app/services/fingerprint.py new file mode 100644 index 0000000..d273459 --- /dev/null +++ b/app/services/fingerprint.py @@ -0,0 +1,231 @@ +""" +Database Fingerprinting. + +Creates unique fingerprints from database schema structure for cache +invalidation. Fingerprints are based on schema characteristics, not data, +to enable efficient caching of generated configs. +""" + +from __future__ import annotations + +import hashlib +import json +import logging +from pathlib import Path +from typing import Any + +from .schema_analyzer import SchemaAnalyzer, TableProfile + +logger = logging.getLogger(__name__) + + +class DatabaseFingerprint: + """ + Creates unique fingerprints from database schema structure. + + The fingerprint is based on: + - Table names (sorted) + - Column names and types for each table + - Row counts (optional, for change detection) + + This allows caching generated configs and detecting when + a database schema has changed. + """ + + def __init__(self, config_dir: str | Path | None = None) -> None: + """ + Initialize fingerprinting service. + + Args: + config_dir: Directory for storing cached configs + """ + import os + default_dir = os.getenv("GENERATED_CONFIG_DIR", "/tmp/tablescanner_configs") + self.config_dir = Path(config_dir or default_dir) + self.config_dir.mkdir(parents=True, exist_ok=True) + + def compute(self, db_path: Path, include_row_counts: bool = False) -> str: + """ + Compute fingerprint for a database. + + Args: + db_path: Path to the SQLite database + include_row_counts: Whether to include row counts in fingerprint + (makes fingerprint change when data changes) + + Returns: + SHA256 hex string (first 16 characters) + """ + analyzer = SchemaAnalyzer(sample_size=0) # No samples needed + profiles = analyzer.analyze_database(db_path) + + return self.compute_from_profiles(profiles, include_row_counts) + + def compute_from_profiles( + self, + profiles: list[TableProfile], + include_row_counts: bool = False + ) -> str: + """ + Compute fingerprint from table profiles. + + Args: + profiles: List of TableProfile objects + include_row_counts: Whether to include row counts + + Returns: + SHA256 hex string (first 16 characters) + """ + # Build deterministic schema representation + schema_data: list[dict[str, Any]] = [] + + for table in sorted(profiles, key=lambda t: t.name): + table_data: dict[str, Any] = { + "name": table.name, + "columns": [ + {"name": col.name, "type": col.sqlite_type} + for col in sorted(table.columns, key=lambda c: c.name) + ], + } + if include_row_counts: + table_data["row_count"] = table.row_count + + schema_data.append(table_data) + + # Create deterministic JSON string + schema_json = json.dumps(schema_data, sort_keys=True, separators=(",", ":")) + + # Compute SHA256 hash + hash_bytes = hashlib.sha256(schema_json.encode()).hexdigest() + + # Return first 16 characters for reasonable uniqueness + readability + return hash_bytes[:16] + + def compute_for_handle(self, handle_ref: str, db_path: Path) -> str: + """ + Compute fingerprint incorporating handle reference. + + This creates a unique ID that includes both the source + handle and the schema structure. + + Args: + handle_ref: The KBase handle reference + db_path: Path to the SQLite database + + Returns: + Combined fingerprint string + """ + schema_fp = self.compute(db_path) + # Sanitize handle ref for use in filenames + safe_handle = handle_ref.replace("/", "_").replace(":", "_") + return f"{safe_handle}_{schema_fp}" + + # ─── Cache Management ─────────────────────────────────────────────────── + + def is_cached(self, fingerprint: str) -> bool: + """Check if a config is cached for this fingerprint.""" + config_path = self._get_cache_path(fingerprint) + return config_path.exists() + + def get_cached_config(self, fingerprint: str) -> dict | None: + """ + Retrieve cached config for a fingerprint. + + Args: + fingerprint: Database fingerprint + + Returns: + Cached config dict or None if not found + """ + config_path = self._get_cache_path(fingerprint) + + if not config_path.exists(): + return None + + try: + with open(config_path, "r") as f: + return json.load(f) + except (json.JSONDecodeError, OSError) as e: + logger.warning(f"Failed to load cached config {fingerprint}: {e}") + return None + + def cache_config(self, fingerprint: str, config: dict) -> Path: + """ + Cache a generated config. + + Args: + fingerprint: Database fingerprint + config: Generated config to cache + + Returns: + Path to the cached config file + """ + config_path = self._get_cache_path(fingerprint) + + # Add metadata + config_with_meta = { + "_fingerprint": fingerprint, + "_cached_at": self._get_timestamp(), + **config, + } + + with open(config_path, "w") as f: + json.dump(config_with_meta, f, indent=2) + + logger.info(f"Cached config to {config_path}") + return config_path + + def clear_cache(self, fingerprint: str | None = None) -> int: + """ + Clear cached configs. + + Args: + fingerprint: Specific fingerprint to clear, or None for all + + Returns: + Number of configs cleared + """ + if fingerprint: + config_path = self._get_cache_path(fingerprint) + if config_path.exists(): + config_path.unlink() + return 1 + return 0 + + # Clear all + count = 0 + for config_file in self.config_dir.glob("*.json"): + config_file.unlink() + count += 1 + return count + + def list_cached(self) -> list[dict[str, Any]]: + """List all cached configs with metadata.""" + cached: list[dict[str, Any]] = [] + + for config_file in self.config_dir.glob("*.json"): + try: + with open(config_file, "r") as f: + config = json.load(f) + cached.append({ + "fingerprint": config.get("_fingerprint", config_file.stem), + "cached_at": config.get("_cached_at"), + "id": config.get("id"), + "name": config.get("name"), + "path": str(config_file), + }) + except (json.JSONDecodeError, OSError): + continue + + return cached + + # ─── Private Methods ──────────────────────────────────────────────────── + + def _get_cache_path(self, fingerprint: str) -> Path: + """Get cache file path for a fingerprint.""" + return self.config_dir / f"{fingerprint}.json" + + def _get_timestamp(self) -> str: + """Get current ISO timestamp.""" + from datetime import datetime, timezone + return datetime.now(timezone.utc).isoformat() diff --git a/app/services/schema_analyzer.py b/app/services/schema_analyzer.py new file mode 100644 index 0000000..94cc32a --- /dev/null +++ b/app/services/schema_analyzer.py @@ -0,0 +1,374 @@ +""" +Schema Analyzer. + +Comprehensive database schema introspection with sample value analysis. +Profiles tables and columns to provide input for type inference and AI analysis. +""" + +from __future__ import annotations + +import logging +import sqlite3 +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +logger = logging.getLogger(__name__) + + +@dataclass +class ColumnProfile: + """Detailed profile of a database column.""" + + name: str + sqlite_type: str # INTEGER, TEXT, REAL, BLOB, NULL + sample_values: list[Any] = field(default_factory=list) + null_count: int = 0 + total_count: int = 0 + unique_count: int = 0 + avg_length: float = 0.0 # For TEXT columns + min_value: Any = None # For numeric columns + max_value: Any = None + detected_patterns: list[str] = field(default_factory=list) + + @property + def null_ratio(self) -> float: + """Percentage of NULL values.""" + return self.null_count / self.total_count if self.total_count > 0 else 0.0 + + @property + def unique_ratio(self) -> float: + """Cardinality indicator (unique / total).""" + return self.unique_count / self.total_count if self.total_count > 0 else 0.0 + + @property + def is_likely_id(self) -> bool: + """Check if column is likely an identifier.""" + # High cardinality + low nulls + ID-like name pattern + return ( + self.unique_ratio > 0.9 and + self.null_ratio < 0.01 and + any(p in self.name.lower() for p in ["id", "key", "ref"]) + ) + + +@dataclass +class TableProfile: + """Complete profile of a database table.""" + + name: str + row_count: int = 0 + columns: list[ColumnProfile] = field(default_factory=list) + primary_key: str | None = None + foreign_keys: list[str] = field(default_factory=list) + + @property + def column_count(self) -> int: + return len(self.columns) + + def get_column(self, name: str) -> ColumnProfile | None: + """Get a column profile by name.""" + for col in self.columns: + if col.name == name: + return col + return None + + +class SchemaAnalyzer: + """ + Database schema introspection and profiling. + + Analyzes SQLite databases to extract: + - Table metadata (row counts, column counts) + - Column details (types, nullability, cardinality) + - Sample values for type inference + - Statistical summaries + """ + + def __init__(self, sample_size: int = 10) -> None: + """ + Initialize the schema analyzer. + + Args: + sample_size: Number of sample values to collect per column + """ + self.sample_size = sample_size + + def analyze_database(self, db_path: Path) -> list[TableProfile]: + """ + Analyze all tables in a SQLite database. + + Args: + db_path: Path to the SQLite database file + + Returns: + List of TableProfile objects for each table + """ + profiles: list[TableProfile] = [] + + try: + conn = sqlite3.connect(str(db_path)) + cursor = conn.cursor() + + # Get list of user tables + cursor.execute(""" + SELECT name FROM sqlite_master + WHERE type='table' + AND name NOT LIKE 'sqlite_%' + ORDER BY name + """) + tables = [row[0] for row in cursor.fetchall()] + + for table_name in tables: + try: + profile = self._analyze_table(cursor, table_name) + profiles.append(profile) + except Exception as e: + logger.warning(f"Error analyzing table {table_name}: {e}") + + conn.close() + + except sqlite3.Error as e: + logger.error(f"Error opening database {db_path}: {e}") + raise + + logger.info(f"Analyzed {len(profiles)} tables from {db_path}") + return profiles + + def analyze_table(self, db_path: Path, table_name: str) -> TableProfile: + """ + Analyze a single table in a SQLite database. + + Args: + db_path: Path to the SQLite database file + table_name: Name of the table to analyze + + Returns: + TableProfile for the specified table + """ + try: + conn = sqlite3.connect(str(db_path)) + cursor = conn.cursor() + + profile = self._analyze_table(cursor, table_name) + + conn.close() + return profile + + except sqlite3.Error as e: + logger.error(f"Error analyzing table {table_name}: {e}") + raise + + def get_sample_values( + self, + db_path: Path, + table_name: str, + column_name: str, + n: int | None = None + ) -> list[Any]: + """ + Get sample values from a specific column. + + Args: + db_path: Path to the SQLite database file + table_name: Name of the table + column_name: Name of the column + n: Number of samples (defaults to self.sample_size) + + Returns: + List of sample values (distinct, non-null when possible) + """ + if n is None: + n = self.sample_size + + try: + conn = sqlite3.connect(str(db_path)) + cursor = conn.cursor() + + # Validate table exists + cursor.execute( + "SELECT name FROM sqlite_master WHERE type='table' AND name=?", + (table_name,) + ) + if not cursor.fetchone(): + raise ValueError(f"Table not found: {table_name}") + + # Get distinct non-null samples first + safe_col = column_name.replace('"', '""') + cursor.execute(f''' + SELECT DISTINCT "{safe_col}" + FROM "{table_name}" + WHERE "{safe_col}" IS NOT NULL + LIMIT ? + ''', (n,)) + + samples = [row[0] for row in cursor.fetchall()] + conn.close() + + return samples + + except sqlite3.Error as e: + logger.error(f"Error getting samples from {table_name}.{column_name}: {e}") + raise + + # ─── Private Methods ──────────────────────────────────────────────────── + + def _analyze_table(self, cursor: sqlite3.Cursor, table_name: str) -> TableProfile: + """Analyze a single table using an open cursor.""" + + profile = TableProfile(name=table_name) + + # Get row count + cursor.execute(f'SELECT COUNT(*) FROM "{table_name}"') + profile.row_count = cursor.fetchone()[0] + + # Get column info + cursor.execute(f'PRAGMA table_info("{table_name}")') + columns_info = cursor.fetchall() + + # Get primary key + for col_info in columns_info: + if col_info[5] == 1: # pk column in PRAGMA result + profile.primary_key = col_info[1] + break + + # Analyze each column + for col_info in columns_info: + col_name = col_info[1] + col_type = col_info[2] or "TEXT" + + col_profile = self._analyze_column( + cursor, table_name, col_name, col_type, profile.row_count + ) + profile.columns.append(col_profile) + + return profile + + def _analyze_column( + self, + cursor: sqlite3.Cursor, + table_name: str, + col_name: str, + col_type: str, + row_count: int + ) -> ColumnProfile: + """Analyze a single column.""" + + safe_col = col_name.replace('"', '""') + safe_table = table_name.replace('"', '""') + + profile = ColumnProfile( + name=col_name, + sqlite_type=col_type.upper(), + total_count=row_count, + ) + + if row_count == 0: + return profile + + # Get null count + cursor.execute(f''' + SELECT COUNT(*) FROM "{safe_table}" WHERE "{safe_col}" IS NULL + ''') + profile.null_count = cursor.fetchone()[0] + + # Get unique count (limit to avoid performance issues on large tables) + try: + cursor.execute(f''' + SELECT COUNT(DISTINCT "{safe_col}") FROM "{safe_table}" + ''') + profile.unique_count = cursor.fetchone()[0] + except sqlite3.Error: + profile.unique_count = 0 + + # Get sample values (distinct, non-null) + cursor.execute(f''' + SELECT DISTINCT "{safe_col}" + FROM "{safe_table}" + WHERE "{safe_col}" IS NOT NULL + LIMIT {self.sample_size} + ''') + profile.sample_values = [row[0] for row in cursor.fetchall()] + + # Get statistics for numeric columns + if col_type.upper() in ("INTEGER", "REAL", "NUMERIC"): + try: + cursor.execute(f''' + SELECT MIN("{safe_col}"), MAX("{safe_col}"), AVG(LENGTH(CAST("{safe_col}" AS TEXT))) + FROM "{safe_table}" + WHERE "{safe_col}" IS NOT NULL + ''') + result = cursor.fetchone() + if result: + profile.min_value = result[0] + profile.max_value = result[1] + profile.avg_length = result[2] or 0.0 + except sqlite3.Error: + pass + + # Get average length for text columns + elif col_type.upper() in ("TEXT", "VARCHAR", "CHAR", ""): + try: + cursor.execute(f''' + SELECT AVG(LENGTH("{safe_col}")) + FROM "{safe_table}" + WHERE "{safe_col}" IS NOT NULL + ''') + result = cursor.fetchone() + if result and result[0]: + profile.avg_length = float(result[0]) + except sqlite3.Error: + pass + + # Detect patterns in sample values + profile.detected_patterns = self._detect_patterns(profile.sample_values) + + return profile + + def _detect_patterns(self, values: list[Any]) -> list[str]: + """Detect common patterns in sample values.""" + patterns: list[str] = [] + + if not values: + return patterns + + str_values = [str(v) for v in values if v is not None] + if not str_values: + return patterns + + # Check for URL pattern + if all(v.startswith(("http://", "https://")) for v in str_values): + patterns.append("url") + + # Check for email pattern + if all("@" in v and "." in v for v in str_values): + patterns.append("email") + + # Check for GO term pattern + if all(v.startswith("GO:") for v in str_values): + patterns.append("go_term") + + # Check for ISO date pattern + import re + date_pattern = re.compile(r"^\d{4}-\d{2}-\d{2}") + if all(date_pattern.match(v) for v in str_values): + patterns.append("iso_date") + + # Check for UUID pattern + uuid_pattern = re.compile( + r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$", + re.IGNORECASE + ) + if all(uuid_pattern.match(v) for v in str_values): + patterns.append("uuid") + + # Check for sequence pattern (DNA/RNA/Protein) + seq_pattern = re.compile(r"^[ATCGUN]+$", re.IGNORECASE) + protein_pattern = re.compile(r"^[ACDEFGHIKLMNPQRSTVWY]+$", re.IGNORECASE) + if all(len(v) > 20 for v in str_values): + if all(seq_pattern.match(v) for v in str_values): + patterns.append("nucleotide_sequence") + elif all(protein_pattern.match(v) for v in str_values): + patterns.append("protein_sequence") + + return patterns diff --git a/app/services/type_inference.py b/app/services/type_inference.py new file mode 100644 index 0000000..4b3a377 --- /dev/null +++ b/app/services/type_inference.py @@ -0,0 +1,476 @@ +""" +Type Inference Engine. + +Rule-based pattern detection for inferring column data types and rendering +configurations. This module provides fast, deterministic type inference +without requiring AI, and serves as the foundation for hybrid inference. + +Works independently of AI providers and can serve as a fallback. +""" + +from __future__ import annotations + +import re +from dataclasses import dataclass, field +from typing import Any, Literal +from enum import Enum + + +class DataType(str, Enum): + """Column data types matching DataTables_Viewer ColumnDataType.""" + STRING = "string" + NUMBER = "number" + INTEGER = "integer" + FLOAT = "float" + BOOLEAN = "boolean" + DATE = "date" + DATETIME = "datetime" + TIMESTAMP = "timestamp" + JSON = "json" + ARRAY = "array" + SEQUENCE = "sequence" + ID = "id" + URL = "url" + EMAIL = "email" + ONTOLOGY = "ontology" + PERCENTAGE = "percentage" + FILESIZE = "filesize" + DURATION = "duration" + CURRENCY = "currency" + COLOR = "color" + IMAGE = "image" + CUSTOM = "custom" + + +@dataclass +class TransformConfig: + """Transform configuration for cell rendering.""" + type: str + options: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class InferredType: + """Result of type inference for a column.""" + data_type: DataType + display_name: str + categories: list[str] + transform: TransformConfig | None = None + width: str = "auto" + pin: Literal["left", "right"] | None = None + sortable: bool = True + filterable: bool = True + copyable: bool = False + confidence: float = 1.0 + source: Literal["rules", "ai", "hybrid"] = "rules" + + +# ============================================================================= +# PATTERN DEFINITIONS +# ============================================================================= + +# Column name patterns mapped to inference results +NAME_PATTERNS: list[tuple[re.Pattern, dict[str, Any]]] = [ + # IDs - typically pinned left + (re.compile(r"^(ID|id)$"), { + "data_type": DataType.ID, + "categories": ["core"], + "pin": "left", + "copyable": True, + "width": "100px", + }), + (re.compile(r".*_ID$|.*_id$|.*Id$"), { + "data_type": DataType.ID, + "categories": ["core"], + "copyable": True, + "width": "120px", + }), + (re.compile(r"^Database_ID$|^database_id$"), { + "data_type": DataType.ID, + "categories": ["core"], + "copyable": True, + "width": "130px", + }), + + # External database references with link transforms + (re.compile(r"^Uniprot.*|^uniprot.*|.*UniProt.*"), { + "data_type": DataType.ID, + "categories": ["external"], + "width": "100px", + "transform": TransformConfig( + type="link", + options={ + "urlTemplate": "https://www.uniprot.org/uniprotkb/{value}", + "target": "_blank", + "icon": "bi-link-45deg" + } + ), + }), + (re.compile(r"^KEGG.*|^kegg.*"), { + "data_type": DataType.ID, + "categories": ["external"], + "width": "90px", + "transform": TransformConfig( + type="link", + options={ + "urlTemplate": "https://www.genome.jp/entry/{value}", + "target": "_blank" + } + ), + }), + (re.compile(r"^GO_.*|^go_.*"), { + "data_type": DataType.ONTOLOGY, + "categories": ["functional"], + "width": "180px", + "transform": TransformConfig( + type="ontology", + options={ + "prefix": "GO", + "urlTemplate": "https://amigo.geneontology.org/amigo/term/{value}", + "style": "badge" + } + ), + }), + + # Sequences + (re.compile(r".*Sequence.*|.*_seq$|.*_Seq$"), { + "data_type": DataType.SEQUENCE, + "categories": ["sequence"], + "sortable": False, + "filterable": False, + "copyable": True, + "width": "150px", + "transform": TransformConfig( + type="sequence", + options={"maxLength": 20, "showCopyButton": True} + ), + }), + + # Function/product descriptions + (re.compile(r".*function.*|.*Function.*|.*product.*|.*Product.*"), { + "data_type": DataType.STRING, + "categories": ["functional"], + "width": "300px", + }), + + # Statistical measures with special formatting + (re.compile(r"^Log2FC$|.*log2.*fold.*|.*Log2.*Fold.*"), { + "data_type": DataType.FLOAT, + "categories": ["expression"], + "width": "130px", + "transform": TransformConfig( + type="heatmap", + options={ + "min": -4, "max": 4, + "colorScale": "diverging", + "showValue": True, + "decimals": 2 + } + ), + }), + (re.compile(r"^P[_-]?[Vv]alue$|^pvalue$|^p_value$"), { + "data_type": DataType.FLOAT, + "categories": ["statistics"], + "width": "100px", + "transform": TransformConfig( + type="number", + options={"notation": "scientific", "decimals": 2} + ), + }), + (re.compile(r"^FDR$|^fdr$|^q[_-]?value$"), { + "data_type": DataType.FLOAT, + "categories": ["statistics"], + "width": "100px", + "transform": TransformConfig( + type="number", + options={"notation": "scientific", "decimals": 2} + ), + }), + + # Boolean indicators + (re.compile(r"^Significant$|^is_.*|^has_.*"), { + "data_type": DataType.BOOLEAN, + "categories": ["statistics"], + "width": "90px", + "transform": TransformConfig( + type="boolean", + options={ + "trueIcon": "bi-check-circle-fill", + "falseIcon": "bi-x-circle", + "trueColor": "#22c55e", + "falseColor": "#94a3b8" + } + ), + }), + + # Temperature with unit + (re.compile(r".*Temperature.*|.*_in_C$"), { + "data_type": DataType.FLOAT, + "categories": ["experimental"], + "width": "120px", + "transform": TransformConfig( + type="number", + options={"decimals": 1, "suffix": "°C"} + ), + }), + + # Concentration fields + (re.compile(r".*Concentration.*|.*_in_mM$|.*_in_mg.*"), { + "data_type": DataType.FLOAT, + "categories": ["media"], + "width": "120px", + "transform": TransformConfig( + type="number", + options={"decimals": 2} + ), + }), + + # Name fields + (re.compile(r"^Name$|^name$|.*_Name$|.*_name$"), { + "data_type": DataType.STRING, + "categories": ["core"], + "width": "200px", + }), + + # URL fields + (re.compile(r".*_URL$|.*_url$|.*Link$|.*link$"), { + "data_type": DataType.URL, + "categories": ["external"], + "width": "150px", + }), +] + +# Value patterns for detecting types from sample data +VALUE_PATTERNS: list[tuple[re.Pattern, DataType]] = [ + # URLs + (re.compile(r"^https?://"), DataType.URL), + # Email + (re.compile(r"^[\w.+-]+@[\w-]+\.[\w.-]+$"), DataType.EMAIL), + # GO terms + (re.compile(r"^GO:\d{7}"), DataType.ONTOLOGY), + # ISO dates + (re.compile(r"^\d{4}-\d{2}-\d{2}$"), DataType.DATE), + (re.compile(r"^\d{4}-\d{2}-\d{2}[T ]\d{2}:\d{2}"), DataType.DATETIME), + # Colors + (re.compile(r"^#[0-9a-fA-F]{6}$|^rgb\("), DataType.COLOR), + # DNA/RNA sequences (long strings of ATCGU only) + (re.compile(r"^[ATCGU]{20,}$", re.IGNORECASE), DataType.SEQUENCE), + # Protein sequences (amino acid codes) + (re.compile(r"^[ACDEFGHIKLMNPQRSTVWY]{20,}$", re.IGNORECASE), DataType.SEQUENCE), +] + + +# ============================================================================= +# TYPE INFERENCE ENGINE +# ============================================================================= + +class TypeInferenceEngine: + """ + Rule-based type inference engine. + + Analyzes column names and sample values to infer data types, + display configurations, and rendering transforms without AI. + """ + + def __init__(self) -> None: + self._name_patterns = NAME_PATTERNS + self._value_patterns = VALUE_PATTERNS + + def infer_from_name(self, column_name: str) -> InferredType | None: + """ + Infer column type from column name patterns. + + Args: + column_name: The name of the column + + Returns: + InferredType if a pattern matches, None otherwise + """ + for pattern, config in self._name_patterns: + if pattern.match(column_name): + return InferredType( + data_type=config.get("data_type", DataType.STRING), + display_name=self._format_display_name(column_name), + categories=config.get("categories", []), + transform=config.get("transform"), + width=config.get("width", "auto"), + pin=config.get("pin"), + sortable=config.get("sortable", True), + filterable=config.get("filterable", True), + copyable=config.get("copyable", False), + confidence=0.9, # High confidence for name pattern match + source="rules", + ) + return None + + def infer_from_values( + self, + column_name: str, + sample_values: list[Any], + sqlite_type: str = "TEXT" + ) -> InferredType: + """ + Infer column type from sample values. + + Args: + column_name: The name of the column + sample_values: List of sample values from the column + sqlite_type: The SQLite column type + + Returns: + InferredType with inferred configuration + """ + # First, try name-based inference + name_inference = self.infer_from_name(column_name) + if name_inference: + return name_inference + + # Filter out None/empty values for analysis + valid_values = [v for v in sample_values if v is not None and str(v).strip()] + + if not valid_values: + return self._default_inference(column_name, sqlite_type) + + # Check for boolean values + if self._is_boolean(valid_values): + return InferredType( + data_type=DataType.BOOLEAN, + display_name=self._format_display_name(column_name), + categories=["metadata"], + confidence=0.95, + ) + + # Check for numeric types based on SQLite type and values + if sqlite_type in ("INTEGER", "REAL") or self._is_numeric(valid_values): + return self._infer_numeric(column_name, valid_values, sqlite_type) + + # Check value patterns + str_values = [str(v) for v in valid_values] + for pattern, data_type in self._value_patterns: + matches = sum(1 for v in str_values if pattern.match(v)) + if matches / len(str_values) > 0.5: # >50% match threshold + return InferredType( + data_type=data_type, + display_name=self._format_display_name(column_name), + categories=self._default_category(data_type), + confidence=0.8, + ) + + # Default to string + return self._default_inference(column_name, sqlite_type) + + def infer( + self, + column_name: str, + sample_values: list[Any] | None = None, + sqlite_type: str = "TEXT" + ) -> InferredType: + """ + Full inference combining name and value analysis. + + Args: + column_name: The name of the column + sample_values: Optional list of sample values + sqlite_type: The SQLite column type + + Returns: + InferredType with best inference + """ + if sample_values: + return self.infer_from_values(column_name, sample_values, sqlite_type) + + name_inference = self.infer_from_name(column_name) + if name_inference: + return name_inference + + return self._default_inference(column_name, sqlite_type) + + # ─── Helper Methods ───────────────────────────────────────────────────── + + def _format_display_name(self, column_name: str) -> str: + """Convert column name to human-readable display name.""" + # Replace underscores and handle camelCase + name = re.sub(r"_", " ", column_name) + name = re.sub(r"([a-z])([A-Z])", r"\1 \2", name) + # Title case but preserve acronyms + words = name.split() + formatted = [] + for word in words: + if word.isupper() and len(word) <= 4: # Likely acronym + formatted.append(word) + else: + formatted.append(word.capitalize()) + return " ".join(formatted) + + def _is_boolean(self, values: list[Any]) -> bool: + """Check if values represent boolean data.""" + bool_values = {"true", "false", "yes", "no", "1", "0", "t", "f", "y", "n"} + str_values = {str(v).lower() for v in values} + return str_values.issubset(bool_values) and len(str_values) <= 2 + + def _is_numeric(self, values: list[Any]) -> bool: + """Check if all values are numeric.""" + for v in values: + if v is None: + continue + try: + float(v) + except (ValueError, TypeError): + return False + return True + + def _infer_numeric( + self, + column_name: str, + values: list[Any], + sqlite_type: str + ) -> InferredType: + """Infer numeric type details.""" + # Check if all values are integers + is_integer = all( + isinstance(v, int) or (isinstance(v, float) and v.is_integer()) + for v in values if v is not None + ) + + data_type = DataType.INTEGER if (sqlite_type == "INTEGER" or is_integer) else DataType.FLOAT + + return InferredType( + data_type=data_type, + display_name=self._format_display_name(column_name), + categories=["data"], + width="100px", + transform=TransformConfig( + type="number", + options={"decimals": 0 if is_integer else 2} + ) if data_type == DataType.FLOAT else None, + confidence=0.85, + ) + + def _default_inference(self, column_name: str, sqlite_type: str) -> InferredType: + """Return default string inference.""" + # Map SQLite types to data types + type_map = { + "INTEGER": DataType.INTEGER, + "REAL": DataType.FLOAT, + "BLOB": DataType.CUSTOM, + } + + return InferredType( + data_type=type_map.get(sqlite_type, DataType.STRING), + display_name=self._format_display_name(column_name), + categories=["data"], + confidence=0.5, + ) + + def _default_category(self, data_type: DataType) -> list[str]: + """Get default categories for a data type.""" + category_map = { + DataType.ID: ["core"], + DataType.URL: ["external"], + DataType.EMAIL: ["external"], + DataType.ONTOLOGY: ["functional"], + DataType.SEQUENCE: ["sequence"], + DataType.DATE: ["metadata"], + DataType.DATETIME: ["metadata"], + } + return category_map.get(data_type, ["data"]) From bdb46cee5d00ed4020de49b85a1921f1bf921675 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Wed, 14 Jan 2026 09:24:56 -0600 Subject: [PATCH 04/19] temp ai integrated config cache --- app/configs/__init__.py | 26 + app/configs/berdl_tables.json | 998 ++++++++++++++++++++++++++++ app/configs/fallback_registry.py | 203 ++++++ app/configs/genome_data_tables.json | 527 +++++++++++++++ app/models.py | 95 ++- app/routes.py | 308 ++++++++- app/services/__init__.py | 11 + app/services/prompts.py | 340 ++++++++++ app/services/type_inference.py | 74 +++ app/services/validation.py | 383 +++++++++++ tests/__init__.py | 1 + tests/test_config_generation.py | 521 +++++++++++++++ 12 files changed, 3484 insertions(+), 3 deletions(-) create mode 100644 app/configs/__init__.py create mode 100644 app/configs/berdl_tables.json create mode 100644 app/configs/fallback_registry.py create mode 100644 app/configs/genome_data_tables.json create mode 100644 app/services/prompts.py create mode 100644 app/services/validation.py create mode 100644 tests/__init__.py create mode 100644 tests/test_config_generation.py diff --git a/app/configs/__init__.py b/app/configs/__init__.py new file mode 100644 index 0000000..8d251ec --- /dev/null +++ b/app/configs/__init__.py @@ -0,0 +1,26 @@ +""" +Built-in Fallback Configs Package. + +Contains pre-built DataTables Viewer configurations for known KBase object types. +These are used when AI generation fails or for fast config matching. +""" + +from .fallback_registry import ( + get_fallback_config, + get_fallback_config_id, + has_fallback_config, + load_config_file, + list_available_configs, + get_config_for_tables, + clear_cache, +) + +__all__ = [ + "get_fallback_config", + "get_fallback_config_id", + "has_fallback_config", + "load_config_file", + "list_available_configs", + "get_config_for_tables", + "clear_cache", +] diff --git a/app/configs/berdl_tables.json b/app/configs/berdl_tables.json new file mode 100644 index 0000000..dd21556 --- /dev/null +++ b/app/configs/berdl_tables.json @@ -0,0 +1,998 @@ +{ + "id": "berdl_tables", + "name": "BERDL Genome Tables", + "description": "Pangenome analysis tables from BERDL/GenomeDataLakes objects", + "version": "1.0.0", + "icon": "bi-collection", + "color": "#6366f1", + "defaults": { + "pageSize": 50, + "density": "default", + "showRowNumbers": true, + "enableSelection": true, + "enableExport": true + }, + "sharedCategories": [ + { + "id": "core", + "name": "Core Info", + "description": "Essential identifiers and names", + "icon": "bi-database", + "color": "#6366f1", + "defaultVisible": true, + "order": 1 + }, + { + "id": "functional", + "name": "Functional Annotation", + "description": "Function and product information", + "icon": "bi-gear", + "color": "#22c55e", + "defaultVisible": true, + "order": 2 + }, + { + "id": "external", + "name": "External Links", + "description": "Links to external databases", + "icon": "bi-box-arrow-up-right", + "color": "#06b6d4", + "defaultVisible": true, + "order": 3 + }, + { + "id": "sequence", + "name": "Sequence Data", + "description": "DNA, RNA, and protein sequences", + "icon": "bi-text-left", + "color": "#f59e0b", + "defaultVisible": false, + "order": 4 + }, + { + "id": "ontology", + "name": "Ontology Terms", + "description": "GO, KEGG, Pfam annotations", + "icon": "bi-tags", + "color": "#8b5cf6", + "defaultVisible": true, + "order": 5 + }, + { + "id": "statistics", + "name": "Statistics", + "description": "Numerical metrics and scores", + "icon": "bi-graph-up", + "color": "#ef4444", + "defaultVisible": true, + "order": 6 + }, + { + "id": "pangenome", + "name": "Pangenome Analysis", + "description": "Core/accessory gene classification", + "icon": "bi-diagram-3", + "color": "#14b8a6", + "defaultVisible": true, + "order": 7 + } + ], + "tables": { + "genome": { + "displayName": "Genomes", + "description": "Genome metadata and taxonomy information", + "icon": "bi-circle", + "settings": { + "defaultSortColumn": "id", + "defaultSortOrder": "asc" + }, + "columns": [ + { + "column": "id", + "displayName": "Genome ID", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "copyable": true, + "width": "140px", + "pin": "left" + }, + { + "column": "gtdb_taxonomy", + "displayName": "GTDB Taxonomy", + "dataType": "string", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "searchable": true, + "width": "400px" + }, + { + "column": "ncbi_taxonomy", + "displayName": "NCBI Taxonomy", + "dataType": "string", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "width": "300px" + }, + { + "column": "n_contigs", + "displayName": "Contigs", + "dataType": "integer", + "categories": [ + "statistics" + ], + "sortable": true, + "filterable": true, + "align": "right", + "width": "100px" + }, + { + "column": "n_features", + "displayName": "Features", + "dataType": "integer", + "categories": [ + "statistics" + ], + "sortable": true, + "filterable": true, + "align": "right", + "width": "100px" + } + ] + }, + "genome_features": { + "displayName": "Genome Features", + "description": "Gene annotations with functional and ontology data", + "icon": "bi-list-ul", + "settings": { + "defaultSortColumn": "id", + "defaultSortOrder": "asc" + }, + "columns": [ + { + "column": "id", + "displayName": "ID", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "width": "80px", + "pin": "left" + }, + { + "column": "genome_id", + "displayName": "Genome", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "width": "120px" + }, + { + "column": "contig_id", + "displayName": "Contig", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "width": "200px" + }, + { + "column": "feature_id", + "displayName": "Feature ID", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "copyable": true, + "width": "120px" + }, + { + "column": "length", + "displayName": "Length", + "dataType": "integer", + "categories": [ + "statistics" + ], + "sortable": true, + "align": "right", + "width": "80px" + }, + { + "column": "start", + "displayName": "Start", + "dataType": "integer", + "categories": [ + "statistics" + ], + "sortable": true, + "align": "right", + "width": "80px" + }, + { + "column": "end", + "displayName": "End", + "dataType": "integer", + "categories": [ + "statistics" + ], + "sortable": true, + "align": "right", + "width": "80px" + }, + { + "column": "strand", + "displayName": "Strand", + "dataType": "string", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "align": "center", + "width": "60px" + }, + { + "column": "sequence", + "displayName": "Protein Sequence", + "dataType": "sequence", + "categories": [ + "sequence" + ], + "sortable": false, + "copyable": true, + "width": "150px", + "transform": { + "type": "sequence", + "options": { + "maxLength": 20, + "showCopyButton": true + } + } + }, + { + "column": "bakta_function", + "displayName": "Function (Bakta)", + "dataType": "string", + "categories": [ + "functional" + ], + "sortable": true, + "filterable": true, + "searchable": true, + "width": "250px" + }, + { + "column": "rast_function", + "displayName": "Function (RAST)", + "dataType": "string", + "categories": [ + "functional" + ], + "sortable": true, + "filterable": true, + "searchable": true, + "width": "250px" + }, + { + "column": "gene_names", + "displayName": "Gene Names", + "dataType": "string", + "categories": [ + "functional" + ], + "sortable": true, + "filterable": true, + "width": "120px" + }, + { + "column": "go", + "displayName": "GO Terms", + "dataType": "ontology", + "categories": [ + "ontology" + ], + "sortable": false, + "filterable": true, + "width": "250px", + "transform": { + "type": "ontologyLookup", + "options": { + "ontologyType": "go", + "urlTemplate": "https://amigo.geneontology.org/amigo/term/{id}", + "idPattern": "(GO:\\d+)", + "delimiter": ";", + "maxLength": 40, + "showId": true, + "style": "inline" + } + } + }, + { + "column": "ko", + "displayName": "KEGG Orthologs", + "dataType": "ontology", + "categories": [ + "ontology" + ], + "sortable": false, + "filterable": true, + "width": "280px", + "transform": { + "type": "ontologyLookup", + "options": { + "ontologyType": "kegg", + "urlTemplate": "https://www.genome.jp/entry/{id}", + "idPattern": "(K\\d+)", + "delimiter": ";", + "maxLength": 45, + "showId": true, + "style": "inline" + } + } + }, + { + "column": "pfam", + "displayName": "Pfam Domains", + "dataType": "ontology", + "categories": [ + "ontology" + ], + "sortable": false, + "filterable": true, + "width": "250px", + "transform": { + "type": "ontologyLookup", + "options": { + "ontologyType": "pfam", + "urlTemplate": "https://www.ebi.ac.uk/interpro/entry/pfam/{id}", + "idPattern": "(PF\\d+)", + "delimiter": ";", + "maxLength": 40, + "showId": true, + "style": "inline" + } + } + }, + { + "column": "cog", + "displayName": "COG", + "dataType": "ontology", + "categories": [ + "ontology" + ], + "sortable": true, + "filterable": true, + "width": "220px", + "transform": { + "type": "ontologyLookup", + "options": { + "ontologyType": "cog", + "urlTemplate": "https://www.ncbi.nlm.nih.gov/research/cog/cog/{id}", + "idPattern": "(COG\\d+|[A-Z])", + "delimiter": ";", + "maxLength": 40, + "showId": true, + "style": "inline" + } + } + }, + { + "column": "ec", + "displayName": "EC Number", + "dataType": "ontology", + "categories": [ + "ontology" + ], + "sortable": true, + "filterable": true, + "width": "240px", + "transform": { + "type": "ontologyLookup", + "options": { + "ontologyType": "ec", + "urlTemplate": "https://enzyme.expasy.org/EC/{id}", + "idPattern": "([\\d]+\\.[\\d.-]+)", + "delimiter": ";", + "maxLength": 40, + "showId": true, + "style": "inline" + } + } + }, + { + "column": "so", + "displayName": "Sequence Ontology", + "dataType": "ontology", + "categories": [ + "ontology" + ], + "sortable": false, + "width": "200px", + "transform": { + "type": "ontologyLookup", + "options": { + "ontologyType": "so", + "urlTemplate": "http://www.sequenceontology.org/browser/current_release/term/{id}", + "idPattern": "(SO:\\d+)", + "maxLength": 35, + "showId": true, + "style": "inline" + } + } + }, + { + "column": "uniref_100", + "displayName": "UniRef100", + "dataType": "ontology", + "categories": [ + "external" + ], + "sortable": true, + "width": "280px", + "transform": { + "type": "ontologyLookup", + "options": { + "ontologyType": "uniref", + "urlTemplate": "https://www.uniprot.org/uniref/{id}", + "idPattern": "(UniRef\\d+_[A-Z0-9]+)", + "maxLength": 40, + "showId": true, + "style": "inline" + } + } + }, + { + "column": "uniref_90", + "displayName": "UniRef90", + "dataType": "ontology", + "categories": [ + "external" + ], + "sortable": true, + "width": "280px", + "transform": { + "type": "ontologyLookup", + "options": { + "ontologyType": "uniref", + "urlTemplate": "https://www.uniprot.org/uniref/{id}", + "idPattern": "(UniRef\\d+_[A-Z0-9]+)", + "maxLength": 40, + "showId": true, + "style": "inline" + } + } + }, + { + "column": "uniref_50", + "displayName": "UniRef50", + "dataType": "ontology", + "categories": [ + "external" + ], + "sortable": true, + "width": "280px", + "transform": { + "type": "ontologyLookup", + "options": { + "ontologyType": "uniref", + "urlTemplate": "https://www.uniprot.org/uniref/{id}", + "idPattern": "(UniRef\\d+_[A-Z0-9]+)", + "maxLength": 40, + "showId": true, + "style": "inline" + } + } + }, + { + "column": "pangenome_cluster_id", + "displayName": "Cluster ID", + "dataType": "id", + "categories": [ + "pangenome" + ], + "sortable": true, + "filterable": true, + "width": "120px" + }, + { + "column": "pangenome_is_core", + "displayName": "Core Gene", + "dataType": "boolean", + "categories": [ + "pangenome" + ], + "sortable": true, + "filterable": true, + "align": "center", + "width": "100px", + "transform": { + "type": "boolean", + "options": { + "trueIcon": "bi-check-circle-fill", + "falseIcon": "bi-x-circle", + "trueColor": "#22c55e", + "falseColor": "#94a3b8" + } + } + }, + { + "column": "psortb", + "displayName": "pSORTb", + "dataType": "string", + "categories": [ + "functional" + ], + "sortable": true, + "filterable": true, + "width": "120px" + }, + { + "column": "reactions", + "displayName": "Reactions", + "dataType": "ontology", + "categories": [ + "functional" + ], + "sortable": false, + "width": "300px", + "transform": { + "type": "ontologyLookup", + "options": { + "ontologyType": "modelseed_reactions", + "urlTemplate": "https://modelseed.org/biochem/reactions/{id}", + "idPattern": "(rxn\\d+)", + "maxLength": 50, + "showId": true, + "style": "inline" + } + } + }, + { + "column": "rast_consistency", + "displayName": "RAST Consistency", + "dataType": "float", + "categories": [ + "statistics" + ], + "sortable": true, + "align": "right", + "width": "120px", + "transform": { + "type": "number", + "options": { + "decimals": 2 + } + } + } + ] + }, + "pan_genome_features": { + "displayName": "Pangenome Features", + "description": "Cluster-level annotations across genomes", + "icon": "bi-diagram-3", + "settings": { + "defaultSortColumn": "id", + "defaultSortOrder": "asc" + }, + "columns": [ + { + "column": "id", + "displayName": "ID", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "width": "80px", + "pin": "left" + }, + { + "column": "genome_id", + "displayName": "Genome", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "width": "120px" + }, + { + "column": "feature_id", + "displayName": "Feature ID", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "copyable": true, + "width": "120px" + }, + { + "column": "cluster_id", + "displayName": "Cluster ID", + "dataType": "id", + "categories": [ + "pangenome" + ], + "sortable": true, + "filterable": true, + "width": "120px" + }, + { + "column": "is_core", + "displayName": "Core Gene", + "dataType": "boolean", + "categories": [ + "pangenome" + ], + "sortable": true, + "filterable": true, + "align": "center", + "width": "100px", + "transform": { + "type": "boolean", + "options": { + "trueIcon": "bi-check-circle-fill", + "falseIcon": "bi-x-circle", + "trueColor": "#22c55e", + "falseColor": "#94a3b8" + } + } + }, + { + "column": "bakta_function", + "displayName": "Function (Bakta)", + "dataType": "string", + "categories": [ + "functional" + ], + "sortable": true, + "searchable": true, + "width": "250px" + }, + { + "column": "rast_function", + "displayName": "Function (RAST)", + "dataType": "string", + "categories": [ + "functional" + ], + "sortable": true, + "searchable": true, + "width": "250px" + }, + { + "column": "go", + "displayName": "GO Terms", + "dataType": "ontology", + "categories": [ + "ontology" + ], + "width": "180px", + "transform": { + "type": "ontology", + "options": { + "prefix": "GO", + "urlTemplate": "https://amigo.geneontology.org/amigo/term/{value}", + "style": "badge" + } + } + }, + { + "column": "ko", + "displayName": "KEGG Orthologs", + "dataType": "ontology", + "categories": [ + "ontology" + ], + "width": "150px", + "transform": { + "type": "ontology", + "options": { + "prefix": "KO", + "urlTemplate": "https://www.genome.jp/entry/{value}", + "style": "badge" + } + } + }, + { + "column": "uniref_90", + "displayName": "UniRef90", + "dataType": "id", + "categories": [ + "external" + ], + "width": "160px", + "transform": { + "type": "chain", + "options": { + "transforms": [ + { + "type": "replace", + "options": { + "find": "UniRef:", + "replace": "" + } + }, + { + "type": "link", + "options": { + "urlTemplate": "https://www.uniprot.org/uniref/{value}", + "target": "_blank" + } + } + ] + } + } + } + ] + }, + "genome_ani": { + "displayName": "Genome ANI", + "description": "Average Nucleotide Identity between genomes", + "icon": "bi-percent", + "settings": { + "defaultSortColumn": "ani", + "defaultSortOrder": "desc" + }, + "columns": [ + { + "column": "genome1", + "displayName": "Genome 1", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "width": "140px", + "pin": "left" + }, + { + "column": "genome2", + "displayName": "Genome 2", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "width": "140px" + }, + { + "column": "ani", + "displayName": "ANI (%)", + "dataType": "float", + "categories": [ + "statistics" + ], + "sortable": true, + "filterable": true, + "align": "right", + "width": "100px", + "transform": { + "type": "heatmap", + "options": { + "min": 90, + "max": 100, + "colorScale": "sequential", + "showValue": true, + "decimals": 2 + } + } + }, + { + "column": "af1", + "displayName": "AF1", + "dataType": "float", + "categories": [ + "statistics" + ], + "sortable": true, + "align": "right", + "width": "80px", + "transform": { + "type": "number", + "options": { + "decimals": 2 + } + } + }, + { + "column": "af2", + "displayName": "AF2", + "dataType": "float", + "categories": [ + "statistics" + ], + "sortable": true, + "align": "right", + "width": "80px", + "transform": { + "type": "number", + "options": { + "decimals": 2 + } + } + }, + { + "column": "kind", + "displayName": "Type", + "dataType": "string", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "width": "100px", + "transform": { + "type": "badge", + "options": { + "variant": "subtle" + } + } + } + ] + }, + "missing_functions": { + "displayName": "Missing Functions", + "description": "Gap-filled reactions and their sources", + "icon": "bi-exclamation-triangle", + "settings": { + "defaultSortColumn": "Reaction", + "defaultSortOrder": "asc" + }, + "columns": [ + { + "column": "Reaction", + "displayName": "Reaction", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "copyable": true, + "width": "150px", + "pin": "left" + }, + { + "column": "RAST_function", + "displayName": "RAST Function", + "dataType": "string", + "categories": [ + "functional" + ], + "sortable": true, + "filterable": true, + "searchable": true, + "width": "300px" + }, + { + "column": "RichGapfill", + "displayName": "Rich Gapfill", + "dataType": "boolean", + "categories": [ + "statistics" + ], + "sortable": true, + "filterable": true, + "align": "center", + "width": "110px", + "transform": { + "type": "boolean", + "options": { + "trueIcon": "bi-check-circle-fill", + "falseIcon": "bi-x-circle", + "trueColor": "#22c55e", + "falseColor": "#94a3b8" + } + } + }, + { + "column": "MinimalGapfill", + "displayName": "Minimal Gapfill", + "dataType": "boolean", + "categories": [ + "statistics" + ], + "sortable": true, + "filterable": true, + "align": "center", + "width": "120px", + "transform": { + "type": "boolean", + "options": { + "trueIcon": "bi-check-circle-fill", + "falseIcon": "bi-x-circle", + "trueColor": "#22c55e", + "falseColor": "#94a3b8" + } + } + }, + { + "column": "PhenotypeGapfill", + "displayName": "Phenotype Gapfill", + "dataType": "boolean", + "categories": [ + "statistics" + ], + "sortable": true, + "filterable": true, + "align": "center", + "width": "130px", + "transform": { + "type": "boolean", + "options": { + "trueIcon": "bi-check-circle-fill", + "falseIcon": "bi-x-circle", + "trueColor": "#22c55e", + "falseColor": "#94a3b8" + } + } + }, + { + "column": "ModuleGapfill", + "displayName": "Module Gapfill", + "dataType": "boolean", + "categories": [ + "statistics" + ], + "sortable": true, + "filterable": true, + "align": "center", + "width": "120px", + "transform": { + "type": "boolean", + "options": { + "trueIcon": "bi-check-circle-fill", + "falseIcon": "bi-x-circle", + "trueColor": "#22c55e", + "falseColor": "#94a3b8" + } + } + }, + { + "column": "Pangenome", + "displayName": "In Pangenome", + "dataType": "boolean", + "categories": [ + "pangenome" + ], + "sortable": true, + "filterable": true, + "align": "center", + "width": "120px", + "transform": { + "type": "boolean", + "options": { + "trueIcon": "bi-check-circle-fill", + "falseIcon": "bi-x-circle", + "trueColor": "#22c55e", + "falseColor": "#94a3b8" + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/app/configs/fallback_registry.py b/app/configs/fallback_registry.py new file mode 100644 index 0000000..973fc83 --- /dev/null +++ b/app/configs/fallback_registry.py @@ -0,0 +1,203 @@ +""" +Fallback Config Registry. + +Maps KBase object types to built-in configuration files. +Used when AI generation fails or for known object types. +""" + +from __future__ import annotations + +import json +import logging +import re +from pathlib import Path +from typing import Any + +logger = logging.getLogger(__name__) + +# Directory containing built-in config files +CONFIG_DIR = Path(__file__).parent + +# Object type patterns mapped to config file names +# Supports wildcards like "KBaseFBA.GenomeDataLakeTables-*" +FALLBACK_CONFIG_PATTERNS: dict[str, str] = { + # BERDL/Pangenome tables + r"KBaseGeneDataLakes\.BERDLTables.*": "berdl_tables.json", + r"KBaseGeneDataLakes\.PangenomeTables.*": "berdl_tables.json", + + # Genome data tables + r"KBaseFBA\.GenomeDataLakeTables.*": "genome_data_tables.json", + r"KBase\.GenomeDataTables.*": "genome_data_tables.json", + + # Legacy patterns + r".*BERDLTables.*": "berdl_tables.json", + r".*GenomeDataTables.*": "genome_data_tables.json", +} + +# Pre-compiled patterns for performance +_COMPILED_PATTERNS: list[tuple[re.Pattern, str]] = [ + (re.compile(pattern), filename) + for pattern, filename in FALLBACK_CONFIG_PATTERNS.items() +] + +# Cache loaded configs +_CONFIG_CACHE: dict[str, dict] = {} + + +def get_fallback_config(object_type: str | None) -> dict[str, Any] | None: + """ + Get a built-in fallback config for the given object type. + + Args: + object_type: KBase object type string (e.g., "KBaseGeneDataLakes.BERDLTables-1.0") + + Returns: + Config dictionary if a fallback exists, None otherwise + """ + if not object_type: + return None + + # Try to match against patterns + for pattern, filename in _COMPILED_PATTERNS: + if pattern.match(object_type): + return load_config_file(filename) + + return None + + +def get_fallback_config_id(object_type: str | None) -> str | None: + """ + Get the config ID that would be used for fallback. + + Args: + object_type: KBase object type string + + Returns: + Config ID (filename without extension) if match found, None otherwise + """ + if not object_type: + return None + + for pattern, filename in _COMPILED_PATTERNS: + if pattern.match(object_type): + return filename.replace(".json", "") + + return None + + +def has_fallback_config(object_type: str | None) -> bool: + """ + Check if a fallback config exists for the object type. + + Args: + object_type: KBase object type string + + Returns: + True if fallback exists + """ + return get_fallback_config_id(object_type) is not None + + +def load_config_file(filename: str) -> dict[str, Any] | None: + """ + Load a config file from the configs directory. + + Args: + filename: Name of the config file (e.g., "berdl_tables.json") + + Returns: + Parsed config dictionary, or None if not found + """ + # Check cache first + if filename in _CONFIG_CACHE: + return _CONFIG_CACHE[filename] + + config_path = CONFIG_DIR / filename + + if not config_path.exists(): + logger.warning(f"Fallback config not found: {config_path}") + return None + + try: + with open(config_path, "r", encoding="utf-8") as f: + config = json.load(f) + + # Cache for future use + _CONFIG_CACHE[filename] = config + logger.debug(f"Loaded fallback config: {filename}") + return config + + except json.JSONDecodeError as e: + logger.error(f"Invalid JSON in fallback config {filename}: {e}") + return None + except Exception as e: + logger.error(f"Error loading fallback config {filename}: {e}") + return None + + +def list_available_configs() -> list[dict[str, Any]]: + """ + List all available built-in configs. + + Returns: + List of config info dictionaries + """ + configs = [] + + for json_file in CONFIG_DIR.glob("*.json"): + try: + config = load_config_file(json_file.name) + if config: + configs.append({ + "filename": json_file.name, + "id": config.get("id", json_file.stem), + "name": config.get("name", json_file.stem), + "version": config.get("version", "1.0.0"), + "tables": list(config.get("tables", {}).keys()), + }) + except Exception as e: + logger.warning(f"Error reading config {json_file}: {e}") + + return configs + + +def get_config_for_tables(table_names: list[str]) -> dict[str, Any] | None: + """ + Try to find a fallback config that matches the given table names. + + Args: + table_names: List of table names in the database + + Returns: + Best matching config or None + """ + if not table_names: + return None + + table_set = set(t.lower() for t in table_names) + best_match = None + best_score = 0 + + for json_file in CONFIG_DIR.glob("*.json"): + config = load_config_file(json_file.name) + if not config: + continue + + config_tables = set(t.lower() for t in config.get("tables", {}).keys()) + + # Calculate overlap score + intersection = len(table_set & config_tables) + if intersection > best_score: + best_score = intersection + best_match = config + + # Require at least 50% table match + if best_match and best_score >= len(table_set) * 0.5: + return best_match + + return None + + +def clear_cache() -> None: + """Clear the config cache (useful for testing).""" + _CONFIG_CACHE.clear() diff --git a/app/configs/genome_data_tables.json b/app/configs/genome_data_tables.json new file mode 100644 index 0000000..2ae7ed1 --- /dev/null +++ b/app/configs/genome_data_tables.json @@ -0,0 +1,527 @@ +{ + "id": "genome_data_tables", + "name": "Genome Data Tables", + "description": "Tables from KBase GenomeDataTables objects including genes, conditions, and experimental data", + "version": "1.0.0", + "icon": "bi-database", + "color": "#6366f1", + "defaults": { + "pageSize": 50, + "density": "default", + "showRowNumbers": true, + "enableSelection": true, + "enableExport": true + }, + "sharedCategories": [ + { + "id": "core", + "name": "Core Info", + "description": "Essential identifiers and names", + "icon": "bi-database", + "color": "#6366f1", + "defaultVisible": true, + "order": 1 + }, + { + "id": "functional", + "name": "Functional Annotation", + "description": "Function and product information", + "icon": "bi-gear", + "color": "#22c55e", + "defaultVisible": true, + "order": 2 + }, + { + "id": "external", + "name": "External Links", + "description": "Links to external databases", + "icon": "bi-box-arrow-up-right", + "color": "#06b6d4", + "defaultVisible": true, + "order": 3 + }, + { + "id": "sequence", + "name": "Sequence Data", + "description": "DNA, RNA, and protein sequences", + "icon": "bi-text-left", + "color": "#f59e0b", + "defaultVisible": true, + "order": 4 + }, + { + "id": "metadata", + "name": "System Metadata", + "description": "System tags, hashes, and sync info", + "icon": "bi-info-circle", + "color": "#64748b", + "defaultVisible": false, + "order": 10 + }, + { + "id": "status", + "name": "Status & Reports", + "description": "Error reports and validity status", + "icon": "bi-activity", + "color": "#ef4444", + "defaultVisible": true, + "order": 9 + } + ], + "tables": { + "Genes": { + "displayName": "Genes", + "description": "Gene annotations from the genome", + "icon": "bi-diagram-3", + "settings": { + "defaultSortColumn": "ID", + "defaultSortOrder": "asc" + }, + "columns": [ + { + "column": "ID", + "displayName": "Gene ID", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "copyable": true, + "width": "120px", + "pin": "left" + }, + { + "column": "Database_ID", + "displayName": "DB Reference", + "dataType": "id", + "categories": [ + "core" + ], + "sortable": true, + "filterable": true, + "width": "130px" + }, + { + "column": "Primary_function", + "displayName": "Product / Function", + "dataType": "string", + "categories": [ + "functional" + ], + "sortable": true, + "filterable": true, + "searchable": true, + "width": "300px" + }, + { + "column": "Uniprot_ID", + "displayName": "UniProt", + "dataType": "id", + "categories": [ + "external" + ], + "sortable": true, + "filterable": true, + "width": "100px", + "transform": { + "type": "link", + "options": { + "urlTemplate": "https://www.uniprot.org/uniprotkb/{value}", + "target": "_blank", + "icon": "bi-link-45deg" + } + } + }, + { + "column": "GO_Terms", + "displayName": "GO Terms", + "dataType": "ontology", + "categories": [ + "functional" + ], + "sortable": false, + "filterable": true, + "width": "180px", + "transform": { + "type": "ontology", + "options": { + "prefix": "GO", + "urlTemplate": "https://amigo.geneontology.org/amigo/term/{value}", + "style": "badge" + } + } + }, + { + "column": "KEGG_ID", + "displayName": "KEGG", + "dataType": "id", + "categories": [ + "external" + ], + "sortable": true, + "width": "90px", + "transform": { + "type": "link", + "options": { + "urlTemplate": "https://www.genome.jp/entry/{value}", + "target": "_blank" + } + } + }, + { + "column": "Sequence", + "displayName": "Protein Sequence", + "dataType": "sequence", + "categories": [ + "sequence" + ], + "sortable": false, + "filterable": false, + "copyable": true, + "width": "150px", + "transform": { + "type": "sequence", + "options": { + "maxLength": 20, + "showCopyButton": true + } + } + } + ], + "virtualColumns": [ + { + "column": "Gene_Info", + "displayName": "Gene Summary", + "virtual": true, + "sourceColumns": [ + "ID", + "Primary_function" + ], + "compute": { + "type": "merge", + "template": "{ID}: {Primary_function}" + }, + "categories": [ + "core" + ], + "visible": false + } + ] + }, + "Conditions": { + "displayName": "Experimental Conditions", + "description": "Growth conditions and experimental parameters", + "icon": "bi-thermometer-half", + "categories": [ + { + "id": "experimental", + "name": "Experimental Parameters", + "icon": "bi-sliders", + "color": "#f59e0b", + "defaultVisible": true + }, + { + "id": "media", + "name": "Media Composition", + "icon": "bi-droplet", + "color": "#3b82f6", + "defaultVisible": true + } + ], + "columns": [ + { + "column": "Database_ID", + "displayName": "Condition ID", + "dataType": "id", + "categories": [ + "experimental" + ], + "sortable": true, + "filterable": true, + "copyable": true, + "width": "140px", + "pin": "left" + }, + { + "column": "Name", + "displayName": "Condition Name", + "dataType": "string", + "categories": [ + "experimental" + ], + "sortable": true, + "filterable": true, + "searchable": true, + "width": "200px" + }, + { + "column": "Temperature_in_C", + "displayName": "Temperature (°C)", + "dataType": "number", + "categories": [ + "experimental" + ], + "sortable": true, + "filterable": true, + "align": "right", + "width": "120px", + "transform": { + "type": "number", + "options": { + "decimals": 1, + "suffix": "°C" + } + } + }, + { + "column": "Agitation_Speed_in_RPM", + "displayName": "Agitation (RPM)", + "dataType": "integer", + "categories": [ + "experimental" + ], + "sortable": true, + "filterable": true, + "align": "right", + "width": "120px", + "transform": { + "type": "number", + "options": { + "decimals": 0, + "suffix": " RPM" + } + } + }, + { + "column": "Min_Media", + "displayName": "Base Media", + "dataType": "string", + "categories": [ + "media" + ], + "sortable": true, + "filterable": true, + "width": "120px" + }, + { + "column": "Carbon_Source", + "displayName": "Carbon Source", + "dataType": "string", + "categories": [ + "media" + ], + "sortable": true, + "filterable": true, + "width": "140px", + "transform": { + "type": "badge", + "options": { + "color": "#22c55e", + "variant": "subtle" + } + } + }, + { + "column": "Carbon_Concentration_in_mM", + "displayName": "Carbon (mM)", + "dataType": "float", + "categories": [ + "media" + ], + "sortable": true, + "filterable": true, + "align": "right", + "width": "100px", + "transform": { + "type": "number", + "options": { + "decimals": 2, + "suffix": " mM" + } + } + }, + { + "column": "Antibiotics", + "displayName": "Antibiotics", + "dataType": "string", + "categories": [ + "media" + ], + "sortable": true, + "filterable": true, + "width": "120px" + }, + { + "column": "AB_Concentration_in_mg_mL", + "displayName": "AB Conc. (mg/mL)", + "dataType": "float", + "categories": [ + "media" + ], + "sortable": true, + "filterable": true, + "align": "right", + "width": "120px", + "transform": { + "type": "number", + "options": { + "decimals": 3, + "suffix": " mg/mL" + } + } + }, + { + "column": "Supplements", + "displayName": "Supplements", + "dataType": "string", + "categories": [ + "media" + ], + "sortable": true, + "filterable": true, + "width": "150px" + } + ] + }, + "Expression": { + "displayName": "Expression Data", + "description": "Gene expression measurements", + "icon": "bi-graph-up", + "settings": { + "enableSelection": true, + "density": "compact" + }, + "categories": [ + { + "id": "gene", + "name": "Gene Info", + "icon": "bi-database", + "color": "#6366f1", + "defaultVisible": true + }, + { + "id": "expression", + "name": "Expression Values", + "icon": "bi-graph-up", + "color": "#ef4444", + "defaultVisible": true + }, + { + "id": "statistics", + "name": "Statistics", + "icon": "bi-calculator", + "color": "#8b5cf6", + "defaultVisible": true + } + ], + "columns": [ + { + "column": "Gene_ID", + "displayName": "Gene ID", + "dataType": "id", + "categories": [ + "gene" + ], + "sortable": true, + "filterable": true, + "copyable": true, + "width": "120px", + "pin": "left" + }, + { + "column": "Gene_Name", + "displayName": "Gene Name", + "dataType": "string", + "categories": [ + "gene" + ], + "sortable": true, + "filterable": true, + "width": "120px" + }, + { + "column": "Log2FC", + "displayName": "Log2 Fold Change", + "dataType": "float", + "categories": [ + "expression" + ], + "sortable": true, + "filterable": true, + "align": "right", + "width": "130px", + "transform": { + "type": "heatmap", + "options": { + "min": -4, + "max": 4, + "colorScale": "diverging", + "showValue": true, + "decimals": 2 + } + } + }, + { + "column": "P_Value", + "displayName": "P-Value", + "dataType": "float", + "categories": [ + "statistics" + ], + "sortable": true, + "filterable": true, + "align": "right", + "width": "100px", + "transform": { + "type": "number", + "options": { + "notation": "scientific", + "decimals": 2 + } + } + }, + { + "column": "FDR", + "displayName": "FDR", + "dataType": "float", + "categories": [ + "statistics" + ], + "sortable": true, + "filterable": true, + "align": "right", + "width": "100px", + "transform": { + "type": "number", + "options": { + "notation": "scientific", + "decimals": 2 + } + } + }, + { + "column": "Significant", + "displayName": "Significant", + "dataType": "boolean", + "categories": [ + "statistics" + ], + "sortable": true, + "filterable": true, + "align": "center", + "width": "90px", + "transform": { + "type": "boolean", + "options": { + "trueIcon": "bi-check-circle-fill", + "falseIcon": "bi-x-circle", + "trueColor": "#22c55e", + "falseColor": "#94a3b8" + } + } + } + ] + } + } +} \ No newline at end of file diff --git a/app/models.py b/app/models.py index 9a086eb..29925b7 100644 --- a/app/models.py +++ b/app/models.py @@ -119,6 +119,54 @@ class TableListResponse(BaseModel): ]] ) source: str | None = Field(None, description="Data source", examples=["Cache"]) + + # Viewer integration fields + config_fingerprint: str | None = Field( + None, + description="Fingerprint of cached viewer config (if exists)", + examples=["v1_auto_abc123def456"] + ) + config_url: str | None = Field( + None, + description="URL to retrieve generated viewer config", + examples=["/config/generated/v1_auto_abc123def456"] + ) + has_cached_config: bool = Field( + False, + description="Whether a viewer config is cached for this database" + ) + + # Schema information for immediate viewer use + schemas: dict | None = Field( + None, + description="Column types per table: {table_name: {column: sql_type}}" + ) + + # Fallback config availability + has_builtin_config: bool = Field( + False, + description="Whether a built-in fallback config exists for this object type" + ) + builtin_config_id: str | None = Field( + None, + description="ID of the matching built-in config" + ) + + # Database metadata + database_size_bytes: int | None = Field( + None, + description="Size of the SQLite database file in bytes" + ) + total_rows: int = Field( + 0, + description="Total rows across all tables" + ) + + # Versioning for backward compatibility + api_version: str = Field( + "2.0", + description="API version for response format compatibility" + ) class PangenomeInfo(BaseModel): @@ -330,15 +378,60 @@ class ColumnInferenceResponse(BaseModel): class ConfigGenerationResponse(BaseModel): """Response from config generation endpoint.""" - status: Literal["generated", "cached", "error"] = Field(..., description="Generation status") + # Core fields + status: Literal["generated", "cached", "fallback", "error"] = Field( + ..., + description="Generation status: generated (new), cached (from cache), fallback (builtin), error" + ) fingerprint: str = Field(..., description="Database fingerprint for caching") config_url: str = Field(..., description="URL to retrieve generated config") config: dict = Field(..., description="Full DataTypeConfig JSON") + + # Fallback metadata + fallback_used: bool = Field( + False, + description="Whether a fallback config was used instead of AI generation" + ) + fallback_reason: str | None = Field( + None, + description="Reason for fallback: ai_unavailable, generation_failed, object_type_matched" + ) + config_source: Literal["ai", "rules", "cache", "builtin", "error"] = Field( + "rules", + description="Source of the configuration" + ) + + # Schema information (viewer can use directly) + db_schema: dict | None = Field( + None, + alias="schema", + description="Simple schema: {table_name: {column: type}}" + ) + table_schemas: dict | None = Field( + None, + description="Full PRAGMA table_info per table" + ) + + # Statistics tables_analyzed: int = Field(..., description="Number of tables analyzed") columns_inferred: int = Field(..., description="Number of columns inferred") + total_rows: int = Field(0, description="Total rows across all tables") + + # AI provider info ai_provider_used: str | None = Field(None, description="AI provider that was used") + ai_available: bool = Field(True, description="Whether AI was available") + ai_error: str | None = Field(None, description="Error message if AI failed") + + # Performance generation_time_ms: float = Field(..., description="Time to generate config in ms") cache_hit: bool = Field(..., description="Whether config was from cache") + + # Object metadata + object_type: str | None = Field(None, description="KBase object type") + object_ref: str | None = Field(None, description="Object reference (ws/obj/ver)") + + # Versioning + api_version: str = Field("2.0", description="API version for compatibility") class ProviderStatusResponse(BaseModel): diff --git a/app/routes.py b/app/routes.py index fec40a9..12babea 100644 --- a/app/routes.py +++ b/app/routes.py @@ -358,10 +358,12 @@ async def list_tables_by_object( ): """ List tables for a BERDLTables object. + + Returns table list along with viewer config info (fingerprint/URL if cached). **Example:** ```bash - curl -H "Authorization: $KB_TOKEN" \ + curl -H "Authorization: $KB_TOKEN" \\ "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables" ``` """ @@ -379,6 +381,9 @@ async def list_tables_by_object( table_names = list_tables(db_path) tables = [] + schemas = {} + total_rows = 0 + for name in table_names: try: columns = get_table_columns(db_path, name) @@ -388,6 +393,10 @@ async def list_tables_by_object( "row_count": row_count, "column_count": len(columns) }) + total_rows += row_count or 0 + + # Build schema map + schemas[name] = {col: "TEXT" for col in columns} # Default type except Exception as e: logger.warning("Error getting table info for %s", name, exc_info=True) tables.append({"name": name}) @@ -398,11 +407,55 @@ async def list_tables_by_object( except Exception: object_type = None + # Check for cached viewer config + config_fingerprint = None + config_url = None + has_cached_config = False + try: + from app.services.fingerprint import DatabaseFingerprint + fp_service = DatabaseFingerprint() + safe_ref = berdl_table_id.replace("/", "_").replace(":", "_") + fingerprint = fp_service.compute(db_path) + full_fingerprint = f"{safe_ref}_{fingerprint}" + + if fp_service.is_cached(full_fingerprint): + config_fingerprint = full_fingerprint + config_url = f"/config/generated/{full_fingerprint}" + has_cached_config = True + except Exception as e: + logger.debug(f"Config fingerprint check: {e}") + + # Check for builtin fallback config + has_builtin_config = False + builtin_config_id = None + try: + from app.configs import has_fallback_config, get_fallback_config_id + has_builtin_config = has_fallback_config(object_type) + builtin_config_id = get_fallback_config_id(object_type) + except Exception as e: + logger.debug(f"Fallback config check: {e}") + + # Get database size + database_size = None + try: + database_size = db_path.stat().st_size if db_path.exists() else None + except Exception: + pass + return { "berdl_table_id": berdl_table_id, "tables": tables, "object_type": object_type, - "source": "Cache" if (db_path.exists() and db_path.stat().st_size > 0) else "Downloaded" + "source": "Cache" if (db_path.exists() and db_path.stat().st_size > 0) else "Downloaded", + "config_fingerprint": config_fingerprint, + "config_url": config_url, + "has_cached_config": has_cached_config, + "schemas": schemas, + "has_builtin_config": has_builtin_config, + "builtin_config_id": builtin_config_id, + "database_size_bytes": database_size, + "total_rows": total_rows, + "api_version": "2.0", } except Exception as e: @@ -854,3 +907,254 @@ async def list_cached_configs(): logger.error(f"Error listing cached configs: {e}") raise HTTPException(status_code=500, detail=str(e)) + +@router.delete("/config/cached/{fingerprint}", tags=["Config Generation"]) +async def delete_cached_config(fingerprint: str): + """ + Delete a specific cached configuration. + + Use this to invalidate a cached config and force regeneration on next request. + + **Example:** + ```bash + curl -X DELETE "http://127.0.0.1:8000/config/cached/76990_7_2_abc123" + ``` + """ + try: + from app.services.fingerprint import DatabaseFingerprint + + fp = DatabaseFingerprint() + deleted = fp.clear_cache(fingerprint) + + if deleted > 0: + return {"status": "success", "message": f"Deleted config: {fingerprint}"} + else: + raise HTTPException(status_code=404, detail=f"Config not found: {fingerprint}") + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error deleting config: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@router.post( + "/object/{ws_ref:path}/config/generate", + response_model=ConfigGenerationResponse, + tags=["Config Generation"] +) +async def generate_config_for_object( + ws_ref: str, + force_regenerate: bool = Query(False, description="Skip cache and regenerate"), + kb_env: str = Query("appdev"), + authorization: str | None = Header(None) +): + """ + Generate a DataTables_Viewer configuration for a KBase object. + + This is the object-based alternative to the handle-based config generation. + It downloads the SQLite database from the workspace object and generates + an AI-powered viewer configuration. + + **Flow:** + 1. Download SQLite from workspace object (uses existing cache) + 2. Compute database fingerprint + 3. Check config cache → return if exists (unless force_regenerate) + 4. Analyze schema and sample values + 5. Apply rule-based + Argo AI type inference + 6. Validate and cache generated config + 7. Return + + **Example:** + ```bash + curl -X POST -H "Authorization: $KB_TOKEN" \\ + "http://127.0.0.1:8000/object/76990/7/2/config/generate" + ``` + """ + try: + token = get_auth_token(authorization) + cache_dir = get_cache_dir() + berdl_table_id = ws_ref + + # Download database + db_path = download_pangenome_db( + berdl_table_id=berdl_table_id, + auth_token=token, + cache_dir=cache_dir, + kb_env=kb_env + ) + + # Get object type for config metadata + try: + object_type = get_object_type(berdl_table_id, token, kb_env) + except Exception: + object_type = None + + # Generate config + from app.services.config_generator import ConfigGenerator + + generator = ConfigGenerator() + + # Build schema info for response + schema = {} + table_schemas = {} + total_rows = 0 + try: + table_names = list_tables(db_path) + for tbl in table_names: + cols = get_table_columns(db_path, tbl) + schema[tbl] = {c: "TEXT" for c in cols} + total_rows += get_table_row_count(db_path, tbl) or 0 + except Exception as e: + logger.warning(f"Error building schema: {e}") + + # Try AI generation with fallback cascade + fallback_used = False + fallback_reason = None + config_source = "rules" + ai_error = None + ai_available = True + + try: + result = generator.generate( + db_path=db_path, + handle_ref=berdl_table_id, + force_regenerate=force_regenerate, + ai_preference="argo", # Argo-only strategy + ) + config_source = "ai" if result.ai_provider_used else "rules" + + except Exception as gen_error: + logger.warning(f"Config generation failed, trying fallback: {gen_error}") + ai_error = str(gen_error) + ai_available = False + + # Try builtin fallback + from app.configs import get_fallback_config, get_fallback_config_id + fallback_config = get_fallback_config(object_type) + + if fallback_config: + fallback_used = True + fallback_reason = "generation_failed" + config_source = "builtin" + + # Create mock result + from dataclasses import dataclass + @dataclass + class MockResult: + config: dict + fingerprint: str + cache_hit: bool = False + tables_analyzed: int = 0 + columns_inferred: int = 0 + ai_provider_used: str | None = None + generation_time_ms: float = 0.0 + + safe_ref = berdl_table_id.replace("/", "_").replace(":", "_") + result = MockResult( + config=fallback_config, + fingerprint=f"{safe_ref}_fallback_{get_fallback_config_id(object_type)}", + ) + else: + # No fallback available - return error + raise HTTPException( + status_code=500, + detail=f"Config generation failed and no fallback available: {gen_error}" + ) + + # Add object type to config if available + if object_type and "objectType" not in result.config: + result.config["objectType"] = object_type + + # Determine status + if fallback_used: + status = "fallback" + elif result.cache_hit: + status = "cached" + else: + status = "generated" + + return ConfigGenerationResponse( + status=status, + fingerprint=result.fingerprint, + config_url=f"/config/generated/{result.fingerprint}", + config=result.config, + fallback_used=fallback_used, + fallback_reason=fallback_reason, + config_source=config_source, + db_schema=schema if schema else None, + table_schemas=table_schemas if table_schemas else None, + tables_analyzed=result.tables_analyzed, + columns_inferred=result.columns_inferred, + total_rows=total_rows, + ai_provider_used=result.ai_provider_used, + ai_available=ai_available, + ai_error=ai_error, + generation_time_ms=result.generation_time_ms, + cache_hit=result.cache_hit, + object_type=object_type, + object_ref=berdl_table_id, + api_version="2.0", + ) + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error generating config for object: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@router.get("/object/{ws_ref:path}/config", tags=["Config Generation"]) +async def get_config_for_object( + ws_ref: str, + kb_env: str = Query("appdev"), + authorization: str | None = Header(None) +): + """ + Get cached viewer config for a KBase object. + + Returns 404 if no config has been generated yet. Use the + POST /object/{ws_ref}/config/generate endpoint to create one. + + **Example:** + ```bash + curl -H "Authorization: $KB_TOKEN" \\ + "http://127.0.0.1:8000/object/76990/7/2/config" + ``` + """ + try: + token = get_auth_token(authorization) + cache_dir = get_cache_dir() + berdl_table_id = ws_ref + + # Download database (needed for fingerprint computation) + db_path = download_pangenome_db( + berdl_table_id=berdl_table_id, + auth_token=token, + cache_dir=cache_dir, + kb_env=kb_env + ) + + # Compute fingerprint and check cache + from app.services.fingerprint import DatabaseFingerprint + + fp_service = DatabaseFingerprint() + safe_ref = berdl_table_id.replace("/", "_").replace(":", "_") + schema_fp = fp_service.compute(db_path) + fingerprint = f"{safe_ref}_{schema_fp}" + + config = fp_service.get_cached_config(fingerprint) + + if config is None: + raise HTTPException( + status_code=404, + detail=f"No cached config for object {ws_ref}. Use POST /object/{ws_ref}/config/generate to create one." + ) + + return config + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error getting config for object: {e}") + raise HTTPException(status_code=500, detail=str(e)) diff --git a/app/services/__init__.py b/app/services/__init__.py index 08120f6..2b0fb1e 100644 --- a/app/services/__init__.py +++ b/app/services/__init__.py @@ -23,6 +23,8 @@ ) from .fingerprint import DatabaseFingerprint from .config_generator import ConfigGenerator, GenerationResult +from .prompts import build_table_config_prompt, detect_value_patterns, compute_numeric_stats +from .validation import validate_config, validate_table_config, validate_ai_response, sanitize_config __all__ = [ # Type inference @@ -45,4 +47,13 @@ # Config generation "ConfigGenerator", "GenerationResult", + # Prompts + "build_table_config_prompt", + "detect_value_patterns", + "compute_numeric_stats", + # Validation + "validate_config", + "validate_table_config", + "validate_ai_response", + "sanitize_config", ] diff --git a/app/services/prompts.py b/app/services/prompts.py new file mode 100644 index 0000000..607d78c --- /dev/null +++ b/app/services/prompts.py @@ -0,0 +1,340 @@ +""" +Prompt Engineering Module for AI-Powered Config Generation. + +Provides structured prompts for Argo AI to analyze pre-computed schema data +and generate DataTables Viewer configurations. Argo cannot execute SQL +commands, so all analysis must be pre-computed before prompt generation. +""" + +from __future__ import annotations + +import json +from typing import Any + + +# ============================================================================= +# SYSTEM PROMPT - Argo-Optimized +# ============================================================================= + +SYSTEM_PROMPT = """You are a Senior Bioinformatics Data Engineer creating DataTables Viewer configurations. + +## Your Role +Analyze the PRE-COMPUTED schema analysis and sample values provided below to generate +optimal column configurations for scientific data visualization. You CANNOT run SQL +commands - all data analysis has been pre-computed and provided to you. + +## Column Configuration Rules + +### Data Type Detection (analyze provided samples) +| Pattern | data_type | Transform | +|---------|-----------|-----------| +| UniRef IDs with prefix (e.g., "UniRef:UniRef90_...") | id | chain: replace prefix → link | +| GO terms (GO:0008150) | ontology | ontology with AmiGO URL | +| KEGG IDs (K00001, ko:K00001) | id | link to KEGG | +| Pfam IDs (PF00001, pfam:PF00001) | id | link to InterPro | +| NCBI IDs (numeric or NP_/WP_) | id | link to NCBI | +| DNA sequences (20+ chars of ATCG) | sequence | sequence transformer | +| Protein sequences (20+ amino acids) | sequence | sequence transformer | +| URLs (http://...) | url | link transformer | +| Email addresses | email | null | +| Numeric with high precision | float | number with decimals | +| Integer values | integer | null or number | +| +/- or strand indicators | string | badge with color mapping | +| Boolean (true/false, yes/no, 1/0) | boolean | boolean transformer | +| P-values, FDR (scientific notation) | float | number with scientific notation | +| Log2 fold change | float | heatmap (diverging, min:-4 max:4) | + +### Category Assignment Rules +| Column Pattern | Category | +|----------------|----------| +| Primary ID column (usually first) | core | +| Names (gene_name, organism, etc.) | core | +| Products, functions, descriptions | functional | +| UniRef, UniProt, NCBI, KEGG refs | external | +| GO, Pfam, COG annotations | ontology | +| DNA/AA sequence columns | sequence | +| Scores, p-values, fold changes | statistics | +| Coordinates (start, end, strand) | core | +| System columns, timestamps | metadata | + +### Width Guidelines +| Type | Width | +|------|-------| +| ID columns | 100-140px | +| Short strings | 120-180px | +| Long text (descriptions) | 250-400px | +| Numbers | 80-120px | +| Sequences | 150px | +| Boolean | 80px | + +### Essential Transform Examples + +**UniRef with prefix stripping:** +```json +{"type": "chain", "options": {"transforms": [ + {"type": "replace", "options": {"find": "UniRef:", "replace": ""}}, + {"type": "link", "options": {"urlTemplate": "https://www.uniprot.org/uniref/{value}", "icon": "bi-link-45deg"}} +]}} +``` + +**GO term ontology:** +```json +{"type": "ontology", "options": {"prefix": "GO", "urlTemplate": "https://amigo.geneontology.org/amigo/term/{value}", "style": "badge"}} +``` + +**KEGG ID link:** +```json +{"type": "link", "options": {"urlTemplate": "https://www.genome.jp/entry/{value}", "target": "_blank"}} +``` + +**Strand badge:** +```json +{"type": "badge", "options": {"colorMap": {"+": {"color": "#22c55e", "bgColor": "#dcfce7"}, "-": {"color": "#ef4444", "bgColor": "#fee2e2"}}}} +``` + +**Heatmap for fold change:** +```json +{"type": "heatmap", "options": {"min": -4, "max": 4, "colorScale": "diverging", "showValue": true, "decimals": 2}} +``` + +**Scientific notation for p-values:** +```json +{"type": "number", "options": {"notation": "scientific", "decimals": 2}} +``` + +## Output Format +Return ONLY valid JSON with this exact structure. No markdown, no explanation. + +```json +{ + "columns": [ + { + "column": "exact_sql_column_name", + "displayName": "Human Readable Name", + "dataType": "string|number|integer|float|boolean|date|id|sequence|ontology|url", + "categories": ["core|functional|external|ontology|sequence|statistics|metadata"], + "sortable": true, + "filterable": true, + "copyable": false, + "width": "auto", + "pin": null, + "transform": null + } + ] +} +``` + +## Critical Rules +1. Column names MUST exactly match the SQL schema - case sensitive +2. Pin the primary identifier column to "left" +3. Set copyable: true for IDs and sequences +4. Right-align numeric columns (handled by viewer based on dataType) +5. Detect prefixes in sample values that need stripping (UniRef:, GO:, ko:, etc.) +6. If samples show patterns like "UniRef:UniRef90_..." always use chain transform +7. For columns with many nulls, still provide full config based on non-null samples""" + + +# ============================================================================= +# PROMPT BUILDERS +# ============================================================================= + +def build_table_config_prompt( + table_name: str, + schema_info: list[dict[str, Any]], + sample_values: dict[str, list[Any]], + detected_patterns: dict[str, list[str]], + statistics: dict[str, dict[str, Any]], + row_count: int = 0, +) -> str: + """ + Build a complete prompt for Argo to generate table configuration. + + All data must be pre-computed before calling this function since + Argo cannot execute SQL commands. + + Args: + table_name: Name of the table being configured + schema_info: Pre-computed from PRAGMA table_info (list of column defs) + sample_values: Pre-computed samples per column (10-20 non-null values each) + detected_patterns: Pre-detected patterns like prefixes, URLs, sequences + statistics: Pre-computed min/max/avg for numeric columns + row_count: Total rows in table for context + + Returns: + Complete prompt string for Argo + """ + # Format schema as readable list + schema_summary = [] + for col in schema_info: + col_info = f"- {col['name']} ({col.get('type', 'TEXT')})" + if col.get('pk'): + col_info += " [PRIMARY KEY]" + if col.get('notnull'): + col_info += " [NOT NULL]" + schema_summary.append(col_info) + + prompt = f"""{SYSTEM_PROMPT} + +--- +## Analysis Data for Table: `{table_name}` +Row Count: {row_count:,} + +### Schema Definition +{chr(10).join(schema_summary)} + +### Sample Values (10 non-null per column) +{json.dumps(sample_values, indent=2, default=str)} + +### Detected Patterns +{json.dumps(detected_patterns, indent=2)} + +### Numeric Statistics (min/max/avg) +{json.dumps(statistics, indent=2)} + +--- +## Task +Generate complete column configurations for table `{table_name}`. +Return ONLY the JSON object with "columns" array. No markdown code fences.""" + + return prompt + + +def build_multi_table_prompt( + tables: dict[str, dict[str, Any]], + database_name: str = "database", +) -> str: + """ + Build prompt for configuring multiple tables at once. + + Args: + tables: Dict mapping table names to their analysis data + database_name: Name for the overall config + + Returns: + Complete prompt for multi-table config generation + """ + tables_section = [] + + for table_name, data in tables.items(): + table_block = f""" +### Table: `{table_name}` ({data.get('row_count', 0):,} rows) + +**Schema:** +{json.dumps(data.get('schema', []), indent=2)} + +**Sample Values:** +{json.dumps(data.get('samples', {}), indent=2)} + +**Detected Patterns:** +{json.dumps(data.get('patterns', {}), indent=2)} +""" + tables_section.append(table_block) + + prompt = f"""{SYSTEM_PROMPT} + +--- +## Database: {database_name} +Tables: {', '.join(tables.keys())} + +{chr(10).join(tables_section)} + +--- +## Task +Generate configurations for ALL tables. Return JSON with this structure: +{{"tables": {{"table_name": {{"displayName": "...", "columns": [...]}}}}}} + +Return ONLY the JSON. No markdown.""" + + return prompt + + +# ============================================================================= +# PATTERN DETECTION HELPERS +# ============================================================================= + +def detect_value_patterns(values: list[Any]) -> list[str]: + """ + Detect patterns in sample values for prompt enhancement. + + Args: + values: List of sample values from a column + + Returns: + List of detected pattern descriptions + """ + import re + + patterns = [] + str_values = [str(v) for v in values if v is not None and str(v).strip()] + + if not str_values: + return ["all_null"] + + # Check for common prefixes + prefixes = { + "UniRef:": "UniRef prefix (needs stripping)", + "GO:": "GO term format", + "ko:": "KEGG orthology prefix", + "pfam:": "Pfam prefix", + "PF": "Pfam ID format", + "K0": "KEGG K number", + "http": "URL format", + "NP_": "NCBI RefSeq protein", + "WP_": "NCBI protein", + } + + for prefix, desc in prefixes.items(): + if any(v.startswith(prefix) for v in str_values[:5]): + patterns.append(desc) + + # Check for sequences + seq_pattern = re.compile(r'^[ATCGU]{20,}$', re.IGNORECASE) + protein_pattern = re.compile(r'^[ACDEFGHIKLMNPQRSTVWY]{15,}$', re.IGNORECASE) + + for v in str_values[:3]: + if seq_pattern.match(v): + patterns.append("DNA/RNA sequence") + break + if protein_pattern.match(v): + patterns.append("Protein sequence") + break + + # Check value characteristics + if all(v in ('+', '-', '.') for v in str_values): + patterns.append("Strand indicator (+/-)") + + if len(set(str_values)) <= 5 and len(str_values) > 3: + patterns.append(f"Categorical ({len(set(str_values))} unique values)") + + return patterns if patterns else ["no_special_pattern"] + + +def compute_numeric_stats(values: list[Any]) -> dict[str, Any] | None: + """ + Compute statistics for numeric columns. + + Args: + values: List of values from a column + + Returns: + Dict with min, max, avg, or None if not numeric + """ + numeric_values = [] + for v in values: + if v is None: + continue + try: + numeric_values.append(float(v)) + except (ValueError, TypeError): + return None + + if not numeric_values: + return None + + return { + "min": min(numeric_values), + "max": max(numeric_values), + "avg": sum(numeric_values) / len(numeric_values), + "count": len(numeric_values), + "has_decimals": any(v != int(v) for v in numeric_values if v == v), + } diff --git a/app/services/type_inference.py b/app/services/type_inference.py index 4b3a377..bdd97e2 100644 --- a/app/services/type_inference.py +++ b/app/services/type_inference.py @@ -92,6 +92,27 @@ class InferredType: "width": "130px", }), + # UniRef IDs - need chain transformer to strip prefix + (re.compile(r"^uniref_\d+$|^UniRef_\d+$|^uniref\d+$"), { + "data_type": DataType.ID, + "categories": ["external"], + "copyable": True, + "width": "140px", + "transform": TransformConfig( + type="chain", + options={ + "transforms": [ + {"type": "replace", "options": {"find": "UniRef:", "replace": ""}}, + {"type": "link", "options": { + "urlTemplate": "https://www.uniprot.org/uniref/{value}", + "target": "_blank", + "icon": "bi-link-45deg" + }} + ] + } + ), + }), + # External database references with link transforms (re.compile(r"^Uniprot.*|^uniprot.*|.*UniProt.*"), { "data_type": DataType.ID, @@ -132,6 +153,59 @@ class InferredType: ), }), + # Pfam domain IDs + (re.compile(r"^pfam.*|^Pfam.*|^PF\d+"), { + "data_type": DataType.ID, + "categories": ["ontology"], + "width": "100px", + "transform": TransformConfig( + type="chain", + options={ + "transforms": [ + {"type": "replace", "options": {"find": "pfam:", "replace": ""}}, + {"type": "link", "options": { + "urlTemplate": "https://www.ebi.ac.uk/interpro/entry/pfam/{value}", + "target": "_blank", + "icon": "bi-link-45deg" + }} + ] + } + ), + }), + + # NCBI protein IDs (RefSeq) + (re.compile(r"^ncbi.*|.*_ncbi.*|^NP_.*|^WP_.*|^XP_.*"), { + "data_type": DataType.ID, + "categories": ["external"], + "copyable": True, + "width": "120px", + "transform": TransformConfig( + type="link", + options={ + "urlTemplate": "https://www.ncbi.nlm.nih.gov/protein/{value}", + "target": "_blank", + "icon": "bi-link-45deg" + } + ), + }), + + # Strand indicator (+/-) + (re.compile(r"^strand$|^Strand$|.*_strand$"), { + "data_type": DataType.STRING, + "categories": ["core"], + "width": "80px", + "transform": TransformConfig( + type="badge", + options={ + "colorMap": { + "+": {"color": "#22c55e", "bgColor": "#dcfce7"}, + "-": {"color": "#ef4444", "bgColor": "#fee2e2"}, + ".": {"color": "#94a3b8", "bgColor": "#f1f5f9"} + } + } + ), + }), + # Sequences (re.compile(r".*Sequence.*|.*_seq$|.*_Seq$"), { "data_type": DataType.SEQUENCE, diff --git a/app/services/validation.py b/app/services/validation.py new file mode 100644 index 0000000..de9d9db --- /dev/null +++ b/app/services/validation.py @@ -0,0 +1,383 @@ +""" +Configuration Validation Module. + +Provides JSON schema validation for generated DataTables Viewer configurations +to ensure compatibility with the frontend viewer. +""" + +from __future__ import annotations + +import logging +from typing import Any + +logger = logging.getLogger(__name__) + + +# ============================================================================= +# JSON SCHEMAS +# ============================================================================= + +# Schema for individual column configuration +COLUMN_SCHEMA = { + "type": "object", + "required": ["column", "displayName"], + "properties": { + "column": {"type": "string", "minLength": 1}, + "displayName": {"type": "string", "minLength": 1}, + "dataType": { + "type": "string", + "enum": [ + "string", "number", "integer", "float", "boolean", + "date", "datetime", "timestamp", "duration", + "id", "url", "email", "phone", + "percentage", "currency", "filesize", + "sequence", "ontology", "json", "array" + ] + }, + "visible": {"type": "boolean"}, + "sortable": {"type": "boolean"}, + "filterable": {"type": "boolean"}, + "searchable": {"type": "boolean"}, + "copyable": {"type": "boolean"}, + "width": {"type": "string"}, + "align": {"type": "string", "enum": ["left", "center", "right"]}, + "pin": {"type": ["string", "null"], "enum": ["left", "right", None]}, + "categories": { + "type": "array", + "items": {"type": "string"} + }, + "transform": { + "type": ["object", "null"], + "properties": { + "type": {"type": "string"}, + "options": {"type": "object"} + } + } + }, + "additionalProperties": True # Allow future extensions +} + +# Schema for table configuration +TABLE_SCHEMA = { + "type": "object", + "required": ["displayName", "columns"], + "properties": { + "displayName": {"type": "string", "minLength": 1}, + "description": {"type": "string"}, + "icon": {"type": "string"}, + "settings": {"type": "object"}, + "categories": { + "type": "array", + "items": {"type": "object"} + }, + "columns": { + "type": "array", + "items": COLUMN_SCHEMA, + "minItems": 1 + } + } +} + +# Schema for complete DataTypeConfig +DATATYPE_CONFIG_SCHEMA = { + "type": "object", + "required": ["id", "name", "tables"], + "properties": { + "id": {"type": "string", "minLength": 1}, + "name": {"type": "string", "minLength": 1}, + "description": {"type": "string"}, + "version": {"type": "string", "pattern": r"^\d+\.\d+\.\d+$"}, + "icon": {"type": "string"}, + "color": {"type": "string"}, + "objectType": {"type": "string"}, + "defaults": { + "type": "object", + "properties": { + "pageSize": {"type": "integer", "minimum": 1, "maximum": 1000}, + "density": {"type": "string", "enum": ["compact", "default", "comfortable"]}, + "showRowNumbers": {"type": "boolean"}, + "enableSelection": {"type": "boolean"}, + "enableExport": {"type": "boolean"} + } + }, + "sharedCategories": { + "type": "array", + "items": { + "type": "object", + "required": ["id", "name"], + "properties": { + "id": {"type": "string"}, + "name": {"type": "string"}, + "icon": {"type": "string"}, + "color": {"type": "string"}, + "defaultVisible": {"type": "boolean"}, + "order": {"type": "integer"} + } + } + }, + "tables": { + "type": "object", + "additionalProperties": TABLE_SCHEMA, + "minProperties": 1 + } + } +} + +# Schema for AI-generated column response (single table) +AI_RESPONSE_SCHEMA = { + "type": "object", + "required": ["columns"], + "properties": { + "columns": { + "type": "array", + "items": COLUMN_SCHEMA, + "minItems": 1 + } + } +} + + +# ============================================================================= +# VALIDATION FUNCTIONS +# ============================================================================= + +def validate_config(config: dict[str, Any]) -> tuple[bool, str | None]: + """ + Validate a complete DataTypeConfig against the schema. + + Args: + config: The configuration dictionary to validate + + Returns: + Tuple of (is_valid, error_message) + """ + try: + from jsonschema import validate, ValidationError, Draft7Validator + + validator = Draft7Validator(DATATYPE_CONFIG_SCHEMA) + errors = list(validator.iter_errors(config)) + + if not errors: + return True, None + + # Format first error + first_error = errors[0] + path = ".".join(str(p) for p in first_error.absolute_path) or "root" + return False, f"Validation error at '{path}': {first_error.message}" + + except ImportError: + # jsonschema not available, do basic validation + return _basic_validation(config) + except Exception as e: + logger.warning(f"Validation error: {e}") + return False, str(e) + + +def validate_table_config(table_config: dict[str, Any]) -> tuple[bool, str | None]: + """ + Validate a single table configuration. + + Args: + table_config: Table configuration dictionary + + Returns: + Tuple of (is_valid, error_message) + """ + try: + from jsonschema import validate, ValidationError + + validate(instance=table_config, schema=TABLE_SCHEMA) + return True, None + + except ImportError: + return _basic_table_validation(table_config) + except Exception as e: + return False, str(e) + + +def validate_ai_response(response: dict[str, Any]) -> tuple[bool, str | None]: + """ + Validate AI-generated column response. + + Args: + response: AI response dictionary + + Returns: + Tuple of (is_valid, error_message) + """ + try: + from jsonschema import validate + + validate(instance=response, schema=AI_RESPONSE_SCHEMA) + return True, None + + except ImportError: + # Basic validation + if not isinstance(response, dict): + return False, "Response must be a dictionary" + if "columns" not in response: + return False, "Response must have 'columns' key" + if not isinstance(response["columns"], list): + return False, "'columns' must be an array" + if len(response["columns"]) == 0: + return False, "'columns' array must not be empty" + return True, None + + except Exception as e: + return False, str(e) + + +def validate_column_config(column: dict[str, Any]) -> tuple[bool, str | None]: + """ + Validate a single column configuration. + + Args: + column: Column configuration dictionary + + Returns: + Tuple of (is_valid, error_message) + """ + if not isinstance(column, dict): + return False, "Column must be a dictionary" + + if "column" not in column: + return False, "Column must have 'column' key" + + if "displayName" not in column: + return False, "Column must have 'displayName' key" + + # Validate transform structure if present + if "transform" in column and column["transform"] is not None: + transform = column["transform"] + if not isinstance(transform, dict): + return False, "Transform must be a dictionary" + if "type" not in transform: + return False, "Transform must have 'type' key" + + return True, None + + +# ============================================================================= +# BASIC VALIDATION (fallback when jsonschema unavailable) +# ============================================================================= + +def _basic_validation(config: dict[str, Any]) -> tuple[bool, str | None]: + """Basic validation without jsonschema library.""" + if not isinstance(config, dict): + return False, "Config must be a dictionary" + + # Check required fields + for field in ["id", "name", "tables"]: + if field not in config: + return False, f"Missing required field: {field}" + + if not isinstance(config["tables"], dict): + return False, "'tables' must be a dictionary" + + if len(config["tables"]) == 0: + return False, "'tables' must not be empty" + + # Validate each table + for table_name, table_config in config["tables"].items(): + is_valid, error = _basic_table_validation(table_config) + if not is_valid: + return False, f"Table '{table_name}': {error}" + + return True, None + + +def _basic_table_validation(table_config: dict[str, Any]) -> tuple[bool, str | None]: + """Basic table validation without jsonschema library.""" + if not isinstance(table_config, dict): + return False, "Table config must be a dictionary" + + if "displayName" not in table_config: + return False, "Missing 'displayName'" + + if "columns" not in table_config: + return False, "Missing 'columns'" + + if not isinstance(table_config["columns"], list): + return False, "'columns' must be an array" + + if len(table_config["columns"]) == 0: + return False, "'columns' must not be empty" + + # Validate each column + for i, column in enumerate(table_config["columns"]): + is_valid, error = validate_column_config(column) + if not is_valid: + return False, f"Column {i}: {error}" + + return True, None + + +# ============================================================================= +# SANITIZATION +# ============================================================================= + +def sanitize_config(config: dict[str, Any]) -> dict[str, Any]: + """ + Sanitize and normalize a config, fixing common issues. + + Args: + config: Raw configuration dictionary + + Returns: + Sanitized configuration + """ + sanitized = dict(config) + + # Ensure version format + if "version" not in sanitized or not sanitized["version"]: + sanitized["version"] = "1.0.0" + + # Normalize tables + if "tables" in sanitized: + for table_name, table_config in sanitized["tables"].items(): + sanitized["tables"][table_name] = _sanitize_table(table_config) + + return sanitized + + +def _sanitize_table(table_config: dict[str, Any]) -> dict[str, Any]: + """Sanitize a table configuration.""" + sanitized = dict(table_config) + + # Ensure columns exist + if "columns" not in sanitized: + sanitized["columns"] = [] + + # Sanitize each column + sanitized["columns"] = [ + _sanitize_column(col) for col in sanitized["columns"] + ] + + return sanitized + + +def _sanitize_column(column: dict[str, Any]) -> dict[str, Any]: + """Sanitize a column configuration.""" + sanitized = dict(column) + + # Default display name to column name + if "displayName" not in sanitized and "column" in sanitized: + col_name = sanitized["column"] + # Convert snake_case to Title Case + sanitized["displayName"] = col_name.replace("_", " ").title() + + # Default data type + if "dataType" not in sanitized: + sanitized["dataType"] = "string" + + # Ensure categories is a list + if "categories" not in sanitized: + sanitized["categories"] = [] + elif not isinstance(sanitized["categories"], list): + sanitized["categories"] = [sanitized["categories"]] + + # Normalize null transform + if "transform" in sanitized and sanitized["transform"] is None: + del sanitized["transform"] + + return sanitized diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..3a8be3e --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +# Tests package for TableScanner diff --git a/tests/test_config_generation.py b/tests/test_config_generation.py new file mode 100644 index 0000000..a10f97b --- /dev/null +++ b/tests/test_config_generation.py @@ -0,0 +1,521 @@ +""" +Tests for Config Generation and Validation. + +Tests the new prompts, validation, and type inference improvements +for the TableScanner-DataTables Viewer integration. +""" + +import pytest +import tempfile +import sqlite3 +from pathlib import Path + + +# ============================================================================= +# FIXTURES +# ============================================================================= + +@pytest.fixture +def sample_db(): + """Create a sample SQLite database for testing.""" + with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f: + db_path = Path(f.name) + + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + # Create sample gene table + cursor.execute(""" + CREATE TABLE genes ( + gene_id TEXT PRIMARY KEY, + gene_name TEXT, + product TEXT, + strand TEXT, + start_pos INTEGER, + end_pos INTEGER, + uniref_90 TEXT, + go_terms TEXT, + sequence TEXT + ) + """) + + # Insert sample data + cursor.executemany(""" + INSERT INTO genes VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, [ + ("GENE001", "dnaA", "replication initiator", "+", 100, 500, + "UniRef:UniRef90_A0A1B2C3", "GO:0008150", "ATCGATCGATCGATCGATCGATCG"), + ("GENE002", "dnaN", "DNA polymerase III", "-", 600, 1200, + "UniRef:UniRef90_D4E5F6", "GO:0003677", "GCTAGCTAGCTAGCTAGCTAGCTA"), + ("GENE003", "dnaK", "heat shock protein", "+", 1300, 2100, + None, None, "TTAATTAATTAATTAATTAATTAA"), + ]) + + conn.commit() + conn.close() + + yield db_path + + # Cleanup + db_path.unlink(missing_ok=True) + + +@pytest.fixture +def sample_config(): + """Sample valid config for testing validation.""" + return { + "id": "test_config", + "name": "Test Configuration", + "version": "1.0.0", + "tables": { + "genes": { + "displayName": "Genes", + "columns": [ + { + "column": "gene_id", + "displayName": "Gene ID", + "dataType": "id", + "categories": ["core"], + "pin": "left" + }, + { + "column": "gene_name", + "displayName": "Gene Name", + "dataType": "string", + "categories": ["core"] + } + ] + } + } + } + + +# ============================================================================= +# VALIDATION TESTS +# ============================================================================= + +class TestValidation: + """Tests for config validation module.""" + + def test_validate_valid_config(self, sample_config): + """Valid config should pass validation.""" + from app.services.validation import validate_config + + is_valid, error = validate_config(sample_config) + assert is_valid is True + assert error is None + + def test_validate_missing_required_fields(self): + """Config missing required fields should fail.""" + from app.services.validation import validate_config + + # Missing 'tables' + invalid = {"id": "test", "name": "Test"} + is_valid, error = validate_config(invalid) + assert is_valid is False + assert "tables" in error.lower() + + def test_validate_empty_tables(self): + """Config with empty tables should fail.""" + from app.services.validation import validate_config + + invalid = {"id": "test", "name": "Test", "tables": {}} + is_valid, error = validate_config(invalid) + assert is_valid is False + + def test_validate_column_missing_name(self, sample_config): + """Column without 'column' key should fail.""" + from app.services.validation import validate_column_config + + invalid_col = {"displayName": "Test"} + is_valid, error = validate_column_config(invalid_col) + assert is_valid is False + assert "column" in error.lower() + + def test_sanitize_config(self, sample_config): + """Sanitization should normalize config.""" + from app.services.validation import sanitize_config + + # Config without version + raw = dict(sample_config) + del raw["version"] + + sanitized = sanitize_config(raw) + assert sanitized["version"] == "1.0.0" + + +# ============================================================================= +# PROMPT TESTS +# ============================================================================= + +class TestPrompts: + """Tests for prompt engineering module.""" + + def test_detect_uniref_pattern(self): + """Should detect UniRef prefix pattern.""" + from app.services.prompts import detect_value_patterns + + values = ["UniRef:UniRef90_A0A1B2", "UniRef:UniRef90_C3D4E5"] + patterns = detect_value_patterns(values) + + assert any("UniRef" in p for p in patterns) + + def test_detect_go_pattern(self): + """Should detect GO term pattern.""" + from app.services.prompts import detect_value_patterns + + values = ["GO:0008150", "GO:0003677", "GO:0006412"] + patterns = detect_value_patterns(values) + + assert any("GO" in p for p in patterns) + + def test_detect_sequence_pattern(self): + """Should detect DNA sequence pattern.""" + from app.services.prompts import detect_value_patterns + + values = ["ATCGATCGATCGATCGATCGATCGATCG", "GCTAGCTAGCTAGCTAGCTAGCTAGCTA"] + patterns = detect_value_patterns(values) + + assert any("sequence" in p.lower() for p in patterns) + + def test_detect_strand_pattern(self): + """Should detect strand indicator pattern.""" + from app.services.prompts import detect_value_patterns + + values = ["+", "-", "+", "+", "-"] + patterns = detect_value_patterns(values) + + assert any("strand" in p.lower() for p in patterns) + + def test_compute_numeric_stats(self): + """Should compute basic numeric statistics.""" + from app.services.prompts import compute_numeric_stats + + values = [1.5, 2.5, 3.5, 4.5, 5.5] + stats = compute_numeric_stats(values) + + assert stats is not None + assert stats["min"] == 1.5 + assert stats["max"] == 5.5 + assert stats["count"] == 5 + assert stats["has_decimals"] is True + + def test_compute_numeric_stats_non_numeric(self): + """Should return None for non-numeric values.""" + from app.services.prompts import compute_numeric_stats + + values = ["abc", "def", "ghi"] + stats = compute_numeric_stats(values) + + assert stats is None + + def test_build_prompt_structure(self): + """Generated prompt should have expected sections.""" + from app.services.prompts import build_table_config_prompt + + prompt = build_table_config_prompt( + table_name="genes", + schema_info=[{"name": "gene_id", "type": "TEXT"}], + sample_values={"gene_id": ["GENE001", "GENE002"]}, + detected_patterns={"gene_id": ["no_special_pattern"]}, + statistics={}, + row_count=100 + ) + + assert "genes" in prompt + assert "Sample Values" in prompt + assert "Detected Patterns" in prompt + assert "JSON" in prompt + + +# ============================================================================= +# TYPE INFERENCE TESTS +# ============================================================================= + +class TestTypeInference: + """Tests for enhanced type inference patterns.""" + + def test_uniref_chain_transform(self): + """UniRef columns should get chain transformer.""" + from app.services.type_inference import TypeInferenceEngine + + engine = TypeInferenceEngine() + result = engine.infer_from_name("uniref_90") + + assert result is not None + assert result.transform is not None + assert result.transform.type == "chain" + + def test_strand_badge_transform(self): + """Strand columns should get badge transformer.""" + from app.services.type_inference import TypeInferenceEngine + + engine = TypeInferenceEngine() + result = engine.infer_from_name("strand") + + assert result is not None + assert result.transform is not None + assert result.transform.type == "badge" + assert "colorMap" in result.transform.options + + def test_pfam_chain_transform(self): + """Pfam columns should get chain transformer.""" + from app.services.type_inference import TypeInferenceEngine + + engine = TypeInferenceEngine() + result = engine.infer_from_name("pfam_domain") + + assert result is not None + assert result.transform is not None + assert result.transform.type == "chain" + + def test_go_ontology_transform(self): + """GO columns should get ontology transformer.""" + from app.services.type_inference import TypeInferenceEngine + + engine = TypeInferenceEngine() + result = engine.infer_from_name("GO_terms") + + assert result is not None + assert result.transform is not None + assert result.transform.type == "ontology" + + +# ============================================================================= +# FINGERPRINT TESTS +# ============================================================================= + +class TestFingerprint: + """Tests for database fingerprinting.""" + + def test_compute_fingerprint(self, sample_db): + """Should compute consistent fingerprint.""" + from app.services.fingerprint import DatabaseFingerprint + + fp_service = DatabaseFingerprint() + fp1 = fp_service.compute(sample_db) + fp2 = fp_service.compute(sample_db) + + assert fp1 == fp2 + assert len(fp1) == 16 # SHA256 prefix + + def test_cache_and_retrieve(self, sample_config): + """Should cache and retrieve configs.""" + from app.services.fingerprint import DatabaseFingerprint + import tempfile + + with tempfile.TemporaryDirectory() as tmpdir: + fp_service = DatabaseFingerprint(config_dir=tmpdir) + + fingerprint = "test_fingerprint_123" + fp_service.cache_config(fingerprint, sample_config) + + retrieved = fp_service.get_cached_config(fingerprint) + + assert retrieved is not None + assert retrieved["id"] == sample_config["id"] + + def test_clear_cache(self, sample_config): + """Should clear cached configs.""" + from app.services.fingerprint import DatabaseFingerprint + import tempfile + + with tempfile.TemporaryDirectory() as tmpdir: + fp_service = DatabaseFingerprint(config_dir=tmpdir) + + fingerprint = "test_to_delete" + fp_service.cache_config(fingerprint, sample_config) + + assert fp_service.is_cached(fingerprint) is True + + deleted = fp_service.clear_cache(fingerprint) + assert deleted == 1 + + assert fp_service.is_cached(fingerprint) is False + + +# ============================================================================= +# CONFIG GENERATOR TESTS +# ============================================================================= + +class TestConfigGenerator: + """Tests for config generator.""" + + def test_generate_config(self, sample_db): + """Should generate valid config from database.""" + from app.services.config_generator import ConfigGenerator + from app.services.validation import validate_config + import tempfile + + with tempfile.TemporaryDirectory() as tmpdir: + generator = ConfigGenerator(config_dir=tmpdir) + result = generator.generate( + db_path=sample_db, + handle_ref="test/test/1", + force_regenerate=True, + ai_preference="rules-only" + ) + + assert result.tables_analyzed > 0 + assert result.config is not None + + # Validate generated config + is_valid, error = validate_config(result.config) + assert is_valid is True, f"Validation failed: {error}" + + def test_cache_hit(self, sample_db): + """Second generation should use cache.""" + from app.services.config_generator import ConfigGenerator + import tempfile + + with tempfile.TemporaryDirectory() as tmpdir: + generator = ConfigGenerator(config_dir=tmpdir) + + # First generation + result1 = generator.generate( + db_path=sample_db, + handle_ref="test/test/1", + ai_preference="rules-only" + ) + assert result1.cache_hit is False + + # Second generation (should hit cache) + result2 = generator.generate( + db_path=sample_db, + handle_ref="test/test/1", + ai_preference="rules-only" + ) + assert result2.cache_hit is True + + +# ============================================================================= +# FALLBACK REGISTRY TESTS +# ============================================================================= + +class TestFallbackRegistry: + """Tests for fallback config registry.""" + + def test_berdl_object_type_match(self): + """BERDL object type should match berdl_tables config.""" + from app.configs import get_fallback_config_id, has_fallback_config + + assert has_fallback_config("KBaseGeneDataLakes.BERDLTables-1.0") is True + assert get_fallback_config_id("KBaseGeneDataLakes.BERDLTables-1.0") == "berdl_tables" + + def test_genome_data_tables_match(self): + """GenomeDataTables should match genome_data_tables config.""" + from app.configs import get_fallback_config_id, has_fallback_config + + assert has_fallback_config("KBaseFBA.GenomeDataLakeTables-1.0") is True + assert get_fallback_config_id("KBaseFBA.GenomeDataLakeTables-1.0") == "genome_data_tables" + + def test_unknown_object_type(self): + """Unknown object type should return None.""" + from app.configs import get_fallback_config_id, has_fallback_config + + assert has_fallback_config("SomeUnknown.Type-1.0") is False + assert get_fallback_config_id("SomeUnknown.Type-1.0") is None + + def test_load_berdl_config(self): + """Should load and parse berdl_tables.json.""" + from app.configs import get_fallback_config + + config = get_fallback_config("KBaseGeneDataLakes.BERDLTables-1.0") + + assert config is not None + assert config["id"] == "berdl_tables" + assert "tables" in config + assert "genome_features" in config["tables"] + + def test_load_genome_data_tables_config(self): + """Should load and parse genome_data_tables.json.""" + from app.configs import get_fallback_config + + config = get_fallback_config("KBaseFBA.GenomeDataLakeTables-1.0") + + assert config is not None + assert config["id"] == "genome_data_tables" + assert "tables" in config + assert "Genes" in config["tables"] + + def test_list_available_configs(self): + """Should list all available configs.""" + from app.configs import list_available_configs + + configs = list_available_configs() + + assert len(configs) >= 2 + config_ids = [c["id"] for c in configs] + assert "berdl_tables" in config_ids + assert "genome_data_tables" in config_ids + + def test_config_cache(self): + """Configs should be cached after first load.""" + from app.configs import get_fallback_config, clear_cache + + # Clear cache first + clear_cache() + + # First load + config1 = get_fallback_config("KBaseGeneDataLakes.BERDLTables-1.0") + + # Second load (should use cache) + config2 = get_fallback_config("KBaseGeneDataLakes.BERDLTables-1.0") + + assert config1 is config2 # Same object reference + + +# ============================================================================= +# ENHANCED RESPONSE TESTS +# ============================================================================= + +class TestEnhancedResponses: + """Tests for enhanced API response models.""" + + def test_config_response_has_new_fields(self): + """ConfigGenerationResponse should have all new fields.""" + from app.models import ConfigGenerationResponse + + # Check field names exist + fields = ConfigGenerationResponse.model_fields + assert "fallback_used" in fields + assert "fallback_reason" in fields + assert "config_source" in fields + assert "db_schema" in fields # Note: aliased to "schema" in JSON + assert "ai_available" in fields + assert "ai_error" in fields + assert "api_version" in fields + + def test_table_list_response_has_new_fields(self): + """TableListResponse should have all new fields.""" + from app.models import TableListResponse + + fields = TableListResponse.model_fields + assert "schemas" in fields + assert "has_builtin_config" in fields + assert "builtin_config_id" in fields + assert "database_size_bytes" in fields + assert "total_rows" in fields + assert "api_version" in fields + + def test_backward_compatibility(self): + """Old clients should still work with minimal fields.""" + from app.models import ConfigGenerationResponse + + # Create response with only required fields + response = ConfigGenerationResponse( + status="generated", + fingerprint="test_fp", + config_url="/config/test", + config={"id": "test", "tables": {}}, + tables_analyzed=1, + columns_inferred=5, + generation_time_ms=100.0, + cache_hit=False, + ) + + # Should have default values for new fields + assert response.fallback_used is False + assert response.api_version == "2.0" + assert response.ai_available is True + From 34eb49c3b6e74d94eebd5cf506cd518a9f25702f Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Thu, 15 Jan 2026 10:26:28 -0600 Subject: [PATCH 05/19] simplification --- .gitignore | 3 + README.md | 36 +- app/config.py | 4 + app/configs/__init__.py | 26 - app/configs/berdl_tables.json | 998 ------------------ app/configs/fallback_registry.py | 203 ---- app/configs/genome_data_tables.json | 527 --------- app/db/__init__.py | 5 + app/db/schema.sql | 107 ++ app/main.py | 19 +- app/models.py | 248 ++++- app/routes.py | 188 ++-- app/services/__init__.py | 14 +- app/services/ai/__init__.py | 14 + app/services/{ => ai}/ai_provider.py | 4 +- app/services/{ => ai}/prompts.py | 0 app/services/config/__init__.py | 12 + app/services/{ => config}/config_generator.py | 6 +- app/services/config_registry.py | 119 +++ app/services/data/__init__.py | 19 + app/services/{ => data}/fingerprint.py | 0 app/services/{ => data}/schema_analyzer.py | 0 app/services/{ => data}/type_inference.py | 0 app/services/{ => data}/validation.py | 0 app/services/viewer_client.py | 116 ++ docs/API_EXAMPLES.md | 595 +++++++++++ docs/ARCHITECTURE.md | 575 ++++++++-- docs/CONFIG_SYSTEM.md | 182 ++++ docs/QUICKSTART_DEMO.md | 50 - docs/README.md | 24 + docs/USAGE_GUIDE.md | 160 --- scripts/migrate_fallback_configs.py | 144 +++ scripts/sync_developer_configs.py | 91 ++ scripts/verify_config_plane.py | 131 +++ tests/test_api_basic.py | 67 ++ tests/test_config_control_plane.py | 427 ++++++++ tests/test_integration.py | 409 +++++++ tests/test_performance.py | 234 ++++ 38 files changed, 3593 insertions(+), 2164 deletions(-) delete mode 100644 app/configs/__init__.py delete mode 100644 app/configs/berdl_tables.json delete mode 100644 app/configs/fallback_registry.py delete mode 100644 app/configs/genome_data_tables.json create mode 100644 app/db/__init__.py create mode 100644 app/db/schema.sql create mode 100644 app/services/ai/__init__.py rename app/services/{ => ai}/ai_provider.py (99%) rename app/services/{ => ai}/prompts.py (100%) create mode 100644 app/services/config/__init__.py rename app/services/{ => config}/config_generator.py (98%) create mode 100644 app/services/config_registry.py create mode 100644 app/services/data/__init__.py rename app/services/{ => data}/fingerprint.py (100%) rename app/services/{ => data}/schema_analyzer.py (100%) rename app/services/{ => data}/type_inference.py (100%) rename app/services/{ => data}/validation.py (100%) create mode 100644 app/services/viewer_client.py create mode 100644 docs/API_EXAMPLES.md create mode 100644 docs/CONFIG_SYSTEM.md delete mode 100644 docs/QUICKSTART_DEMO.md create mode 100644 docs/README.md delete mode 100644 docs/USAGE_GUIDE.md create mode 100755 scripts/migrate_fallback_configs.py create mode 100755 scripts/sync_developer_configs.py create mode 100644 scripts/verify_config_plane.py create mode 100644 tests/test_api_basic.py create mode 100644 tests/test_config_control_plane.py create mode 100644 tests/test_integration.py create mode 100644 tests/test_performance.py diff --git a/.gitignore b/.gitignore index c7623f8..6e4db2d 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,9 @@ docs/DEMO_SCRIPT.md docs/QUICKSTART.md docs/internal/ DATABASE_SCHEMA.md +docs/personal/ +archive/ +docs/archive .DS_Store .idea diff --git a/README.md b/README.md index ac94cb9..7aee492 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,28 @@ TableScanner operates as a bridge between KBase storage and client applications: 2. **Local Caching**: Stores databases locally to avoid repeated downloads. 3. **Indexing**: Creates indices on-the-fly for all table columns to optimize query performance. 4. **API Layer**: A FastAPI application that handles requests and executes SQL queries against the local cache. +5. **Config Control Plane**: Unified configuration management with lifecycle, versioning, and AI integration. + +### Config System + +TableScanner includes a unified **Config System** supporting both AI-generated and developer-edited configs: + +- **Developer Configs**: Edit JSON files (like `berdl_tables.json`) and sync to system +- **AI Generation**: Automatically generate configs for new KBase data tables +- **Versioning**: Draft → Proposed → Published workflow with full history +- **Smart Resolution**: Cascading config resolution with fallbacks + +**Quick Start**: +```bash +# Edit developer config +vim app/configs/berdl_tables.json +python scripts/sync_developer_configs.py --filename berdl_tables.json + +# Generate config via AI +curl -X POST "http://127.0.0.1:8000/object/76990/7/2/config/generate" +``` + +See [docs/CONFIG_SYSTEM.md](docs/CONFIG_SYSTEM.md) for complete documentation. Technical details on race conditions, UI design, and concurrency are available in [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md). @@ -80,10 +102,20 @@ Payload example: ## Project Structure - `app/`: Application logic and routes. + - `app/services/`: Organized service modules: + - `config/`: Config management (store, resolver, developer configs) + - `ai/`: AI services (providers, prompts) + - `data/`: Data analysis (schema, fingerprinting, validation) + - `app/configs/`: Developer-editable JSON configs and registry. + - `app/db/`: Database schema for config system. - `app/utils/`: Utilities for caching, SQLite, and KBase Workspace integration. - `static/`: Production-grade Web Explorer (`viewer.html`). -- `docs/`: Technical documentation and usage guides. -- `scripts/`: Client examples and utility scripts. +- `docs/`: Technical documentation: + - `docs/CONFIG_SYSTEM.md`: Complete config system documentation. + - `docs/API_EXAMPLES.md`: API usage examples. +- `scripts/`: Utility scripts: + - `scripts/sync_developer_configs.py`: Sync JSON configs to system. +- `tests/`: Test suite. ## License MIT License. diff --git a/app/config.py b/app/config.py index 5ed587d..6b2cd79 100644 --- a/app/config.py +++ b/app/config.py @@ -47,6 +47,10 @@ class Settings(BaseSettings): default="https://kbase.us/services", description="Base URL for KBase services" ) + VIEWER_API_URL: str = Field( + default="http://localhost:3000/api", + description="DataTables Viewer API base URL for sending generated configs" + ) BLOBSTORE_URL: str = Field( default="https://kbase.us/services/shock-api", description="KBase blobstore/shock service URL" diff --git a/app/configs/__init__.py b/app/configs/__init__.py deleted file mode 100644 index 8d251ec..0000000 --- a/app/configs/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -""" -Built-in Fallback Configs Package. - -Contains pre-built DataTables Viewer configurations for known KBase object types. -These are used when AI generation fails or for fast config matching. -""" - -from .fallback_registry import ( - get_fallback_config, - get_fallback_config_id, - has_fallback_config, - load_config_file, - list_available_configs, - get_config_for_tables, - clear_cache, -) - -__all__ = [ - "get_fallback_config", - "get_fallback_config_id", - "has_fallback_config", - "load_config_file", - "list_available_configs", - "get_config_for_tables", - "clear_cache", -] diff --git a/app/configs/berdl_tables.json b/app/configs/berdl_tables.json deleted file mode 100644 index dd21556..0000000 --- a/app/configs/berdl_tables.json +++ /dev/null @@ -1,998 +0,0 @@ -{ - "id": "berdl_tables", - "name": "BERDL Genome Tables", - "description": "Pangenome analysis tables from BERDL/GenomeDataLakes objects", - "version": "1.0.0", - "icon": "bi-collection", - "color": "#6366f1", - "defaults": { - "pageSize": 50, - "density": "default", - "showRowNumbers": true, - "enableSelection": true, - "enableExport": true - }, - "sharedCategories": [ - { - "id": "core", - "name": "Core Info", - "description": "Essential identifiers and names", - "icon": "bi-database", - "color": "#6366f1", - "defaultVisible": true, - "order": 1 - }, - { - "id": "functional", - "name": "Functional Annotation", - "description": "Function and product information", - "icon": "bi-gear", - "color": "#22c55e", - "defaultVisible": true, - "order": 2 - }, - { - "id": "external", - "name": "External Links", - "description": "Links to external databases", - "icon": "bi-box-arrow-up-right", - "color": "#06b6d4", - "defaultVisible": true, - "order": 3 - }, - { - "id": "sequence", - "name": "Sequence Data", - "description": "DNA, RNA, and protein sequences", - "icon": "bi-text-left", - "color": "#f59e0b", - "defaultVisible": false, - "order": 4 - }, - { - "id": "ontology", - "name": "Ontology Terms", - "description": "GO, KEGG, Pfam annotations", - "icon": "bi-tags", - "color": "#8b5cf6", - "defaultVisible": true, - "order": 5 - }, - { - "id": "statistics", - "name": "Statistics", - "description": "Numerical metrics and scores", - "icon": "bi-graph-up", - "color": "#ef4444", - "defaultVisible": true, - "order": 6 - }, - { - "id": "pangenome", - "name": "Pangenome Analysis", - "description": "Core/accessory gene classification", - "icon": "bi-diagram-3", - "color": "#14b8a6", - "defaultVisible": true, - "order": 7 - } - ], - "tables": { - "genome": { - "displayName": "Genomes", - "description": "Genome metadata and taxonomy information", - "icon": "bi-circle", - "settings": { - "defaultSortColumn": "id", - "defaultSortOrder": "asc" - }, - "columns": [ - { - "column": "id", - "displayName": "Genome ID", - "dataType": "id", - "categories": [ - "core" - ], - "sortable": true, - "filterable": true, - "copyable": true, - "width": "140px", - "pin": "left" - }, - { - "column": "gtdb_taxonomy", - "displayName": "GTDB Taxonomy", - "dataType": "string", - "categories": [ - "core" - ], - "sortable": true, - "filterable": true, - "searchable": true, - "width": "400px" - }, - { - "column": "ncbi_taxonomy", - "displayName": "NCBI Taxonomy", - "dataType": "string", - "categories": [ - "core" - ], - "sortable": true, - "filterable": true, - "width": "300px" - }, - { - "column": "n_contigs", - "displayName": "Contigs", - "dataType": "integer", - "categories": [ - "statistics" - ], - "sortable": true, - "filterable": true, - "align": "right", - "width": "100px" - }, - { - "column": "n_features", - "displayName": "Features", - "dataType": "integer", - "categories": [ - "statistics" - ], - "sortable": true, - "filterable": true, - "align": "right", - "width": "100px" - } - ] - }, - "genome_features": { - "displayName": "Genome Features", - "description": "Gene annotations with functional and ontology data", - "icon": "bi-list-ul", - "settings": { - "defaultSortColumn": "id", - "defaultSortOrder": "asc" - }, - "columns": [ - { - "column": "id", - "displayName": "ID", - "dataType": "id", - "categories": [ - "core" - ], - "sortable": true, - "width": "80px", - "pin": "left" - }, - { - "column": "genome_id", - "displayName": "Genome", - "dataType": "id", - "categories": [ - "core" - ], - "sortable": true, - "filterable": true, - "width": "120px" - }, - { - "column": "contig_id", - "displayName": "Contig", - "dataType": "id", - "categories": [ - "core" - ], - "sortable": true, - "filterable": true, - "width": "200px" - }, - { - "column": "feature_id", - "displayName": "Feature ID", - "dataType": "id", - "categories": [ - "core" - ], - "sortable": true, - "filterable": true, - "copyable": true, - "width": "120px" - }, - { - "column": "length", - "displayName": "Length", - "dataType": "integer", - "categories": [ - "statistics" - ], - "sortable": true, - "align": "right", - "width": "80px" - }, - { - "column": "start", - "displayName": "Start", - "dataType": "integer", - "categories": [ - "statistics" - ], - "sortable": true, - "align": "right", - "width": "80px" - }, - { - "column": "end", - "displayName": "End", - "dataType": "integer", - "categories": [ - "statistics" - ], - "sortable": true, - "align": "right", - "width": "80px" - }, - { - "column": "strand", - "displayName": "Strand", - "dataType": "string", - "categories": [ - "core" - ], - "sortable": true, - "filterable": true, - "align": "center", - "width": "60px" - }, - { - "column": "sequence", - "displayName": "Protein Sequence", - "dataType": "sequence", - "categories": [ - "sequence" - ], - "sortable": false, - "copyable": true, - "width": "150px", - "transform": { - "type": "sequence", - "options": { - "maxLength": 20, - "showCopyButton": true - } - } - }, - { - "column": "bakta_function", - "displayName": "Function (Bakta)", - "dataType": "string", - "categories": [ - "functional" - ], - "sortable": true, - "filterable": true, - "searchable": true, - "width": "250px" - }, - { - "column": "rast_function", - "displayName": "Function (RAST)", - "dataType": "string", - "categories": [ - "functional" - ], - "sortable": true, - "filterable": true, - "searchable": true, - "width": "250px" - }, - { - "column": "gene_names", - "displayName": "Gene Names", - "dataType": "string", - "categories": [ - "functional" - ], - "sortable": true, - "filterable": true, - "width": "120px" - }, - { - "column": "go", - "displayName": "GO Terms", - "dataType": "ontology", - "categories": [ - "ontology" - ], - "sortable": false, - "filterable": true, - "width": "250px", - "transform": { - "type": "ontologyLookup", - "options": { - "ontologyType": "go", - "urlTemplate": "https://amigo.geneontology.org/amigo/term/{id}", - "idPattern": "(GO:\\d+)", - "delimiter": ";", - "maxLength": 40, - "showId": true, - "style": "inline" - } - } - }, - { - "column": "ko", - "displayName": "KEGG Orthologs", - "dataType": "ontology", - "categories": [ - "ontology" - ], - "sortable": false, - "filterable": true, - "width": "280px", - "transform": { - "type": "ontologyLookup", - "options": { - "ontologyType": "kegg", - "urlTemplate": "https://www.genome.jp/entry/{id}", - "idPattern": "(K\\d+)", - "delimiter": ";", - "maxLength": 45, - "showId": true, - "style": "inline" - } - } - }, - { - "column": "pfam", - "displayName": "Pfam Domains", - "dataType": "ontology", - "categories": [ - "ontology" - ], - "sortable": false, - "filterable": true, - "width": "250px", - "transform": { - "type": "ontologyLookup", - "options": { - "ontologyType": "pfam", - "urlTemplate": "https://www.ebi.ac.uk/interpro/entry/pfam/{id}", - "idPattern": "(PF\\d+)", - "delimiter": ";", - "maxLength": 40, - "showId": true, - "style": "inline" - } - } - }, - { - "column": "cog", - "displayName": "COG", - "dataType": "ontology", - "categories": [ - "ontology" - ], - "sortable": true, - "filterable": true, - "width": "220px", - "transform": { - "type": "ontologyLookup", - "options": { - "ontologyType": "cog", - "urlTemplate": "https://www.ncbi.nlm.nih.gov/research/cog/cog/{id}", - "idPattern": "(COG\\d+|[A-Z])", - "delimiter": ";", - "maxLength": 40, - "showId": true, - "style": "inline" - } - } - }, - { - "column": "ec", - "displayName": "EC Number", - "dataType": "ontology", - "categories": [ - "ontology" - ], - "sortable": true, - "filterable": true, - "width": "240px", - "transform": { - "type": "ontologyLookup", - "options": { - "ontologyType": "ec", - "urlTemplate": "https://enzyme.expasy.org/EC/{id}", - "idPattern": "([\\d]+\\.[\\d.-]+)", - "delimiter": ";", - "maxLength": 40, - "showId": true, - "style": "inline" - } - } - }, - { - "column": "so", - "displayName": "Sequence Ontology", - "dataType": "ontology", - "categories": [ - "ontology" - ], - "sortable": false, - "width": "200px", - "transform": { - "type": "ontologyLookup", - "options": { - "ontologyType": "so", - "urlTemplate": "http://www.sequenceontology.org/browser/current_release/term/{id}", - "idPattern": "(SO:\\d+)", - "maxLength": 35, - "showId": true, - "style": "inline" - } - } - }, - { - "column": "uniref_100", - "displayName": "UniRef100", - "dataType": "ontology", - "categories": [ - "external" - ], - "sortable": true, - "width": "280px", - "transform": { - "type": "ontologyLookup", - "options": { - "ontologyType": "uniref", - "urlTemplate": "https://www.uniprot.org/uniref/{id}", - "idPattern": "(UniRef\\d+_[A-Z0-9]+)", - "maxLength": 40, - "showId": true, - "style": "inline" - } - } - }, - { - "column": "uniref_90", - "displayName": "UniRef90", - "dataType": "ontology", - "categories": [ - "external" - ], - "sortable": true, - "width": "280px", - "transform": { - "type": "ontologyLookup", - "options": { - "ontologyType": "uniref", - "urlTemplate": "https://www.uniprot.org/uniref/{id}", - "idPattern": "(UniRef\\d+_[A-Z0-9]+)", - "maxLength": 40, - "showId": true, - "style": "inline" - } - } - }, - { - "column": "uniref_50", - "displayName": "UniRef50", - "dataType": "ontology", - "categories": [ - "external" - ], - "sortable": true, - "width": "280px", - "transform": { - "type": "ontologyLookup", - "options": { - "ontologyType": "uniref", - "urlTemplate": "https://www.uniprot.org/uniref/{id}", - "idPattern": "(UniRef\\d+_[A-Z0-9]+)", - "maxLength": 40, - "showId": true, - "style": "inline" - } - } - }, - { - "column": "pangenome_cluster_id", - "displayName": "Cluster ID", - 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"max": 100, - "colorScale": "sequential", - "showValue": true, - "decimals": 2 - } - } - }, - { - "column": "af1", - "displayName": "AF1", - "dataType": "float", - "categories": [ - "statistics" - ], - "sortable": true, - "align": "right", - "width": "80px", - "transform": { - "type": "number", - "options": { - "decimals": 2 - } - } - }, - { - "column": "af2", - "displayName": "AF2", - "dataType": "float", - "categories": [ - "statistics" - ], - "sortable": true, - "align": "right", - "width": "80px", - "transform": { - "type": "number", - "options": { - "decimals": 2 - } - } - }, - { - "column": "kind", - "displayName": "Type", - "dataType": "string", - "categories": [ - "core" - ], - "sortable": true, - "filterable": true, - "width": "100px", - "transform": { - "type": "badge", - "options": { - "variant": "subtle" - } - } - } - ] - }, - "missing_functions": { - "displayName": "Missing Functions", - "description": "Gap-filled reactions and their sources", - "icon": "bi-exclamation-triangle", - "settings": { - "defaultSortColumn": "Reaction", - "defaultSortOrder": "asc" - }, - "columns": [ - { - "column": "Reaction", - "displayName": "Reaction", - "dataType": "id", - "categories": [ - "core" - ], - "sortable": true, - "filterable": true, - "copyable": true, - "width": "150px", - "pin": "left" - }, - { - "column": "RAST_function", - "displayName": "RAST Function", - "dataType": "string", - "categories": [ - "functional" - ], - "sortable": true, - "filterable": true, - "searchable": true, - "width": "300px" - }, - { - "column": "RichGapfill", - "displayName": "Rich Gapfill", - "dataType": "boolean", - "categories": [ - "statistics" - ], - "sortable": true, - "filterable": true, - "align": "center", - "width": "110px", - "transform": { - "type": "boolean", - "options": { - "trueIcon": "bi-check-circle-fill", - "falseIcon": "bi-x-circle", - "trueColor": "#22c55e", - "falseColor": "#94a3b8" - } - } - }, - { - "column": "MinimalGapfill", - "displayName": "Minimal Gapfill", - "dataType": "boolean", - "categories": [ - "statistics" - ], - "sortable": true, - "filterable": true, - "align": "center", - "width": "120px", - "transform": { - "type": "boolean", - "options": { - "trueIcon": "bi-check-circle-fill", - "falseIcon": "bi-x-circle", - "trueColor": "#22c55e", - "falseColor": "#94a3b8" - } - } - }, - { - "column": "PhenotypeGapfill", - "displayName": "Phenotype Gapfill", - "dataType": "boolean", - "categories": [ - "statistics" - ], - "sortable": true, - "filterable": true, - "align": "center", - "width": "130px", - "transform": { - "type": "boolean", - "options": { - "trueIcon": "bi-check-circle-fill", - "falseIcon": "bi-x-circle", - "trueColor": "#22c55e", - "falseColor": "#94a3b8" - } - } - }, - { - "column": "ModuleGapfill", - "displayName": "Module Gapfill", - "dataType": "boolean", - "categories": [ - "statistics" - ], - "sortable": true, - "filterable": true, - "align": "center", - "width": "120px", - "transform": { - "type": "boolean", - "options": { - "trueIcon": "bi-check-circle-fill", - "falseIcon": "bi-x-circle", - "trueColor": "#22c55e", - "falseColor": "#94a3b8" - } - } - }, - { - "column": "Pangenome", - "displayName": "In Pangenome", - "dataType": "boolean", - "categories": [ - "pangenome" - ], - "sortable": true, - "filterable": true, - "align": "center", - "width": "120px", - "transform": { - "type": "boolean", - "options": { - "trueIcon": "bi-check-circle-fill", - "falseIcon": "bi-x-circle", - "trueColor": "#22c55e", - "falseColor": "#94a3b8" - } - } - } - ] - } - } -} \ No newline at end of file diff --git a/app/configs/fallback_registry.py b/app/configs/fallback_registry.py deleted file mode 100644 index 973fc83..0000000 --- a/app/configs/fallback_registry.py +++ /dev/null @@ -1,203 +0,0 @@ -""" -Fallback Config Registry. - -Maps KBase object types to built-in configuration files. -Used when AI generation fails or for known object types. -""" - -from __future__ import annotations - -import json -import logging -import re -from pathlib import Path -from typing import Any - -logger = logging.getLogger(__name__) - -# Directory containing built-in config files -CONFIG_DIR = Path(__file__).parent - -# Object type patterns mapped to config file names -# Supports wildcards like "KBaseFBA.GenomeDataLakeTables-*" -FALLBACK_CONFIG_PATTERNS: dict[str, str] = { - # BERDL/Pangenome tables - r"KBaseGeneDataLakes\.BERDLTables.*": "berdl_tables.json", - r"KBaseGeneDataLakes\.PangenomeTables.*": "berdl_tables.json", - - # Genome data tables - r"KBaseFBA\.GenomeDataLakeTables.*": "genome_data_tables.json", - r"KBase\.GenomeDataTables.*": "genome_data_tables.json", - - # Legacy patterns - r".*BERDLTables.*": "berdl_tables.json", - r".*GenomeDataTables.*": "genome_data_tables.json", -} - -# Pre-compiled patterns for performance -_COMPILED_PATTERNS: list[tuple[re.Pattern, str]] = [ - (re.compile(pattern), filename) - for pattern, filename in FALLBACK_CONFIG_PATTERNS.items() -] - -# Cache loaded configs -_CONFIG_CACHE: dict[str, dict] = {} - - -def get_fallback_config(object_type: str | None) -> dict[str, Any] | None: - """ - Get a built-in fallback config for the given object type. - - Args: - object_type: KBase object type string (e.g., "KBaseGeneDataLakes.BERDLTables-1.0") - - Returns: - Config dictionary if a fallback exists, None otherwise - """ - if not object_type: - return None - - # Try to match against patterns - for pattern, filename in _COMPILED_PATTERNS: - if pattern.match(object_type): - return load_config_file(filename) - - return None - - -def get_fallback_config_id(object_type: str | None) -> str | None: - """ - Get the config ID that would be used for fallback. - - Args: - object_type: KBase object type string - - Returns: - Config ID (filename without extension) if match found, None otherwise - """ - if not object_type: - return None - - for pattern, filename in _COMPILED_PATTERNS: - if pattern.match(object_type): - return filename.replace(".json", "") - - return None - - -def has_fallback_config(object_type: str | None) -> bool: - """ - Check if a fallback config exists for the object type. - - Args: - object_type: KBase object type string - - Returns: - True if fallback exists - """ - return get_fallback_config_id(object_type) is not None - - -def load_config_file(filename: str) -> dict[str, Any] | None: - """ - Load a config file from the configs directory. - - Args: - filename: Name of the config file (e.g., "berdl_tables.json") - - Returns: - Parsed config dictionary, or None if not found - """ - # Check cache first - if filename in _CONFIG_CACHE: - return _CONFIG_CACHE[filename] - - config_path = CONFIG_DIR / filename - - if not config_path.exists(): - logger.warning(f"Fallback config not found: {config_path}") - return None - - try: - with open(config_path, "r", encoding="utf-8") as f: - config = json.load(f) - - # Cache for future use - _CONFIG_CACHE[filename] = config - logger.debug(f"Loaded fallback config: {filename}") - return config - - except json.JSONDecodeError as e: - logger.error(f"Invalid JSON in fallback config {filename}: {e}") - return None - except Exception as e: - logger.error(f"Error loading fallback config {filename}: {e}") - return None - - -def list_available_configs() -> list[dict[str, Any]]: - """ - List all available built-in configs. - - Returns: - List of config info dictionaries - """ - configs = [] - - for json_file in CONFIG_DIR.glob("*.json"): - try: - config = load_config_file(json_file.name) - if config: - configs.append({ - "filename": json_file.name, - "id": config.get("id", json_file.stem), - "name": config.get("name", json_file.stem), - "version": config.get("version", "1.0.0"), - "tables": list(config.get("tables", {}).keys()), - }) - except Exception as e: - logger.warning(f"Error reading config {json_file}: {e}") - - return configs - - -def get_config_for_tables(table_names: list[str]) -> dict[str, Any] | None: - """ - Try to find a fallback config that matches the given table names. - - Args: - table_names: List of table names in the database - - Returns: - Best matching config or None - """ - if not table_names: - return None - - table_set = set(t.lower() for t in table_names) - best_match = None - best_score = 0 - - for json_file in CONFIG_DIR.glob("*.json"): - config = load_config_file(json_file.name) - if not config: - continue - - config_tables = set(t.lower() for t in config.get("tables", {}).keys()) - - # Calculate overlap score - intersection = len(table_set & config_tables) - if intersection > best_score: - best_score = intersection - best_match = config - - # Require at least 50% table match - if best_match and best_score >= len(table_set) * 0.5: - return best_match - - return None - - -def clear_cache() -> None: - """Clear the config cache (useful for testing).""" - _CONFIG_CACHE.clear() diff --git a/app/configs/genome_data_tables.json b/app/configs/genome_data_tables.json deleted file mode 100644 index 2ae7ed1..0000000 --- a/app/configs/genome_data_tables.json +++ /dev/null @@ -1,527 +0,0 @@ -{ - "id": "genome_data_tables", - "name": "Genome Data Tables", - "description": "Tables from KBase GenomeDataTables objects including genes, conditions, and experimental data", - "version": "1.0.0", - "icon": "bi-database", - "color": "#6366f1", - "defaults": { - "pageSize": 50, - "density": "default", - "showRowNumbers": true, - "enableSelection": true, - "enableExport": true - }, - "sharedCategories": [ - { - "id": "core", - "name": "Core Info", - "description": "Essential identifiers and names", - "icon": "bi-database", - "color": "#6366f1", - "defaultVisible": true, - "order": 1 - }, - { - "id": "functional", - "name": "Functional Annotation", - "description": "Function and product information", - "icon": "bi-gear", - "color": "#22c55e", - "defaultVisible": true, - "order": 2 - }, - { - "id": "external", - "name": "External Links", - "description": "Links to external databases", - "icon": "bi-box-arrow-up-right", - "color": "#06b6d4", - "defaultVisible": true, - "order": 3 - }, - { - "id": "sequence", - "name": "Sequence Data", - "description": "DNA, RNA, and protein sequences", - "icon": "bi-text-left", - "color": "#f59e0b", - "defaultVisible": true, - "order": 4 - }, - { - "id": "metadata", - "name": "System Metadata", - "description": "System tags, hashes, and sync info", - "icon": "bi-info-circle", - "color": "#64748b", - "defaultVisible": false, - "order": 10 - }, - { - "id": "status", - "name": "Status & Reports", - "description": "Error reports and validity status", - "icon": "bi-activity", - "color": "#ef4444", - "defaultVisible": true, - "order": 9 - } - ], - "tables": { - "Genes": { - "displayName": "Genes", - "description": "Gene annotations from the genome", - "icon": "bi-diagram-3", - "settings": { - "defaultSortColumn": "ID", - "defaultSortOrder": "asc" - }, - "columns": [ - { - "column": "ID", - "displayName": "Gene ID", - "dataType": "id", - "categories": [ - "core" - ], - "sortable": true, - "filterable": true, - "copyable": true, - "width": "120px", - "pin": "left" - }, - { - "column": "Database_ID", - "displayName": "DB Reference", - "dataType": "id", - "categories": [ - "core" - ], - "sortable": true, - "filterable": true, - "width": "130px" - }, - { - "column": "Primary_function", - "displayName": "Product / Function", - "dataType": "string", - "categories": [ - "functional" - ], - "sortable": true, - "filterable": true, - "searchable": true, - "width": "300px" - }, - { - "column": "Uniprot_ID", - "displayName": "UniProt", - "dataType": "id", - "categories": [ - "external" - ], - "sortable": true, - "filterable": true, - "width": "100px", - "transform": { - "type": "link", - "options": { - "urlTemplate": "https://www.uniprot.org/uniprotkb/{value}", - "target": "_blank", - "icon": "bi-link-45deg" - } - } - }, - { - "column": "GO_Terms", - "displayName": "GO Terms", - "dataType": "ontology", - "categories": [ - "functional" - ], - "sortable": false, - "filterable": true, - "width": "180px", - "transform": { - "type": "ontology", - "options": { - "prefix": "GO", - "urlTemplate": "https://amigo.geneontology.org/amigo/term/{value}", - "style": "badge" - } - } - }, - { - "column": "KEGG_ID", - "displayName": "KEGG", - "dataType": "id", - "categories": [ - "external" - ], - "sortable": true, - "width": "90px", - "transform": { - "type": "link", - "options": { - "urlTemplate": "https://www.genome.jp/entry/{value}", - "target": "_blank" - } - } - }, - { - "column": "Sequence", - "displayName": "Protein Sequence", - "dataType": "sequence", - "categories": [ - "sequence" - ], - "sortable": false, - "filterable": false, - "copyable": true, - "width": "150px", - "transform": { - "type": "sequence", - "options": { - "maxLength": 20, - "showCopyButton": true - } - } - } - ], - "virtualColumns": [ - { - "column": "Gene_Info", - "displayName": "Gene Summary", - "virtual": true, - "sourceColumns": [ - "ID", - "Primary_function" - ], - "compute": { - "type": "merge", - "template": "{ID}: {Primary_function}" - }, - "categories": [ - "core" - ], - "visible": false - } - ] - }, - "Conditions": { - "displayName": "Experimental Conditions", - "description": "Growth conditions and experimental parameters", - "icon": "bi-thermometer-half", - "categories": [ - { - "id": "experimental", - "name": "Experimental Parameters", - "icon": "bi-sliders", - "color": "#f59e0b", - "defaultVisible": true - }, - { - "id": "media", - "name": "Media Composition", - "icon": "bi-droplet", - "color": "#3b82f6", - "defaultVisible": true - } - ], - "columns": [ - { - "column": "Database_ID", - "displayName": "Condition ID", - "dataType": "id", - "categories": [ - "experimental" - ], - "sortable": true, - "filterable": true, - "copyable": true, - "width": "140px", - "pin": "left" - }, - { - "column": "Name", - "displayName": "Condition Name", - "dataType": "string", - "categories": [ - "experimental" - ], - "sortable": true, - "filterable": true, - "searchable": true, - "width": "200px" - }, - { - "column": "Temperature_in_C", - "displayName": "Temperature (°C)", - "dataType": "number", - "categories": [ - "experimental" - ], - "sortable": true, - "filterable": true, - "align": "right", - "width": "120px", - "transform": { - "type": "number", - "options": { - "decimals": 1, - "suffix": "°C" - } - } - }, - { - "column": "Agitation_Speed_in_RPM", - "displayName": "Agitation (RPM)", - "dataType": "integer", - "categories": [ - "experimental" - ], - "sortable": true, - "filterable": true, - "align": "right", - "width": "120px", - "transform": { - "type": "number", - "options": { - "decimals": 0, - "suffix": " RPM" - } - } - }, - { - "column": "Min_Media", - "displayName": "Base Media", - "dataType": "string", - "categories": [ - "media" - ], - "sortable": true, - "filterable": true, - "width": "120px" - }, - { - "column": "Carbon_Source", - "displayName": "Carbon Source", - "dataType": "string", - "categories": [ - "media" - ], - "sortable": true, - "filterable": true, - "width": "140px", - "transform": { - "type": "badge", - "options": { - "color": "#22c55e", - "variant": "subtle" - } - } - }, - { - "column": "Carbon_Concentration_in_mM", - "displayName": "Carbon (mM)", - "dataType": "float", - "categories": [ - "media" - ], - "sortable": true, - "filterable": true, - "align": "right", - "width": "100px", - "transform": { - "type": "number", - "options": { - "decimals": 2, - "suffix": " mM" - } - } - }, - { - "column": "Antibiotics", - "displayName": "Antibiotics", - "dataType": "string", - "categories": [ - "media" - ], - "sortable": true, - "filterable": true, - "width": "120px" - }, - { - "column": "AB_Concentration_in_mg_mL", - "displayName": "AB Conc. (mg/mL)", - "dataType": "float", - "categories": [ - "media" - ], - "sortable": true, - "filterable": true, - "align": "right", - "width": "120px", - "transform": { - "type": "number", - "options": { - "decimals": 3, - "suffix": " mg/mL" - } - } - }, - { - "column": "Supplements", - "displayName": "Supplements", - "dataType": "string", - "categories": [ - "media" - ], - "sortable": true, - "filterable": true, - "width": "150px" - } - ] - }, - "Expression": { - "displayName": "Expression Data", - "description": "Gene expression measurements", - "icon": "bi-graph-up", - "settings": { - "enableSelection": true, - "density": "compact" - }, - "categories": [ - { - "id": "gene", - "name": "Gene Info", - "icon": "bi-database", - "color": "#6366f1", - "defaultVisible": true - }, - { - "id": "expression", - "name": "Expression Values", - "icon": "bi-graph-up", - "color": "#ef4444", - "defaultVisible": true - }, - { - "id": "statistics", - "name": "Statistics", - "icon": "bi-calculator", - "color": "#8b5cf6", - "defaultVisible": true - } - ], - "columns": [ - { - "column": "Gene_ID", - "displayName": "Gene ID", - "dataType": "id", - "categories": [ - "gene" - ], - "sortable": true, - "filterable": true, - "copyable": true, - "width": "120px", - "pin": "left" - }, - { - "column": "Gene_Name", - "displayName": "Gene Name", - "dataType": "string", - "categories": [ - "gene" - ], - "sortable": true, - "filterable": true, - "width": "120px" - }, - { - "column": "Log2FC", - "displayName": "Log2 Fold Change", - "dataType": "float", - "categories": [ - "expression" - ], - "sortable": true, - "filterable": true, - "align": "right", - "width": "130px", - "transform": { - "type": "heatmap", - "options": { - "min": -4, - "max": 4, - "colorScale": "diverging", - "showValue": true, - "decimals": 2 - } - } - }, - { - "column": "P_Value", - "displayName": "P-Value", - "dataType": "float", - "categories": [ - "statistics" - ], - "sortable": true, - "filterable": true, - "align": "right", - "width": "100px", - "transform": { - "type": "number", - "options": { - "notation": "scientific", - "decimals": 2 - } - } - }, - { - "column": "FDR", - "displayName": "FDR", - "dataType": "float", - "categories": [ - "statistics" - ], - "sortable": true, - "filterable": true, - "align": "right", - "width": "100px", - "transform": { - "type": "number", - "options": { - "notation": "scientific", - "decimals": 2 - } - } - }, - { - "column": "Significant", - "displayName": "Significant", - "dataType": "boolean", - "categories": [ - "statistics" - ], - "sortable": true, - "filterable": true, - "align": "center", - "width": "90px", - "transform": { - "type": "boolean", - "options": { - "trueIcon": "bi-check-circle-fill", - "falseIcon": "bi-x-circle", - "trueColor": "#22c55e", - "falseColor": "#94a3b8" - } - } - } - ] - } - } -} \ No newline at end of file diff --git a/app/db/__init__.py b/app/db/__init__.py new file mode 100644 index 0000000..3038256 --- /dev/null +++ b/app/db/__init__.py @@ -0,0 +1,5 @@ +""" +Database module for Config Control Plane. + +Provides SQLite-based persistent storage for configuration records. +""" diff --git a/app/db/schema.sql b/app/db/schema.sql new file mode 100644 index 0000000..db58d0a --- /dev/null +++ b/app/db/schema.sql @@ -0,0 +1,107 @@ +-- ============================================================================= +-- Config Control Plane Database Schema +-- ============================================================================= +-- +-- Stores configuration records with full lifecycle support: +-- - draft: Work in progress, modifiable +-- - proposed: Ready for review, read-only +-- - published: Production-ready, locked +-- - deprecated: Marked for removal +-- - archived: Historical reference +-- +-- ============================================================================= + +-- Config records with full lifecycle support +CREATE TABLE IF NOT EXISTS config_records ( + id TEXT PRIMARY KEY, + source_type TEXT NOT NULL CHECK(source_type IN ('object', 'handle', 'builtin', 'custom')), + source_ref TEXT NOT NULL, + fingerprint TEXT, + version INTEGER NOT NULL DEFAULT 1, + + -- Lifecycle + state TEXT NOT NULL DEFAULT 'draft' CHECK(state IN ('draft', 'proposed', 'published', 'deprecated', 'archived')), + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + created_by TEXT NOT NULL, + published_at TIMESTAMP, + published_by TEXT, + + -- Content + config_json TEXT NOT NULL, -- Full DataTypeConfig JSON + extends_id TEXT REFERENCES config_records(id), + overlays_json TEXT, + + -- Metadata + object_type TEXT, + ai_provider TEXT, + confidence REAL DEFAULT 1.0, + generation_time_ms REAL, + + -- Audit + change_summary TEXT, + change_author TEXT, + + -- Unique constraint on source_ref + fingerprint + version + UNIQUE(source_ref, fingerprint, version) +); + +-- Audit log for all changes +CREATE TABLE IF NOT EXISTS config_audit_log ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + config_id TEXT NOT NULL REFERENCES config_records(id) ON DELETE CASCADE, + action TEXT NOT NULL, + old_state TEXT, + new_state TEXT, + changed_by TEXT NOT NULL, + changed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + diff_json TEXT, + reason TEXT +); + +-- User overrides for personalized config preferences +CREATE TABLE IF NOT EXISTS user_config_overrides ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + user_id TEXT NOT NULL, + source_ref TEXT NOT NULL, + override_config_json TEXT NOT NULL, -- Partial or full config override + priority INTEGER DEFAULT 100, -- Lower = higher priority + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + is_active BOOLEAN DEFAULT 1, + UNIQUE(user_id, source_ref) +); + +-- Config version history for diff visualization +CREATE TABLE IF NOT EXISTS config_version_history ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + config_id TEXT NOT NULL REFERENCES config_records(id) ON DELETE CASCADE, + version INTEGER NOT NULL, + config_json TEXT NOT NULL, + snapshot_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + UNIQUE(config_id, version) +); + +-- Config test results for validation against real data +CREATE TABLE IF NOT EXISTS config_test_results ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + config_id TEXT NOT NULL REFERENCES config_records(id) ON DELETE CASCADE, + test_type TEXT NOT NULL, -- 'schema', 'data', 'performance', 'integration' + test_status TEXT NOT NULL, -- 'passed', 'failed', 'warning' + test_details_json TEXT, -- Detailed test results + tested_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + tested_by TEXT, + execution_time_ms REAL +); + +-- Indexes for fast lookups +CREATE INDEX IF NOT EXISTS idx_config_source ON config_records(source_type, source_ref); +CREATE INDEX IF NOT EXISTS idx_config_state ON config_records(state); +CREATE INDEX IF NOT EXISTS idx_config_fingerprint ON config_records(fingerprint); +CREATE INDEX IF NOT EXISTS idx_config_object_type ON config_records(object_type); +CREATE INDEX IF NOT EXISTS idx_config_extends ON config_records(extends_id); +CREATE INDEX IF NOT EXISTS idx_audit_config_id ON config_audit_log(config_id); +CREATE INDEX IF NOT EXISTS idx_audit_changed_at ON config_audit_log(changed_at); +CREATE INDEX IF NOT EXISTS idx_user_override_user ON user_config_overrides(user_id, source_ref); +CREATE INDEX IF NOT EXISTS idx_version_history_config ON config_version_history(config_id, version); +CREATE INDEX IF NOT EXISTS idx_test_results_config ON config_test_results(config_id, test_type); \ No newline at end of file diff --git a/app/main.py b/app/main.py index 6c0d26f..8067d88 100644 --- a/app/main.py +++ b/app/main.py @@ -37,16 +37,25 @@ def create_app() -> FastAPI: description = """ ## TableScanner API - A FastAPI service for querying BERDL table data from KBase. + A FastAPI service for querying tabular data from KBase with AI-powered + configuration generation for DataTables Viewer. ### Features - - List pangenomes from BERDLTables objects - - List tables within a pangenome + - List tables in KBase objects - Query table data with filtering, sorting, and pagination - Local caching for performance + - **AI-Powered Config Generation**: Automatically generates DataTables Viewer configs for new data types + - **Config Registry**: Tracks which configs exist to avoid regeneration + - **Viewer Integration**: Sends generated configs to DataTables Viewer for storage ### Authentication Pass your KBase auth token in the `Authorization` header. + + ### Config Generation + - **AI Generation**: Automatically generates configs for new KBase data types + - **Registry Tracking**: Tracks which object types have configs + - **Viewer Storage**: Configs are sent to and stored in DataTables Viewer + - **Developer Editing**: Configs can be edited in DataTables Viewer """ tags_metadata = [ @@ -62,6 +71,10 @@ def create_app() -> FastAPI: "name": "Handle Access", "description": "API endpoints for accessing data via Blobstore handle references (KBH_...).", }, + { + "name": "Config Generation", + "description": "AI-powered generation of DataTables Viewer configurations from database schemas.", + }, { "name": "Cache Management", "description": "Operations for managing and inspecting the local SQLite cache.", diff --git a/app/models.py b/app/models.py index 29925b7..f11b3e7 100644 --- a/app/models.py +++ b/app/models.py @@ -1,4 +1,6 @@ from __future__ import annotations +from datetime import datetime +from enum import Enum from typing import Any, Literal from pydantic import BaseModel, Field @@ -439,4 +441,248 @@ class ProviderStatusResponse(BaseModel): name: str = Field(..., description="Provider name") available: bool = Field(..., description="Whether provider is available") priority: int = Field(..., description="Provider priority (lower = higher)") - error: str | None = Field(None, description="Error message if unavailable") \ No newline at end of file + error: str | None = Field(None, description="Error message if unavailable") + + +# ============================================================================= +# CONFIG CONTROL PLANE MODELS +# ============================================================================= + + +class ConfigState(str, Enum): + """Lifecycle states for configuration records.""" + DRAFT = "draft" + PROPOSED = "proposed" + PUBLISHED = "published" + DEPRECATED = "deprecated" + ARCHIVED = "archived" + + +class ConfigSourceType(str, Enum): + """Types of configuration sources.""" + OBJECT = "object" + HANDLE = "handle" + BUILTIN = "builtin" + CUSTOM = "custom" + + +class ConfigCreateRequest(BaseModel): + """Request to create a new configuration.""" + source_type: ConfigSourceType = Field(..., description="Type of source") + source_ref: str = Field(..., description="Reference (UPA, handle, or ID)") + config: dict = Field(..., description="Full DataTypeConfig JSON") + extends_id: str | None = Field(None, description="Parent config ID to inherit from") + change_summary: str = Field("Initial creation", description="Description of changes") + object_type: str | None = Field(None, description="KBase object type") + fingerprint: str | None = Field(None, description="Database fingerprint") + + +class ConfigUpdateRequest(BaseModel): + """Request to update an existing draft configuration.""" + config: dict | None = Field(None, description="Updated config (full replacement)") + overlays: dict | None = Field(None, description="Delta overlays to merge") + change_summary: str = Field(..., description="Description of changes") + + +class ConfigRecord(BaseModel): + """Full configuration record from database.""" + id: str = Field(..., description="Unique config ID") + source_type: ConfigSourceType = Field(..., description="Type of source") + source_ref: str = Field(..., description="Source reference") + fingerprint: str | None = Field(None, description="Database fingerprint") + version: int = Field(1, description="Version number") + state: ConfigState = Field(ConfigState.DRAFT, description="Lifecycle state") + created_at: datetime = Field(..., description="Creation timestamp") + updated_at: datetime = Field(..., description="Last update timestamp") + created_by: str = Field(..., description="Creator identifier") + published_at: datetime | None = Field(None, description="Publication timestamp") + published_by: str | None = Field(None, description="Publisher identifier") + config: dict = Field(..., description="Full DataTypeConfig JSON") + extends_id: str | None = Field(None, description="Parent config ID") + overlays: dict | None = Field(None, description="Delta overlays from parent") + object_type: str | None = Field(None, description="KBase object type") + ai_provider: str | None = Field(None, description="AI provider that generated config") + confidence: float = Field(1.0, ge=0.0, le=1.0, description="Confidence score") + generation_time_ms: float | None = Field(None, description="Generation time in ms") + change_summary: str | None = Field(None, description="Latest change summary") + change_author: str | None = Field(None, description="Latest change author") + + +class ConfigListResponse(BaseModel): + """Paginated response for listing configurations.""" + configs: list[ConfigRecord] = Field(default_factory=list) + total: int = Field(..., description="Total number of matching configs") + page: int = Field(1, ge=1, description="Current page number") + per_page: int = Field(20, ge=1, le=100, description="Items per page") + + +class ConfigResolveResponse(BaseModel): + """Response from config resolution endpoint.""" + config: dict = Field(..., description="Resolved DataTypeConfig") + source: Literal["user_override", "published", "generated", "builtin", "default"] = Field( + ..., description="Resolution source" + ) + config_id: str | None = Field(None, description="Config record ID if from database") + fingerprint: str | None = Field(None, description="Database fingerprint") + version: int | None = Field(None, description="Config version") + object_type: str | None = Field(None, description="KBase object type") + resolution_time_ms: float = Field(..., description="Resolution time in ms") + + +class AIProposalRequest(BaseModel): + """AI agent proposal for configuration changes.""" + intent: str = Field(..., description="Natural language description of intent") + target_config_id: str | None = Field(None, description="Existing config ID to modify") + target_source_ref: str | None = Field(None, description="Source ref for new config") + target_tables: list[str] = Field(default_factory=list, description="Tables to affect") + proposed_changes: dict = Field(..., description="Proposed config or overlay") + reasoning: str = Field("", description="AI reasoning for changes") + confidence: float = Field(1.0, ge=0.0, le=1.0, description="AI confidence") + requires_human_review: bool = Field(True, description="AI self-assessment") + + +class AIProposalResponse(BaseModel): + """Response to AI config proposal.""" + status: Literal["accepted", "needs_revision", "rejected"] = Field( + ..., description="Proposal status" + ) + proposal_id: str = Field(..., description="Unique proposal ID for tracking") + config_id: str | None = Field(None, description="Created/updated config ID") + validation_errors: list[str] = Field(default_factory=list, description="Validation issues") + suggestions: list[str] = Field(default_factory=list, description="Improvement suggestions") + diff_summary: str | None = Field(None, description="Summary of changes") + + +class ConfigValidationRequest(BaseModel): + """Request to validate a configuration.""" + config: dict = Field(..., description="Config to validate") + strict: bool = Field(False, description="Enable strict validation") + + +class ConfigValidationResponse(BaseModel): + """Response from config validation.""" + valid: bool = Field(..., description="Whether config is valid") + errors: list[str] = Field(default_factory=list, description="Validation errors") + warnings: list[str] = Field(default_factory=list, description="Validation warnings") + + +# ============================================================================= +# USER OVERRIDES MODELS +# ============================================================================= + + +class UserOverrideRequest(BaseModel): + """Request to set a user override.""" + source_ref: str = Field(..., description="Source reference") + override_config: dict = Field(..., description="Partial or full config override") + priority: int = Field(100, ge=1, le=1000, description="Override priority (lower = higher)") + + +class UserOverrideResponse(BaseModel): + """Response for user override operations.""" + user_id: str = Field(..., description="User identifier") + source_ref: str = Field(..., description="Source reference") + override_config: dict = Field(..., description="Override configuration") + priority: int = Field(..., description="Override priority") + created_at: datetime = Field(..., description="Creation timestamp") + updated_at: datetime = Field(..., description="Last update timestamp") + + +# ============================================================================= +# CONFIG DIFF MODELS +# ============================================================================= + + +class ConfigDiffRequest(BaseModel): + """Request to diff two configs.""" + config_id1: str = Field(..., description="First config ID") + config_id2: str | None = Field(None, description="Second config ID (or use version)") + version1: int | None = Field(None, description="First version number") + version2: int | None = Field(None, description="Second version number") + + +class ConfigDiffResponse(BaseModel): + """Response from config diff.""" + added: dict = Field(default_factory=dict, description="Added fields") + removed: dict = Field(default_factory=dict, description="Removed fields") + modified: dict = Field(default_factory=dict, description="Modified fields") + unchanged: dict = Field(default_factory=dict, description="Unchanged fields") + summary: str = Field(..., description="Human-readable summary") + has_changes: bool = Field(..., description="Whether any changes exist") + + +# ============================================================================= +# CONFIG TESTING MODELS +# ============================================================================= + + +class ConfigTestRequest(BaseModel): + """Request to test a configuration.""" + config_id: str = Field(..., description="Config to test") + test_types: list[Literal["schema", "data", "performance", "integration"]] = Field( + default_factory=lambda: ["schema", "data", "performance"], + description="Types of tests to run" + ) + db_path: str | None = Field(None, description="Path to test database (optional)") + + +class TestResultDetail(BaseModel): + """Individual test result.""" + test_type: Literal["schema", "data", "performance", "integration"] = Field(..., description="Test type") + status: Literal["passed", "failed", "warning"] = Field(..., description="Test status") + details: dict = Field(default_factory=dict, description="Test details") + execution_time_ms: float = Field(..., description="Execution time") + errors: list[str] = Field(default_factory=list, description="Errors found") + warnings: list[str] = Field(default_factory=list, description="Warnings found") + + +class ConfigTestResponse(BaseModel): + """Response from config testing.""" + config_id: str = Field(..., description="Tested config ID") + results: list[TestResultDetail] = Field(..., description="Test results") + overall_status: Literal["passed", "failed", "warning"] = Field(..., description="Overall status") + total_time_ms: float = Field(..., description="Total test execution time") + + +# ============================================================================= +# DEVELOPER CONFIG MODELS +# ============================================================================= + + +class DeveloperConfigInfo(BaseModel): + """Information about a developer-editable config file.""" + filename: str = Field(..., description="Config filename") + config_id: str = Field(..., description="Config ID") + name: str = Field(..., description="Config name") + version: str = Field(..., description="Config version") + object_types: list[str] = Field(default_factory=list, description="Matching object types") + sync_status: dict = Field(..., description="Sync status with Control Plane") + last_modified: str = Field(..., description="File last modified timestamp") + file_path: str = Field(..., description="Full file path") + + +class DeveloperConfigUpdateRequest(BaseModel): + """Request to update a developer config.""" + config: dict = Field(..., description="Updated config JSON") + sync_to_control_plane: bool = Field(True, description="Sync to Control Plane after update") + auto_publish: bool = Field(False, description="Auto-publish after sync") + + +class DeveloperConfigSyncResponse(BaseModel): + """Response from config sync operation.""" + status: Literal["synced", "unchanged", "error"] = Field(..., description="Sync status") + config_id: str | None = Field(None, description="Config ID in Control Plane") + state: str | None = Field(None, description="Config state") + version: int | None = Field(None, description="Config version") + message: str = Field(..., description="Status message") + + +class DeveloperConfigPreviewResponse(BaseModel): + """Response from config preview.""" + filename: str = Field(..., description="Config filename") + config: dict = Field(..., description="Config JSON") + object_types: list[str] = Field(default_factory=list, description="Matching object types") + sync_status: dict = Field(..., description="Sync status") + tables: list[str] = Field(default_factory=list, description="Table names") + table_count: int = Field(..., description="Number of tables") + resolution: dict | None = Field(None, description="Resolution preview if source_ref provided") \ No newline at end of file diff --git a/app/routes.py b/app/routes.py index 12babea..953392c 100644 --- a/app/routes.py +++ b/app/routes.py @@ -412,7 +412,7 @@ async def list_tables_by_object( config_url = None has_cached_config = False try: - from app.services.fingerprint import DatabaseFingerprint + from app.services.data.fingerprint import DatabaseFingerprint fp_service = DatabaseFingerprint() safe_ref = berdl_table_id.replace("/", "_").replace(":", "_") fingerprint = fp_service.compute(db_path) @@ -427,13 +427,9 @@ async def list_tables_by_object( # Check for builtin fallback config has_builtin_config = False + # Configs are now stored in DataTables Viewer, not here + has_builtin_config = False builtin_config_id = None - try: - from app.configs import has_fallback_config, get_fallback_config_id - has_builtin_config = has_fallback_config(object_type) - builtin_config_id = get_fallback_config_id(object_type) - except Exception as e: - logger.debug(f"Fallback config check: {e}") # Get database size database_size = None @@ -769,7 +765,7 @@ async def generate_viewer_config( cache_dir = get_cache_dir() # Import config generator - from app.services.config_generator import ConfigGenerator + from app.services.config.config_generator import ConfigGenerator # Get database path (using existing handle logic) client = KBaseClient(token, kb_env, cache_dir) @@ -826,7 +822,7 @@ async def get_generated_config(fingerprint: str): ``` """ try: - from app.services.fingerprint import DatabaseFingerprint + from app.services.data.fingerprint import DatabaseFingerprint fp = DatabaseFingerprint() config = fp.get_cached_config(fingerprint) @@ -864,7 +860,7 @@ async def list_ai_providers(): ``` """ try: - from app.services.ai_provider import list_ai_providers + from app.services.ai.ai_provider import list_ai_providers providers = list_ai_providers() return [ @@ -893,7 +889,7 @@ async def list_cached_configs(): ``` """ try: - from app.services.fingerprint import DatabaseFingerprint + from app.services.data.fingerprint import DatabaseFingerprint fp = DatabaseFingerprint() cached = fp.list_cached() @@ -921,7 +917,7 @@ async def delete_cached_config(fingerprint: str): ``` """ try: - from app.services.fingerprint import DatabaseFingerprint + from app.services.data.fingerprint import DatabaseFingerprint fp = DatabaseFingerprint() deleted = fp.clear_cache(fingerprint) @@ -991,7 +987,7 @@ async def generate_config_for_object( object_type = None # Generate config - from app.services.config_generator import ConfigGenerator + from app.services.config.config_generator import ConfigGenerator generator = ConfigGenerator() @@ -1008,94 +1004,79 @@ async def generate_config_for_object( except Exception as e: logger.warning(f"Error building schema: {e}") - # Try AI generation with fallback cascade - fallback_used = False - fallback_reason = None - config_source = "rules" - ai_error = None - ai_available = True + # Check if config already exists in DataTables Viewer + from app.services.config_registry import get_config_registry + from app.services.viewer_client import get_viewer_client + + registry = get_config_registry() + viewer = get_viewer_client() + + # Check registry first + if not force_regenerate and registry.has_config(object_type): + # Verify with viewer + if viewer.check_config_exists(object_type): + logger.info(f"Config already exists for {object_type}, skipping generation") + return ConfigGenerationResponse( + status="exists", + fingerprint="", + config_url="", + config={}, + tables_analyzed=0, + columns_inferred=0, + ai_provider_used=None, + generation_time_ms=0, + cache_hit=True, + ) + else: + # Registry says it exists but viewer doesn't - update registry + registry.mark_no_config(object_type) + # Generate config with AI try: result = generator.generate( db_path=db_path, handle_ref=berdl_table_id, - force_regenerate=force_regenerate, - ai_preference="argo", # Argo-only strategy + force_regenerate=True, # Always generate fresh for new configs + ai_preference="argo", ) - config_source = "ai" if result.ai_provider_used else "rules" - except Exception as gen_error: - logger.warning(f"Config generation failed, trying fallback: {gen_error}") - ai_error = str(gen_error) - ai_available = False + # Add object type to config + if object_type and "objectType" not in result.config: + result.config["objectType"] = object_type - # Try builtin fallback - from app.configs import get_fallback_config, get_fallback_config_id - fallback_config = get_fallback_config(object_type) - - if fallback_config: - fallback_used = True - fallback_reason = "generation_failed" - config_source = "builtin" - - # Create mock result - from dataclasses import dataclass - @dataclass - class MockResult: - config: dict - fingerprint: str - cache_hit: bool = False - tables_analyzed: int = 0 - columns_inferred: int = 0 - ai_provider_used: str | None = None - generation_time_ms: float = 0.0 - - safe_ref = berdl_table_id.replace("/", "_").replace(":", "_") - result = MockResult( - config=fallback_config, - fingerprint=f"{safe_ref}_fallback_{get_fallback_config_id(object_type)}", - ) - else: - # No fallback available - return error - raise HTTPException( - status_code=500, - detail=f"Config generation failed and no fallback available: {gen_error}" + # Send config to DataTables Viewer + try: + viewer.send_config( + object_type=object_type, + source_ref=berdl_table_id, + config=result.config ) - - # Add object type to config if available - if object_type and "objectType" not in result.config: - result.config["objectType"] = object_type - - # Determine status - if fallback_used: - status = "fallback" - elif result.cache_hit: - status = "cached" - else: - status = "generated" - - return ConfigGenerationResponse( - status=status, - fingerprint=result.fingerprint, - config_url=f"/config/generated/{result.fingerprint}", - config=result.config, - fallback_used=fallback_used, - fallback_reason=fallback_reason, - config_source=config_source, - db_schema=schema if schema else None, - table_schemas=table_schemas if table_schemas else None, - tables_analyzed=result.tables_analyzed, - columns_inferred=result.columns_inferred, - total_rows=total_rows, - ai_provider_used=result.ai_provider_used, - ai_available=ai_available, - ai_error=ai_error, - generation_time_ms=result.generation_time_ms, - cache_hit=result.cache_hit, - object_type=object_type, - object_ref=berdl_table_id, - api_version="2.0", - ) + # Mark as having config + registry.mark_has_config(object_type) + status = "generated_and_sent" + except Exception as e: + logger.error(f"Failed to send config to viewer: {e}") + # Still return the config even if viewer send failed + status = "generated_but_send_failed" + + return ConfigGenerationResponse( + status=status, + fingerprint=result.fingerprint, + config_url="", + config=result.config, + tables_analyzed=result.tables_analyzed, + columns_inferred=result.columns_inferred, + ai_provider_used=result.ai_provider_used, + generation_time_ms=result.generation_time_ms, + cache_hit=False, + ) + + except Exception as gen_error: + logger.error(f"Config generation failed: {gen_error}") + raise HTTPException( + status_code=500, + detail=f"Config generation failed: {gen_error}" + ) except HTTPException: raise @@ -1135,23 +1116,12 @@ async def get_config_for_object( kb_env=kb_env ) - # Compute fingerprint and check cache - from app.services.fingerprint import DatabaseFingerprint - - fp_service = DatabaseFingerprint() - safe_ref = berdl_table_id.replace("/", "_").replace(":", "_") - schema_fp = fp_service.compute(db_path) - fingerprint = f"{safe_ref}_{schema_fp}" - - config = fp_service.get_cached_config(fingerprint) - - if config is None: - raise HTTPException( - status_code=404, - detail=f"No cached config for object {ws_ref}. Use POST /object/{ws_ref}/config/generate to create one." - ) - - return config + # Configs are now stored in DataTables Viewer + # This endpoint is deprecated - configs should be retrieved from viewer + raise HTTPException( + status_code=404, + detail=f"Configs are now stored in DataTables Viewer. Use POST /object/{ws_ref}/config/generate to create one, then retrieve from viewer." + ) except HTTPException: raise diff --git a/app/services/__init__.py b/app/services/__init__.py index 2b0fb1e..f2394fc 100644 --- a/app/services/__init__.py +++ b/app/services/__init__.py @@ -11,9 +11,9 @@ - fingerprint: Database fingerprinting for caching """ -from .type_inference import TypeInferenceEngine, InferredType, DataType -from .schema_analyzer import SchemaAnalyzer, ColumnProfile, TableProfile -from .ai_provider import ( +from .data.type_inference import TypeInferenceEngine, InferredType, DataType +from .data.schema_analyzer import SchemaAnalyzer, ColumnProfile, TableProfile +from .ai.ai_provider import ( AIProvider, AIProviderFactory, get_ai_provider, @@ -21,10 +21,10 @@ ColumnInference, ProviderStatus, ) -from .fingerprint import DatabaseFingerprint -from .config_generator import ConfigGenerator, GenerationResult -from .prompts import build_table_config_prompt, detect_value_patterns, compute_numeric_stats -from .validation import validate_config, validate_table_config, validate_ai_response, sanitize_config +from .data.fingerprint import DatabaseFingerprint +from .config.config_generator import ConfigGenerator, GenerationResult +from .ai.prompts import build_table_config_prompt, detect_value_patterns, compute_numeric_stats +from .data.validation import validate_config, validate_table_config, validate_ai_response, sanitize_config __all__ = [ # Type inference diff --git a/app/services/ai/__init__.py b/app/services/ai/__init__.py new file mode 100644 index 0000000..16e79bc --- /dev/null +++ b/app/services/ai/__init__.py @@ -0,0 +1,14 @@ +""" +AI Services. + +AI-powered config generation and inference. +""" + +from .ai_provider import AIProvider, list_ai_providers +from ..config.config_generator import ConfigGenerator + +__all__ = [ + "AIProvider", + "list_ai_providers", + "ConfigGenerator", +] diff --git a/app/services/ai_provider.py b/app/services/ai/ai_provider.py similarity index 99% rename from app/services/ai_provider.py rename to app/services/ai/ai_provider.py index 8d6e1f1..b7cc7e5 100644 --- a/app/services/ai_provider.py +++ b/app/services/ai/ai_provider.py @@ -22,8 +22,8 @@ from pathlib import Path from typing import Any, Literal -from .schema_analyzer import ColumnProfile, TableProfile -from .type_inference import DataType, InferredType, TransformConfig, TypeInferenceEngine +from ..data.schema_analyzer import ColumnProfile, TableProfile +from ..data.type_inference import DataType, InferredType, TransformConfig, TypeInferenceEngine logger = logging.getLogger(__name__) diff --git a/app/services/prompts.py b/app/services/ai/prompts.py similarity index 100% rename from app/services/prompts.py rename to app/services/ai/prompts.py diff --git a/app/services/config/__init__.py b/app/services/config/__init__.py new file mode 100644 index 0000000..7190ea4 --- /dev/null +++ b/app/services/config/__init__.py @@ -0,0 +1,12 @@ +""" +Config Generation Service. + +AI-powered config generation for DataTables Viewer. +""" + +from .config_generator import ConfigGenerator, GenerationResult + +__all__ = [ + "ConfigGenerator", + "GenerationResult", +] diff --git a/app/services/config_generator.py b/app/services/config/config_generator.py similarity index 98% rename from app/services/config_generator.py rename to app/services/config/config_generator.py index 3013223..f16fa03 100644 --- a/app/services/config_generator.py +++ b/app/services/config/config_generator.py @@ -14,9 +14,9 @@ from pathlib import Path from typing import Any, Literal -from .ai_provider import AIProvider, ColumnInference, get_ai_provider -from .schema_analyzer import SchemaAnalyzer, TableProfile -from .fingerprint import DatabaseFingerprint +from ..ai.ai_provider import AIProvider, ColumnInference, get_ai_provider +from ..data.schema_analyzer import SchemaAnalyzer, TableProfile +from ..data.fingerprint import DatabaseFingerprint logger = logging.getLogger(__name__) diff --git a/app/services/config_registry.py b/app/services/config_registry.py new file mode 100644 index 0000000..e7e4351 --- /dev/null +++ b/app/services/config_registry.py @@ -0,0 +1,119 @@ +""" +Simple Config Registry. + +Tracks which object types have configs in DataTables Viewer. +Used to avoid regenerating configs that already exist. +""" + +from __future__ import annotations + +import json +import logging +import sqlite3 +from pathlib import Path +from typing import Any + +from app.config import settings + +logger = logging.getLogger(__name__) + + +class ConfigRegistry: + """ + Simple registry tracking which object types have configs. + + This is just a tracking mechanism - actual configs are stored + in DataTables Viewer. We only track what exists to avoid + regenerating configs. + """ + + def __init__(self, db_path: Path | None = None): + """Initialize registry.""" + self.db_path = db_path or Path(settings.CACHE_DIR) / "config_registry.db" + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self._init_db() + + def _init_db(self) -> None: + """Initialize database schema.""" + schema_sql = """ + CREATE TABLE IF NOT EXISTS config_registry ( + object_type TEXT PRIMARY KEY, + has_config BOOLEAN DEFAULT 1, + last_checked TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ); + CREATE INDEX IF NOT EXISTS idx_config_registry_type ON config_registry(object_type); + """ + with sqlite3.connect(self.db_path) as conn: + conn.executescript(schema_sql) + logger.debug(f"Initialized config registry at {self.db_path}") + + def has_config(self, object_type: str) -> bool: + """ + Check if object type has a config in DataTables Viewer. + + Args: + object_type: KBase object type (e.g., "KBaseGeneDataLakes.BERDLTables-1.0") + + Returns: + True if config exists, False otherwise + """ + with sqlite3.connect(self.db_path) as conn: + conn.row_factory = sqlite3.Row + cursor = conn.execute( + "SELECT has_config FROM config_registry WHERE object_type = ?", + (object_type,) + ) + row = cursor.fetchone() + if row: + return bool(row["has_config"]) + return False + + def mark_has_config(self, object_type: str) -> None: + """ + Mark that object type has a config. + + Args: + object_type: KBase object type + """ + with sqlite3.connect(self.db_path) as conn: + conn.execute( + """INSERT OR REPLACE INTO config_registry + (object_type, has_config, last_checked) + VALUES (?, 1, CURRENT_TIMESTAMP)""", + (object_type,) + ) + logger.debug(f"Marked {object_type} as having config") + + def mark_no_config(self, object_type: str) -> None: + """ + Mark that object type does not have a config. + + Args: + object_type: KBase object type + """ + with sqlite3.connect(self.db_path) as conn: + conn.execute( + """INSERT OR REPLACE INTO config_registry + (object_type, has_config, last_checked) + VALUES (?, 0, CURRENT_TIMESTAMP)""", + (object_type,) + ) + logger.debug(f"Marked {object_type} as not having config") + + def list_registered_types(self) -> list[str]: + """List all registered object types.""" + with sqlite3.connect(self.db_path) as conn: + cursor = conn.execute("SELECT object_type FROM config_registry WHERE has_config = 1") + return [row[0] for row in cursor.fetchall()] + + +# Singleton instance +_registry: ConfigRegistry | None = None + + +def get_config_registry() -> ConfigRegistry: + """Get or create the singleton ConfigRegistry instance.""" + global _registry + if _registry is None: + _registry = ConfigRegistry() + return _registry diff --git a/app/services/data/__init__.py b/app/services/data/__init__.py new file mode 100644 index 0000000..dd828f1 --- /dev/null +++ b/app/services/data/__init__.py @@ -0,0 +1,19 @@ +""" +Data Analysis Services. + +Schema analysis, fingerprinting, and validation. +""" + +from .schema_analyzer import SchemaAnalyzer +from .fingerprint import DatabaseFingerprint +from .type_inference import TypeInferenceEngine, InferredType, DataType +from .validation import validate_config + +__all__ = [ + "SchemaAnalyzer", + "DatabaseFingerprint", + "TypeInferenceEngine", + "InferredType", + "DataType", + "validate_config", +] diff --git a/app/services/fingerprint.py b/app/services/data/fingerprint.py similarity index 100% rename from app/services/fingerprint.py rename to app/services/data/fingerprint.py diff --git a/app/services/schema_analyzer.py b/app/services/data/schema_analyzer.py similarity index 100% rename from app/services/schema_analyzer.py rename to app/services/data/schema_analyzer.py diff --git a/app/services/type_inference.py b/app/services/data/type_inference.py similarity index 100% rename from app/services/type_inference.py rename to app/services/data/type_inference.py diff --git a/app/services/validation.py b/app/services/data/validation.py similarity index 100% rename from app/services/validation.py rename to app/services/data/validation.py diff --git a/app/services/viewer_client.py b/app/services/viewer_client.py new file mode 100644 index 0000000..2359174 --- /dev/null +++ b/app/services/viewer_client.py @@ -0,0 +1,116 @@ +""" +DataTables Viewer Client. + +Sends generated configs to DataTables Viewer for storage. +""" + +from __future__ import annotations + +import logging +import httpx +from typing import Any + +from app.config import settings + +logger = logging.getLogger(__name__) + + +class ViewerClient: + """ + Client for sending configs to DataTables Viewer. + + When AI generates a config, it's sent to DataTables Viewer + which stores and manages it. + """ + + def __init__(self, base_url: str | None = None): + """ + Initialize viewer client. + + Args: + base_url: DataTables Viewer API base URL + """ + self.base_url = base_url or getattr(settings, "VIEWER_API_URL", "http://localhost:3000/api") + self.timeout = 30.0 + + def send_config( + self, + object_type: str, + source_ref: str, + config: dict[str, Any] + ) -> dict[str, Any]: + """ + Send generated config to DataTables Viewer. + + Args: + object_type: KBase object type + source_ref: Source reference (e.g., "76990/7/2") + config: Generated config JSON + + Returns: + Response from viewer API + + Raises: + Exception: If viewer API call fails + """ + url = f"{self.base_url}/configs" + + payload = { + "object_type": object_type, + "source_ref": source_ref, + "config": config, + "source": "ai_generated" + } + + try: + with httpx.Client(timeout=self.timeout) as client: + response = client.post(url, json=payload) + response.raise_for_status() + result = response.json() + logger.info(f"Sent config to viewer for {object_type}") + return result + except httpx.RequestError as e: + logger.error(f"Failed to send config to viewer: {e}") + raise Exception(f"Viewer API error: {e}") + except httpx.HTTPStatusError as e: + logger.error(f"Viewer API returned error: {e.response.status_code}") + raise Exception(f"Viewer API error: {e.response.status_code}") + + def check_config_exists(self, object_type: str) -> bool: + """ + Check if config exists in DataTables Viewer. + + Args: + object_type: KBase object type + + Returns: + True if config exists, False otherwise + """ + url = f"{self.base_url}/configs/check" + params = {"object_type": object_type} + + try: + with httpx.Client(timeout=self.timeout) as client: + response = client.get(url, params=params) + if response.status_code == 404: + return False + response.raise_for_status() + result = response.json() + return result.get("exists", False) + except httpx.RequestError: + logger.warning(f"Could not check config existence in viewer for {object_type}") + return False + except httpx.HTTPStatusError: + return False + + +# Singleton instance +_viewer_client: ViewerClient | None = None + + +def get_viewer_client() -> ViewerClient: + """Get or create the singleton ViewerClient instance.""" + global _viewer_client + if _viewer_client is None: + _viewer_client = ViewerClient() + return _viewer_client diff --git a/docs/API_EXAMPLES.md b/docs/API_EXAMPLES.md new file mode 100644 index 0000000..8eafd78 --- /dev/null +++ b/docs/API_EXAMPLES.md @@ -0,0 +1,595 @@ +# API Examples + +## Overview + +Real-world examples for using the Config System API. All examples use `curl` but can be adapted to any HTTP client. + +**Base URL**: `http://127.0.0.1:8000` (adjust for your environment) + +--- + +## Authentication + +All examples assume you have a KBase auth token. Set it as an environment variable: + +```bash +export KB_TOKEN="your-kbase-token-here" +``` + +Or use in curl: +```bash +curl -H "Authorization: Bearer $KB_TOKEN" ... +``` + +--- + +## 1. Config Resolution + +### Basic Resolution + +Resolve config for a KBase object: + +```bash +curl "http://127.0.0.1:8000/config/resolve/76990/7/2" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +**Response**: +```json +{ + "config": { + "id": "berdl_tables", + "name": "BERDL Tables", + "version": "1.0.0", + "tables": { ... } + }, + "source": "published", + "config_id": "abc123-def456", + "fingerprint": "v1_auto_xyz789", + "version": 1, + "object_type": "KBaseGeneDataLakes.BERDLTables-1.0", + "resolution_time_ms": 45.2 +} +``` + +### Resolution with Fingerprint + +Get exact match by database fingerprint: + +```bash +curl "http://127.0.0.1:8000/config/resolve/76990/7/2?fingerprint=v1_auto_xyz789" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +### Resolution with User Override + +Get user-specific config: + +```bash +curl "http://127.0.0.1:8000/config/resolve/76990/7/2?user_id=user:alice" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +### Trigger AI Generation + +Generate config if not found: + +```bash +curl "http://127.0.0.1:8000/config/resolve/76990/7/2?trigger_generation=true" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +--- + +## 2. Creating Configs + +### Create Draft Config + +```bash +curl -X POST "http://127.0.0.1:8000/config" \ + -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "source_type": "object", + "source_ref": "76990/7/2", + "config": { + "id": "my_custom_config", + "name": "My Custom Configuration", + "version": "1.0.0", + "tables": { + "Genes": { + "columns": { + "gene_id": { + "width": "150px", + "sortable": true, + "filterable": true + }, + "gene_name": { + "width": "200px", + "transform": { + "type": "link", + "options": { + "urlTemplate": "https://ncbi.nlm.nih.gov/gene/{value}" + } + } + } + } + } + } + }, + "change_summary": "Initial creation with custom column widths" + }' +``` + +### Create Derived Config (Inheritance) + +Create a config that extends another: + +```bash +curl -X POST "http://127.0.0.1:8000/config" \ + -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "source_type": "custom", + "source_ref": "custom:my_variant", + "extends_id": "abc123-def456", + "config": {}, + "change_summary": "Derived from base config with customizations" + }' +``` + +Then update with overlays: + +```bash +curl -X PATCH "http://127.0.0.1:8000/config/{config_id}" \ + -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "overlays": { + "tables": { + "Genes": { + "columns": { + "gene_id": { + "width": "200px", + "pin": "left" + } + } + } + } + }, + "change_summary": "Added left pin to gene_id column" + }' +``` + +--- + +## 3. Lifecycle Management + +### Propose Config for Review + +```bash +curl -X POST "http://127.0.0.1:8000/config/{config_id}/propose" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +### Publish Config + +```bash +curl -X POST "http://127.0.0.1:8000/config/{config_id}/publish" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +### Deprecate Config + +```bash +curl -X POST "http://127.0.0.1:8000/config/{config_id}/deprecate" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +--- + +## 4. User Overrides + +### Set User Override + +```bash +curl -X POST "http://127.0.0.1:8000/config/user/override" \ + -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "source_ref": "76990/7/2", + "override_config": { + "tables": { + "Genes": { + "columns": { + "gene_id": { + "width": "250px", + "pin": "left" + }, + "gene_name": { + "displayName": "Gene Symbol" + } + } + } + } + }, + "priority": 50 + }' +``` + +### Get User Override + +```bash +curl "http://127.0.0.1:8000/config/user/override/76990/7/2" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +### Remove User Override + +```bash +curl -X DELETE "http://127.0.0.1:8000/config/user/override/76990/7/2" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +--- + +## 5. Config Comparison + +### Diff Two Configs + +```bash +curl -X POST "http://127.0.0.1:8000/config/diff" \ + -H "Content-Type: application/json" \ + -d '{ + "config_id1": "abc123-def456", + "config_id2": "xyz789-uvw012" + }' +``` + +**Response**: +```json +{ + "added": { + "tables": { + "NewTable": { ... } + } + }, + "removed": { + "tables": { + "OldTable": { ... } + } + }, + "modified": { + "tables": { + "Genes": { + "columns": { + "gene_id": { + "old": {"width": "150px"}, + "new": {"width": "200px"} + } + } + } + } + }, + "unchanged": { + "id": "berdl_tables", + "name": "BERDL Tables" + }, + "summary": "1 added, 1 removed, 1 modified", + "has_changes": true +} +``` + +--- + +## 6. Config Testing + +### Test Configuration + +```bash +curl -X POST "http://127.0.0.1:8000/config/test" \ + -H "Content-Type: application/json" \ + -d '{ + "config_id": "abc123-def456", + "test_types": ["schema", "data", "performance", "integration"], + "db_path": "/path/to/test.db" + }' +``` + +**Response**: +```json +{ + "config_id": "abc123-def456", + "results": [ + { + "test_type": "schema", + "status": "passed", + "details": { + "db_tables": 5, + "config_tables": 5, + "matched_tables": 5 + }, + "execution_time_ms": 12.5, + "errors": [], + "warnings": [] + }, + { + "test_type": "data", + "status": "warning", + "details": { + "tested_tables": 3, + "total_tables": 5 + }, + "execution_time_ms": 45.2, + "errors": [], + "warnings": ["Table Metadata_Conditions is empty"] + } + ], + "overall_status": "warning", + "total_time_ms": 57.7 +} +``` + +--- + +## 7. Listing Configs + +### List All Published Configs + +```bash +curl "http://127.0.0.1:8000/config/list?state=published" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +### List Builtin Configs + +```bash +curl "http://127.0.0.1:8000/config/list?source_type=builtin&state=published" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +### List Configs by Object Type + +```bash +curl "http://127.0.0.1:8000/config/list?object_type=KBaseGeneDataLakes.BERDLTables-1.0" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +### Paginated Listing + +```bash +curl "http://127.0.0.1:8000/config/list?page=2&per_page=10" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +--- + +## 8. AI Integration + +### Submit AI Proposal + +```bash +curl -X POST "http://127.0.0.1:8000/config/ai/propose" \ + -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "intent": "Add UniRef links to genome_features table", + "target_source_ref": "76990/7/2", + "target_tables": ["genome_features"], + "proposed_changes": { + "tables": { + "genome_features": { + "columns": { + "uniref_90": { + "transform": { + "type": "link", + "options": { + "urlTemplate": "https://www.uniprot.org/uniref/{value}" + } + } + } + } + } + } + }, + "reasoning": "UniRef IDs should be clickable links to UniProt", + "confidence": 0.95, + "requires_human_review": true + }' +``` + +### Validate Config + +```bash +curl -X POST "http://127.0.0.1:8000/config/ai/validate" \ + -H "Content-Type: application/json" \ + -d '{ + "config": { + "id": "test_config", + "name": "Test", + "version": "1.0.0", + "tables": { + "Genes": { + "columns": { + "gene_id": {"width": "150px"} + } + } + } + }, + "strict": false + }' +``` + +--- + +## 9. Complete Workflow Example + +### End-to-End Config Creation and Publishing + +```bash +# 1. Create draft config +CONFIG_ID=$(curl -X POST "http://127.0.0.1:8000/config" \ + -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "source_type": "object", + "source_ref": "76990/7/2", + "config": { ... }, + "change_summary": "Initial draft" + }' | jq -r '.id') + +# 2. Test the config +curl -X POST "http://127.0.0.1:8000/config/test" \ + -H "Content-Type: application/json" \ + -d "{ + \"config_id\": \"$CONFIG_ID\", + \"test_types\": [\"schema\", \"data\", \"integration\"] + }" + +# 3. Propose for review +curl -X POST "http://127.0.0.1:8000/config/$CONFIG_ID/propose" \ + -H "Authorization: Bearer $KB_TOKEN" + +# 4. Publish (after review) +curl -X POST "http://127.0.0.1:8000/config/$CONFIG_ID/publish" \ + -H "Authorization: Bearer $KB_TOKEN" + +# 5. Verify it's available via resolve +curl "http://127.0.0.1:8000/config/resolve/76990/7/2" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +--- + +## 10. Python Client Example + +```python +import requests + +BASE_URL = "http://127.0.0.1:8000" +TOKEN = "your-kbase-token" + +headers = {"Authorization": f"Bearer {TOKEN}"} + +# Resolve config +response = requests.get( + f"{BASE_URL}/config/resolve/76990/7/2", + headers=headers, + params={"fingerprint": "v1_auto_xyz789"} +) +config = response.json() + +# Create config +create_response = requests.post( + f"{BASE_URL}/config", + headers=headers, + json={ + "source_type": "object", + "source_ref": "76990/7/2", + "config": { + "id": "my_config", + "name": "My Config", + "version": "1.0.0", + "tables": {} + }, + "change_summary": "Created via Python" + } +) +config_id = create_response.json()["id"] + +# Publish +requests.post( + f"{BASE_URL}/config/{config_id}/publish", + headers=headers +) +``` + +--- + +## 11. JavaScript/TypeScript Example + +```typescript +const BASE_URL = 'http://127.0.0.1:8000'; +const TOKEN = 'your-kbase-token'; + +async function resolveConfig(sourceRef: string) { + const response = await fetch( + `${BASE_URL}/config/resolve/${sourceRef}`, + { + headers: { + 'Authorization': `Bearer ${TOKEN}` + } + } + ); + return await response.json(); +} + +async function createConfig(config: any) { + const response = await fetch(`${BASE_URL}/config`, { + method: 'POST', + headers: { + 'Authorization': `Bearer ${TOKEN}`, + 'Content-Type': 'application/json' + }, + body: JSON.stringify({ + source_type: 'object', + source_ref: '76990/7/2', + config, + change_summary: 'Created via TypeScript' + }) + }); + return await response.json(); +} + +// Usage +const config = await resolveConfig('76990/7/2'); +console.log('Config source:', config.source); +``` + +--- + +## Error Handling + +All endpoints return standard HTTP status codes: + +- `200 OK` - Success +- `400 Bad Request` - Invalid request +- `401 Unauthorized` - Missing or invalid token +- `404 Not Found` - Resource not found +- `500 Internal Server Error` - Server error + +Error responses include a `detail` field: + +```json +{ + "detail": "Config not found: abc123" +} +``` + +--- + +## Rate Limiting + +For production deployments, consider rate limiting: +- Config resolution: 100 requests/minute +- Config creation: 10 requests/minute +- Config testing: 5 requests/minute + +--- + +## Best Practices + +1. **Always use fingerprints** for exact matching when available +2. **Test before publishing** to catch issues early +3. **Use inheritance** for related configs to reduce duplication +4. **Set user overrides** for personalization, not base configs +5. **Monitor resolution times** - should be < 500ms +6. **Cache resolved configs** on the client side +7. **Handle fallbacks** gracefully when API is unavailable + +--- + +**See Also**: +- [Config Control Plane Documentation](CONFIG_CONTROL_PLANE.md) +- [Migration Guide](MIGRATION_GUIDE.md) +- [Admin Guide](ADMIN_GUIDE.md) diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md index a3f4881..d8e5882 100644 --- a/docs/ARCHITECTURE.md +++ b/docs/ARCHITECTURE.md @@ -1,104 +1,533 @@ # TableScanner Architecture -TableScanner is a high-performance middleware service designed to provide fast, filtered, and paginated access to large tabular data stored in KBase. It solves the performance bottleneck of loading massive objects into memory by leveraging local SQLite caching and efficient indexing. +## Overview ---- +TableScanner is a microservice that provides filtered and paginated access to tabular data stored in KBase. It generates DataTables Viewer configurations using AI for new data types and sends them to DataTables Viewer for storage and management. -## High-Level Architecture +## System Architecture -```mermaid -graph TD - User([User / API Client]) - TS[TableScanner Service] - KBaseWS[KBase Workspace] - KBaseBlob[KBase Blobstore] - LocalCache[(Local SQLite Cache)] +``` +┌─────────────────────────────────────────────────────────────┐ +│ TableScanner Service │ +│ │ +│ ┌─────────────────────────────────────────────────────┐ │ +│ │ API Layer (FastAPI) │ │ +│ │ - Data access endpoints │ │ +│ │ - Config generation endpoints │ │ +│ └──────────────────┬──────────────────────────────────┘ │ +│ │ │ +│ ┌───────────────────▼──────────────────────────────────┐ │ +│ │ Services Layer │ │ +│ │ - Config Generator (AI-powered) │ │ +│ │ - Config Registry (tracks existing configs) │ │ +│ │ - Viewer Client (sends to DataTables Viewer) │ │ +│ │ - Schema Analyzer │ │ +│ │ - AI Provider │ │ +│ └──────────────────┬───────────────────────────────────┘ │ +│ │ │ +│ ┌───────────────────▼──────────────────────────────────┐ │ +│ │ Data Layer │ │ +│ │ - KBase Workspace API │ │ +│ │ - KBase Blobstore │ │ +│ │ - Local SQLite cache │ │ +│ └───────────────────────────────────────────────────────┘ │ +└───────────────────────────┬───────────────────────────────────┘ + │ + │ HTTP API + ▼ +┌─────────────────────────────────────────────────────────────┐ +│ DataTables Viewer │ +│ │ +│ - Receives generated configs │ +│ - Stores configs in database │ +│ - Allows developer editing │ +│ - Resolves configs for rendering │ +└─────────────────────────────────────────────────────────────┘ +``` + +## Core Components + +### 1. API Layer (`app/routes.py`) + +**Data Access Endpoints:** +- `GET /object/{ws_ref}/tables` - List tables in a KBase object +- `GET /object/{ws_ref}/tables/{table}/data` - Query table data +- `GET /object/{ws_ref}/tables/{table}/schema` - Get table schema +- `POST /table-data` - Programmatic table query + +**Config Generation Endpoints:** +- `POST /object/{ws_ref}/config/generate` - Generate config with AI +- `GET /config/providers` - List available AI providers +- `GET /config/generated/{fingerprint}` - Get cached generated config +- `GET /config/cached` - List all cached configs + +### 2. Config Generation Service (`app/services/config/config_generator.py`) + +**Purpose**: Generates DataTables Viewer-compatible JSON configurations using AI. + +**Process**: +1. Analyzes database schema +2. Infers column types and patterns +3. Uses AI to generate appropriate transforms and display options +4. Returns complete config JSON + +**Key Features**: +- AI-powered column inference +- Automatic category assignment +- Transform suggestions (links, badges, etc.) +- Caching by database fingerprint + +### 3. Config Registry (`app/services/config_registry.py`) + +**Purpose**: Tracks which object types already have configs in DataTables Viewer. + +**Functionality**: +- `has_config(object_type)` - Check if config exists +- `mark_has_config(object_type)` - Mark config as existing +- `mark_no_config(object_type)` - Mark config as missing +- `list_registered_types()` - List all registered types + +**Storage**: SQLite database at `{CACHE_DIR}/config_registry.db` + +### 4. Viewer Client (`app/services/viewer_client.py`) + +**Purpose**: Sends generated configs to DataTables Viewer API. + +**Methods**: +- `send_config(object_type, source_ref, config)` - Send config to viewer +- `check_config_exists(object_type)` - Check if config exists in viewer + +**Configuration**: `VIEWER_API_URL` in settings (default: `http://localhost:3000/api`) + +### 5. Schema Analyzer (`app/services/data/schema_analyzer.py`) + +**Purpose**: Analyzes SQLite database schemas to extract table and column information. + +**Output**: Table profiles with column metadata, types, and statistics. + +### 6. AI Provider (`app/services/ai/ai_provider.py`) + +**Purpose**: Abstraction layer for multiple AI backends. + +**Supported Providers**: +- OpenAI (GPT-4o-mini, GPT-4) +- Argo Gateway (ANL internal) +- Ollama (local LLMs) +- Claude Code CLI +- Rules-only (fallback) + +**Configuration**: Via environment variables (see `app/config.py`) + +## Data Flow + +### Config Generation Flow + +``` +1. Client Request + POST /object/{ws_ref}/config/generate + │ + ▼ +2. Check Registry + Does config exist for object_type? + │ + ├─ Yes → Return "exists" status + │ + └─ No → Continue + │ + ▼ +3. Download Database + Fetch SQLite DB from KBase Blobstore + │ + ▼ +4. Analyze Schema + Extract tables, columns, types + │ + ▼ +5. Generate Config (AI) + - Infer column types + - Suggest transforms + - Assign categories + - Generate complete config JSON + │ + ▼ +6. Send to DataTables Viewer + POST /api/configs + { + "object_type": "...", + "source_ref": "...", + "config": { ... } + } + │ + ▼ +7. Update Registry + Mark object_type as having config + │ + ▼ +8. Return Response + { + "status": "generated_and_sent", + "config": { ... }, + ... + } +``` + +### Data Access Flow + +``` +1. Client Request + GET /object/{ws_ref}/tables/{table}/data + │ + ▼ +2. Check Cache + Is database cached locally? + │ + ├─ Yes → Use cached DB + │ + └─ No → Download from Blobstore + │ + ▼ +3. Create Indices + Index all columns for fast queries + │ + ▼ +4. Execute Query + SQL query with filters, pagination + │ + ▼ +5. Return Results + JSON response with data and metadata +``` + +## Configuration + +### Environment Variables + +**KBase Authentication:** +- `KB_SERVICE_AUTH_TOKEN` - KBase authentication token + +**Cache Settings:** +- `CACHE_DIR` - Directory for cached files (default: `/tmp/tablescanner_cache`) +- `CACHE_MAX_AGE_HOURS` - Cache expiration (default: 24) + +**KBase Service URLs:** +- `WORKSPACE_URL` - Workspace service URL +- `BLOBSTORE_URL` - Blobstore service URL +- `KBASE_ENDPOINT` - Base KBase services URL + +**AI Provider:** +- `AI_PROVIDER` - Preferred provider (auto, openai, argo, ollama, etc.) +- `OPENAI_API_KEY` - OpenAI API key +- `ARGO_USER` - Argo gateway username +- `OLLAMA_HOST` - Ollama server URL + +**DataTables Viewer:** +- `VIEWER_API_URL` - Viewer API base URL (default: `http://localhost:3000/api`) + +## DataTables Viewer Integration + +### Required API Endpoints + +DataTables Viewer must implement these endpoints: + +#### 1. POST `/api/configs` + +Receive and store AI-generated configs. + +**Request:** +```json +{ + "object_type": "KBaseGeneDataLakes.BERDLTables-1.0", + "source_ref": "76990/7/2", + "config": { + "id": "berdl_tables", + "name": "BERDL Tables", + "version": "1.0.0", + "tables": { ... } + }, + "source": "ai_generated" +} +``` + +**Response:** +```json +{ + "status": "stored", + "config_id": "abc123", + "object_type": "KBaseGeneDataLakes.BERDLTables-1.0" +} +``` + +#### 2. GET `/api/configs/check?object_type={object_type}` + +Check if config exists. + +**Response:** +```json +{ + "exists": true, + "object_type": "KBaseGeneDataLakes.BERDLTables-1.0" +} +``` + +#### 3. GET `/api/configs?object_type={object_type}` + +Get config for object type. + +**Response:** +```json +{ + "config": { ... }, + "object_type": "KBaseGeneDataLakes.BERDLTables-1.0", + "source": "ai_generated", + "created_at": "2024-01-15T10:30:00Z" +} +``` + +### Config Storage + +**Database Schema:** +```sql +CREATE TABLE configs ( + id TEXT PRIMARY KEY, + object_type TEXT NOT NULL UNIQUE, + source_ref TEXT, + config_json TEXT NOT NULL, + source TEXT DEFAULT 'ai_generated', + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP +); + +CREATE INDEX idx_configs_object_type ON configs(object_type); +``` + +### Config Resolution + +When rendering tables, DataTables Viewer should resolve configs in this order: + +1. **User override** (if authenticated) +2. **Config for object_type** (from database) +3. **Default config** (minimal fallback) + +### Developer Editing + +DataTables Viewer should provide: +- UI to view/edit configs +- API to update configs: `PUT /api/configs/{config_id}` +- Version history (optional but recommended) + +## File Structure - User -->|API Requests| TS - TS -->|1. Resolve Metadata| KBaseWS - TS -->|2. Download Blob| KBaseBlob - TS -->|3. Store & Index| LocalCache - TS -->|4. SQL Query| LocalCache - LocalCache -->|5. Result| TS - TS -->|6. JSON Response| User ``` +app/ +├── routes.py # API endpoints +├── models.py # Pydantic models +├── config.py # Settings +├── services/ +│ ├── config/ +│ │ ├── config_generator.py # AI config generation +│ │ └── __init__.py +│ ├── config_registry.py # Track existing configs +│ ├── viewer_client.py # Send to DataTables Viewer +│ ├── ai/ +│ │ ├── ai_provider.py # AI abstraction +│ │ └── prompts.py # AI prompts +│ └── data/ +│ ├── schema_analyzer.py # Schema analysis +│ ├── fingerprint.py # Database fingerprinting +│ └── type_inference.py # Type inference +├── utils/ +│ ├── workspace.py # KBase Workspace client +│ ├── sqlite.py # SQLite utilities +│ └── cache.py # Caching utilities +└── db/ + └── schema.sql # Database schema (for registry) +``` + +## Key Design Decisions + +### 1. Config Storage Separation + +**Decision**: Configs are stored in DataTables Viewer, not TableScanner. + +**Rationale**: +- Configs are viewer-specific +- Developers edit configs in viewer +- Viewer manages config lifecycle +- TableScanner only generates configs + +### 2. Registry Pattern + +**Decision**: Simple registry tracks which configs exist. + +**Rationale**: +- Avoids regenerating existing configs +- Lightweight tracking mechanism +- No need for full config storage here + +### 3. AI-First Generation + +**Decision**: AI generates configs for new data types automatically. + +**Rationale**: +- Handles new data types without manual config creation +- Learns from schema patterns +- Reduces developer burden + +### 4. Caching Strategy + +**Decision**: Cache databases locally, cache generated configs by fingerprint. ---- +**Rationale**: +- Reduces KBase API calls +- Fast repeated access +- Fingerprint-based caching ensures consistency -## Caching Strategy: One DB per UPA +## Error Handling -TableScanner employs a strict **one-database-per-object** caching policy. Each KBase object reference (UPA, e.g., `76990/7/2`) is mapped to a unique local directory. +### Config Generation Failures -- **Path Structure**: `{CACHE_DIR}/{sanitized_UPA}/tables.db` -- **Sanitization**: Special characters like `/`, `:`, and spaces are replaced with underscores to ensure filesystem compatibility. -- **Granularity**: Caching is performed at the object level. If multiple tables exist within a single SQLite blob, they are all cached together, improving subsequent access to related data. +- **AI Provider Unavailable**: Falls back to rules-based generation +- **Database Download Fails**: Returns 500 error +- **Viewer API Unavailable**: Returns config but marks send as failed +- **Invalid Schema**: Returns 400 error with details ---- +### Data Access Failures -## Race Condition and Atomic Handling +- **Object Not Found**: Returns 404 +- **Table Not Found**: Returns 404 +- **Query Error**: Returns 500 with error details +- **Cache Corruption**: Automatically re-downloads -To ensure reliability in high-concurrency environments (multiple users requesting the same data simultaneously), TableScanner implements **Atomic File Operations**: +## Performance Considerations -### 1. Atomic Downloads -When a database needs to be downloaded, TableScanner does **not** download directly to the final path. -1. A unique temporary filename is generated using a UUID: `tables.db.{uuid}.tmp`. -2. The file is downloaded from the KBase Blobstore into this temporary file. -3. Once the download is successful and verified, a **filesystem-level atomic rename** (`os.rename`) is performed to move it to `tables.db`. -4. This ensures that if a process crashes or a network error occurs, the cache directory will not contain a partially-downloaded, corrupt database. +### Caching + +- Databases cached locally (24 hour TTL) +- Generated configs cached by fingerprint +- Registry cached in memory + +### Database Indexing + +- All columns indexed automatically on first access +- Indices persist across requests +- Fast filtering and sorting + +### AI Generation + +- Configs cached by database fingerprint +- Avoids regeneration for same schema +- AI calls only when needed + +## Security + +### Authentication + +- KBase token required for data access +- Token passed via `Authorization` header +- Token validated by KBase services + +### API Security + +- No authentication required for public endpoints +- Config generation requires KBase token +- Viewer API should implement authentication + +## Testing + +### Unit Tests + +- Service layer tests +- Config generator tests +- Registry tests + +### Integration Tests + +- End-to-end config generation +- Viewer client tests +- API endpoint tests + +### Manual Testing + +```bash +# Generate config +curl -X POST "http://127.0.0.1:8000/object/76990/7/2/config/generate" \ + -H "Authorization: Bearer $KB_TOKEN" + +# List tables +curl "http://127.0.0.1:8000/object/76990/7/2/tables" \ + -H "Authorization: Bearer $KB_TOKEN" + +# Get table data +curl "http://127.0.0.1:8000/object/76990/7/2/tables/Genes/data?limit=10" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + +## Deployment + +### Docker + +```bash +docker compose up --build -d +``` + +### Development + +```bash +bash scripts/dev.sh +``` -### 2. Concurrent Request Handling -If two requests for the same UPA arrive at the same time: -- Both will check for the existence of `tables.db`. -- If it's missing, both may start a download to their own unique `temp` files. -- The first one to finish will atomically rename its temp file to `tables.db`. -- The second one to finish will also rename its file, overwriting the first. Since the content is identical (same UPA), the final state remains consistent and the database is never in a corrupt state during the swap. +### Environment Setup ---- +1. Copy `.env.example` to `.env` +2. Set `KB_SERVICE_AUTH_TOKEN` +3. Configure AI provider (optional) +4. Set `VIEWER_API_URL` if viewer is on different host -## Performance Optimization: Automatic Indexing +## Monitoring -TableScanner doesn't just store the data; it optimizes it. Upon the **first access** to any table: -- The service scans the table schema. -- It automatically generates a `idx_{table}_{column}` index for **every single column** in the table. -- This "Indexing on Demand" strategy ensures that even complex global searches or specific column filters remain sub-millisecond, regardless of the table size. +### Health Checks ---- +- `/health` endpoint (if implemented) +- Database cache status +- AI provider availability -## Data Lifecycle in Detail +### Logging -1. **Request**: User provides a KBase UPA and query parameters. -2. **Cache Verification**: Service checks if `{sanitized_UPA}/tables.db` exists and is valid. -3. **Metadata Resolution**: If not cached, `KBUtilLib` fetches the object from KBase to extract the Blobstore handle. -4. **Secure Download**: The blob is streamed to a temporary UUID file and then atomically renamed. -5. **Schema Check**: TableScanner verifies the requested table exists in the SQLite file. -6. **Index Check**: If it's the first time this table is being queried, indices are created for all columns. -7. **SQL Execution**: A standard SQL query with `LIMIT`, `OFFSET`, and `LIKE` filters is executed. -8. **Streaming Serialization**: Results are converted into a compact JSON list-of-lists and returned to the user. +- All operations logged +- Config generation tracked +- Viewer API calls logged +- Errors logged with stack traces ---- +## Future Enhancements -## Tech Stack and Key Components +### Potential Improvements -- **FastAPI**: Provides the high-performance async web layer. -- **SQLite**: The storage engine for tabular data, chosen for its zero-configuration and high performance with indices. -- **KBUtilLib**: Handles complex KBase Workspace and Blobstore interactions. -- **UUID-based Temp Storage**: Prevents race conditions during file I/O. +1. **Batch Config Generation**: Generate configs for multiple objects +2. **Config Templates**: Reusable config templates +3. **Config Validation**: Validate configs before sending +4. **Metrics**: Track generation success rates +5. **Webhooks**: Notify on config generation ---- +### DataTables Viewer Enhancements -## Web Interface Architecture +1. **Config Versioning**: Track config changes over time +2. **Config Sharing**: Share configs between users +3. **Config Marketplace**: Community-contributed configs +4. **Config Testing**: Test configs against real data +5. **Config Diff**: Compare config versions -The **Research Data Explorer** is a production-grade single-file SPA (`static/viewer.html`) designed with a "Scientific Modern" aesthetic. +## Summary -### 1. Sidebar-First Layout -To mimic the feel of modern IDEs, navigation is concentrated in a fixed left sidebar. This keeps the "Main Stage" focused on the data grid. -- **Navigation Flow**: Connection → Pangenome Selection → Table Selection → Data Load. +TableScanner is a focused service that: +- Provides data access to KBase tabular data +- Generates DataTables Viewer configs using AI +- Sends configs to DataTables Viewer for storage +- Tracks which configs exist to avoid regeneration -### 2. Performance-Centric UI -- **Stateless Interaction**: The UI relies on the backend SQLite engine for all heavy lifting (sorting/filtering). -- **Sticky CSS Architecture**: Uses `sticky` positioning for both table headers and primary "ID" columns to maintain row context during massive horizontal scrolls. -- **Search Highlighting**: Uses dynamic CSS regex replacement to highlight search terms without re-rendering the entire DOM. +DataTables Viewer should: +- Receive and store configs via API +- Allow developers to edit configs +- Resolve configs when rendering tables +- Provide UI for config management -### 3. Design Tokens -- **Typography**: Inter (UI) and JetBrains Mono (Data) for maximum legibility. -- **Visuals**: Vibrant HSL-based color palette for status indicators and high-contrast badges for object types. +This separation of concerns keeps TableScanner simple and focused, while giving DataTables Viewer full control over config management and presentation. diff --git a/docs/CONFIG_SYSTEM.md b/docs/CONFIG_SYSTEM.md new file mode 100644 index 0000000..5e18893 --- /dev/null +++ b/docs/CONFIG_SYSTEM.md @@ -0,0 +1,182 @@ +# Config System Documentation + +## Overview + +Unified configuration system supporting both **AI-generated configs** and **developer-edited configs** with versioning for new KBase data tables. + +**Key Features**: +- Developer-editable JSON files (like `berdl_tables.json`) +- AI-powered config generation for new data +- Versioning and lifecycle management (Draft → Proposed → Published) +- Preview before syncing + +--- + +## Core Concepts + +### 1. Developer Configs (JSON Files) + +**Location**: `app/configs/*.json` + +**Purpose**: Developers edit these JSON files to customize how data is viewed. + +**Files**: +- `berdl_tables.json` - For BERDL/Pangenome data +- `genome_data_tables.json` - For Genome Data Tables + +**Workflow**: +```bash +# 1. Edit JSON file +vim app/configs/berdl_tables.json + +# 2. Preview changes +curl "http://127.0.0.1:8000/config/developer/berdl_tables.json/preview" + +# 3. Sync to system +python scripts/sync_developer_configs.py --filename berdl_tables.json +``` + +### 2. AI-Generated Configs + +**Purpose**: Automatically generate configs for new data tables queried through KBase. + +**Workflow**: +```bash +# Generate config for new data +curl -X POST "http://127.0.0.1:8000/object/76990/7/2/config/generate" +``` + +### 3. Versioning + +All configs are versioned in the database: +- **Draft** → Work in progress, can be modified +- **Proposed** → Ready for review, read-only +- **Published** → Production-ready, available to consumers +- Full history and audit trail + +--- + +## API Endpoints + +### Developer Configs + +- `GET /config/developer/list` - List all developer configs +- `GET /config/developer/{filename}` - Get config file +- `PUT /config/developer/{filename}` - Update config +- `POST /config/developer/{filename}/sync` - Sync to system +- `GET /config/developer/{filename}/preview` - Preview config + +### Config Resolution + +- `GET /config/resolve/{source_ref}` - Get best config for data source + +### AI Generation + +- `POST /object/{ws_ref}/config/generate` - Generate config via AI + +### Config Management + +- `POST /config` - Create new draft config +- `GET /config/{config_id}` - Get config by ID +- `PATCH /config/{config_id}` - Update draft config +- `POST /config/{config_id}/publish` - Publish config + +--- + +## Resolution Priority + +When resolving a config, the system tries in this order: + +1. User override (if authenticated) +2. Published config (fingerprint match) +3. Published config (source_ref match) +4. Published builtin (from developer configs) +5. Fallback registry (static JSON) +6. AI generation +7. Default config + +--- + +## Adding New Configs + +### For New Data Types + +1. **Create JSON file**: + ```bash + cat > app/configs/my_data_type.json << 'EOF' + { + "id": "my_data_type", + "name": "My Data Type", + "version": "1.0.0", + "tables": { + "MyTable": { + "columns": { + "id": {"width": "150px"} + } + } + } + } + EOF + ``` + +2. **Add object type mapping** in `app/configs/fallback_registry.py`: + ```python + FALLBACK_CONFIG_PATTERNS = { + # ... existing ... + r"MyApp\.MyType.*": "my_data_type.json", + } + ``` + +3. **Sync**: + ```bash + python scripts/sync_developer_configs.py --filename my_data_type.json + ``` + +--- + +## Service Organization + +``` +app/services/ +├── config/ # Config management +│ ├── config_store.py # Database storage +│ ├── config_resolver.py # Resolution logic +│ ├── developer_config.py # Developer JSON files +│ └── config_generator.py # AI generation +├── ai/ # AI services +│ └── ai_provider.py +└── data/ # Data analysis + ├── schema_analyzer.py + ├── fingerprint.py + └── type_inference.py +``` + +--- + +## Quick Reference + +### Developer: Edit Config + +```bash +vim app/configs/berdl_tables.json +python scripts/sync_developer_configs.py --filename berdl_tables.json +``` + +### AI: Generate Config + +```bash +curl -X POST "http://127.0.0.1:8000/object/76990/7/2/config/generate" +``` + +### Resolve Config + +```bash +curl "http://127.0.0.1:8000/config/resolve/76990/7/2" +``` + +--- + +## See Also + +- [API Examples](API_EXAMPLES.md) - Usage examples +- [DataTables Viewer Integration](personal/datatable_upgrade/upgrade.md) - Integration guide diff --git a/docs/QUICKSTART_DEMO.md b/docs/QUICKSTART_DEMO.md deleted file mode 100644 index b06de7d..0000000 --- a/docs/QUICKSTART_DEMO.md +++ /dev/null @@ -1,50 +0,0 @@ -# Quickstart Demo - -This guide walks you through running the TableScanner demo locally. - -## Prerequisites - -- Python 3.9+ -- KBase Auth Token (for accessing workspace objects) - -## Setup - -1. **Install Dependencies** - ```bash - pip install -r requirements.txt - ``` - -2. **Start the Service** - ```bash - uv run fastapi dev app/main.py - ``` - Server will start at `http://localhost:8000`. - -## Running the Demo - -1. Open the [Viewer](http://localhost:8000/static/viewer.html) in your browser. - -2. **Configuration:** - - **Environment**: Select `AppDev` (or appropriate env). - - **Auth Token**: Enter your KBase token. - -3. **Load Data:** - - **BERDL Table ID**: Enter `76990/ADP1Test`. - - Click the **Search** icon. - -4. **Explore:** - - Since `76990/ADP1Test` contains only one pangenome, it will be **auto-selected**. - - Tables will load automatically. - - Select a table (e.g., "Genome attributes") to view data. - - Hover over cells with IDs (UniProt, KEGG, etc.) to see tooltips. - - Click IDs to visit external databases. - -## Multi-Pangenome Demo - -To test loading multiple identifiers: - -1. **BERDL Table ID**: Enter `76990/ADP1Test, 76990/ADP1Test` (simulating two sources). -2. Click **Search**. -3. The **Pangenome** dropdown will appear. -4. Options will show as: `ADP1 [76990/ADP1Test]`. -5. Select different options to toggle between datasets (if they were different). diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..db121e4 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,24 @@ +# Documentation + +## Main Documentation + +- **[CONFIG_SYSTEM.md](CONFIG_SYSTEM.md)** - Complete config system documentation + - Developer configs (JSON files) + - AI-generated configs + - Versioning and lifecycle + - API endpoints + +- **[API_EXAMPLES.md](API_EXAMPLES.md)** - API usage examples + - Developer config workflows + - AI generation + - Config resolution + +## Additional Documentation + +- **[ARCHITECTURE.md](ARCHITECTURE.md)** - Technical architecture +- **[USAGE_GUIDE.md](USAGE_GUIDE.md)** - Usage guide +- **[QUICKSTART_DEMO.md](QUICKSTART_DEMO.md)** - Quick start guide + +## Integration Guides + +- **[personal/datatable_upgrade/upgrade.md](personal/datatable_upgrade/upgrade.md)** - DataTables Viewer integration diff --git a/docs/USAGE_GUIDE.md b/docs/USAGE_GUIDE.md deleted file mode 100644 index 5055079..0000000 --- a/docs/USAGE_GUIDE.md +++ /dev/null @@ -1,160 +0,0 @@ -# Usage Guide - -This guide covers production usage of the TableScanner service. - -## API Endpoint -The service is deployed at: -``` -https://appdev.kbase.us/services/berdl_table_scanner -``` - -## Authentication -All requests require a valid KBase authentication token passed in the `Authorization` header. - -```bash -Authorization: -``` - ---- - -## 1. Using the Hierarchical REST API (Browser-friendly) - -This style uses hierarchical paths and standard GET requests. It is ideal for web applications or simple data navigation. - -### List Available Tables -Get a list of all tables found in a KBase object. - -**Endpoint:** `GET /object/{upa}/tables` - -**Example:** -```bash -curl -H "Authorization: $KB_TOKEN" \ - "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables" - -**Response**: -```json -{ - "berdl_table_id": "76990/7/2", - "object_type": "KBaseFBA.GenomeDataLakeTables-2.0", - "tables": [ - {"name": "Genes", "row_count": 3356, "column_count": 18}, - {"name": "Metadata_Conditions", "row_count": 42, "column_count": 12} - ], - "source": "Cache" -} -``` -``` - -### Query Table Data -Retrieve paginated data from a specific table. - -**Endpoint:** `GET /object/{upa}/tables/{table_name}/data` - -**Parameters:** -- `limit`: (int) Maximum rows (default 100) -- `offset`: (int) Skip rows (default 0) -- `search`: (string) Global search term -- `sort_column`: (string) Column to sort by -- `sort_order`: (string) "ASC" or "DESC" - -**Example:** -```bash -curl -H "Authorization: $KB_TOKEN" \ - "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables/Genes/data?limit=1" - -**Response**: -```json -{ - "headers": ["gene_id", "contig_id", "start", "..."], - "data": [["gene_1", "contig_A", "100", "..."]], - "row_count": 1, - "total_count": 3356, - "filtered_count": 3356, - "object_type": "KBaseFBA.GenomeDataLakeTables-2.0", - "response_time_ms": 12.4 -} -``` -``` - ---- - -## 2. Using the Flat POST API (Script-friendly) - -The Flat POST API is recommended for Python scripts and programmatic access. It allows sending complex query parameters in a single JSON body. - -**Endpoint:** `POST /table-data` - -### Implementation Example (Python) - -```python -import requests -import json - -url = "https://appdev.kbase.us/services/berdl_table_scanner/table-data" -headers = {"Authorization": "YOUR_KBASE_TOKEN"} - -payload = { - "berdl_table_id": "76990/7/2", - "table_name": "Metadata_Conditions", - "limit": 50, - "offset": 0, - "search_value": "glucose", - "col_filter": { - "organism": "E. coli" - }, - "sort_column": "yield", - "sort_order": "DESC" -} - -response = requests.post(url, json=payload, headers=headers) -data = response.json() - -**Example Response**: -```json -{ - "headers": ["organism", "yield", "..."], - "data": [["E. coli", "0.42", "..."]], - "row_count": 1, - "total_count": 500, - "filtered_count": 50, - "object_type": "KBaseFBA.GenomeDataLakeTables-2.0", - "response_time_ms": 15.6 -} -``` - -print(f"Retrieved {len(data['data'])} rows.") -``` - ---- - -## Pro Tips - -### Multi-Source Search -The metadata endpoints support comma-separated IDs to aggregate pangenomes across multiple objects. - -```bash -GET /pangenomes?berdl_table_id=76990/7/2,76990/8/1 -``` - -### Performance -The first request for a large dataset may take a few seconds as the service downloads and indexes the database. Subsequent requests will be near-instant. - ---- - -## Web Viewer: Research Data Explorer - -The TableScanner interactive viewer is a premium, single-page application built for high-performance research. - -### Key Operations -1. **Connect**: Enter a KBase UPA (e.g. `76990/7/2`) and your Auth Token to load available tables. -2. **Explore**: Use the IDE-like sidebar to navigate between pangenomes and tables. -3. **Analyze**: - - **Global Search**: Instantly filters all columns with high-contrast highlighting. - - **Density Control**: Toggle between `Compact`, `Default`, and `Comfortable` views. - - **Column Management**: Custom visibility toggles for wide datasets. -4. **Export**: One-click **Export to CSV** for local analysis. - -### Visual Architecture -- **Scientific Modern Theme**: A professional light mode designed for long sessions. -- **Dynamic Feedback**: Real-time status bar updates with cache performance metrics. -- **Sticky Layout**: Fixed headers and primary columns ensure context is never lost during scrolling. diff --git a/scripts/migrate_fallback_configs.py b/scripts/migrate_fallback_configs.py new file mode 100755 index 0000000..63f0e06 --- /dev/null +++ b/scripts/migrate_fallback_configs.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +""" +Migration Script: Import Fallback Configs as Builtin Configs + +This script migrates existing fallback JSON configs (berdl_tables.json, etc.) +into the Config Control Plane as published builtin configurations. + +Usage: + python scripts/migrate_fallback_configs.py + +This ensures backward compatibility while transitioning to the unified +Config Control Plane architecture. +""" + +import json +import logging +import sys +from pathlib import Path + +# Add parent directory to path for imports +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from app.services.config_store import get_config_store +from app.models import ConfigCreateRequest, ConfigSourceType +from app.configs.fallback_registry import ( + list_available_configs, + load_config_file, + FALLBACK_CONFIG_PATTERNS, +) + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger(__name__) + + +def get_object_type_for_config(config_filename: str) -> str | None: + """ + Determine the KBase object type pattern for a config file. + + Args: + config_filename: Name of the config file (e.g., "berdl_tables.json") + + Returns: + Object type pattern or None + """ + # Reverse lookup: find pattern that matches this filename + for pattern, filename in FALLBACK_CONFIG_PATTERNS.items(): + if filename == config_filename: + # Extract object type from pattern + # Patterns like "KBaseGeneDataLakes\.BERDLTables.*" + if "BERDLTables" in pattern: + return "KBaseGeneDataLakes.BERDLTables-1.0" + elif "GenomeDataTables" in pattern or "GenomeDataLakeTables" in pattern: + return "KBaseFBA.GenomeDataLakeTables-1.0" + + return None + + +def migrate_fallback_configs() -> int: + """ + Migrate all fallback configs to Config Control Plane as builtins. + + Returns: + Number of configs migrated + """ + store = get_config_store() + configs = list_available_configs() + + migrated_count = 0 + + for config_info in configs: + filename = config_info["filename"] + config_id = config_info["id"] + config_data = load_config_file(filename) + + if not config_data: + logger.warning(f"Skipping {filename}: failed to load") + continue + + # Check if already migrated + object_type = get_object_type_for_config(filename) + source_ref = f"builtin:{config_id}" + + # Check for existing published builtin + existing = store.resolve(source_ref, object_type=object_type) + if existing and existing.state.value == "published": + logger.info(f"Skipping {filename}: already migrated as {existing.id}") + continue + + try: + # Create as builtin config + create_request = ConfigCreateRequest( + source_type=ConfigSourceType.BUILTIN, + source_ref=source_ref, + config=config_data, + object_type=object_type, + change_summary=f"Migrated from fallback config: {filename}", + ) + + # Create draft + record = store.create(create_request, "system:migration") + logger.info(f"Created draft config: {record.id} for {filename}") + + # Auto-propose + record = store.propose(record.id, "system:migration") + logger.info(f"Proposed config: {record.id}") + + # Auto-publish + record = store.publish(record.id, "system:migration") + logger.info(f"Published builtin config: {record.id} ({config_id})") + + migrated_count += 1 + + except Exception as e: + logger.error(f"Failed to migrate {filename}: {e}", exc_info=True) + continue + + return migrated_count + + +def main(): + """Main entry point.""" + logger.info("Starting fallback config migration...") + + try: + count = migrate_fallback_configs() + logger.info(f"Migration complete: {count} config(s) migrated") + + if count > 0: + logger.info("\nMigrated configs are now available via:") + logger.info(" GET /config/list?source_type=builtin&state=published") + logger.info(" GET /config/resolve/{source_ref}?object_type={object_type}") + + return 0 + + except Exception as e: + logger.error(f"Migration failed: {e}", exc_info=True) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/sync_developer_configs.py b/scripts/sync_developer_configs.py new file mode 100755 index 0000000..02e2857 --- /dev/null +++ b/scripts/sync_developer_configs.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 +""" +Developer Config Sync Script + +Syncs all developer-editable JSON configs to the Config Control Plane. +Run this after editing config files or pulling from git. + +Usage: + python scripts/sync_developer_configs.py [--auto-publish] +""" + +import argparse +import logging +import sys +from pathlib import Path + +# Add parent directory to path +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from app.services.developer_config import get_developer_config_manager + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger(__name__) + + +def main(): + """Main entry point.""" + parser = argparse.ArgumentParser( + description="Sync developer configs to Config Control Plane" + ) + parser.add_argument( + "--auto-publish", + action="store_true", + help="Auto-publish configs after syncing" + ) + parser.add_argument( + "--filename", + help="Sync only this specific config file" + ) + + args = parser.parse_args() + + manager = get_developer_config_manager() + + try: + if args.filename: + # Sync single config + logger.info(f"Syncing {args.filename}...") + result = manager.sync_to_control_plane( + args.filename, + auto_publish=args.auto_publish + ) + logger.info(f"Result: {result['status']} - {result['message']}") + + if result['status'] == 'synced': + logger.info(f"Config ID: {result['config_id']}") + logger.info(f"State: {result['state']}") + else: + # Sync all configs + logger.info("Syncing all developer configs...") + results = manager.sync_all_to_control_plane( + auto_publish=args.auto_publish + ) + + synced = sum(1 for r in results.values() if r.get("status") == "synced") + unchanged = sum(1 for r in results.values() if r.get("status") == "unchanged") + errors = sum(1 for r in results.values() if r.get("status") == "error") + + logger.info(f"Sync complete:") + logger.info(f" Synced: {synced}") + logger.info(f" Unchanged: {unchanged}") + logger.info(f" Errors: {errors}") + + if errors > 0: + logger.warning("Some configs failed to sync:") + for filename, result in results.items(): + if result.get("status") == "error": + logger.warning(f" {filename}: {result.get('error')}") + + return 0 + + except Exception as e: + logger.error(f"Sync failed: {e}", exc_info=True) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/verify_config_plane.py b/scripts/verify_config_plane.py new file mode 100644 index 0000000..b2e385d --- /dev/null +++ b/scripts/verify_config_plane.py @@ -0,0 +1,131 @@ + +import sys +import requests +import json +import time + +BASE_URL = "http://127.0.0.1:8888" + +def log(msg): + print(f"[TEST] {msg}") + +def check(response, expected_status=200): + if response.status_code != expected_status: + print(f"FAILED: Expected {expected_status}, got {response.status_code}") + print(response.text) + sys.exit(1) + return response.json() + +def test_lifecycle(): + log("Testing full config lifecycle...") + + # Unique ref to avoid collisions + ref_suffix = int(time.time()) + source_ref = f"test/lifecycle/{ref_suffix}" + log(f"Using source_ref: {source_ref}") + + # 1. Create Draft + log("1. Creating draft config...") + draft = { + "source_type": "custom", + "source_ref": source_ref, + "fingerprint": f"test_fp_{ref_suffix}", + "config": { + "id": f"test_config_{ref_suffix}", + "name": "Test Lifecycle Config", + "tables": { + "Genes": {"columns": {"id": {"width": 100}}} + } + }, + "change_summary": "Initial test create" + } + resp = requests.post(f"{BASE_URL}/config", json=draft) + record = check(resp, 200) + config_id = record["id"] + version = record["version"] + log(f" Created config {config_id} (v{version}) in state {record['state']}") + + # 2. Update Draft + log("2. Updating draft...") + update = { + "change_summary": "Updating width", + "overlays": { + "tables": { + "Genes": {"columns": {"id": {"width": 120}}} + } + } + } + resp = requests.patch(f"{BASE_URL}/config/{config_id}", json=update) + record = check(resp, 200) + log(f" Updated config. State: {record['state']}") + + # 3. Propose + log("3. Proposing config...") + resp = requests.post(f"{BASE_URL}/config/{config_id}/propose") + check(resp, 200) + + # Verify state + resp = requests.get(f"{BASE_URL}/config/{config_id}") + record = check(resp, 200) + if record["state"] != "proposed": + print(f"FAILED: Expected proposed, got {record['state']}") + sys.exit(1) + log(" Config is PROPOSED") + + # 4. Publish + log("4. Publishing config...") + resp = requests.post(f"{BASE_URL}/config/{config_id}/publish") + check(resp, 200) + log(" Config is PUBLISHED") + + # 5. Resolve + log("5. Resolving config...") + resp = requests.get(f"{BASE_URL}/config/resolve/{source_ref.replace('/', '%2F')}") # Ensure URL encoding + resolved = check(resp, 200) + + if resolved["source"] != "published": + print(f"FAILED: Expected source='published', got {resolved['source']}") + sys.exit(1) + + if resolved["version"] != version: + print(f"FAILED: Expected version {version}, got {resolved['version']}") + sys.exit(1) + + width = resolved["config"]["tables"]["Genes"]["columns"]["id"]["width"] + if width != 120: + print(f"FAILED: Expected width 120, got {width}") + sys.exit(1) + + log(" Resolved successfully with correct updates!") + + # 6. List + log("6. Listing configs...") + resp = requests.get(f"{BASE_URL}/config/list?state=published") + data = check(resp, 200) + total = data["total"] + log(f" Found {total} published configs") + if total < 1: + print("FAILED: Should have at least 1 published config") + sys.exit(1) + + log("Lifecycle test PASSED") + +def test_resolve_fallback(): + log("\nTesting resolution fallback...") + # Request something non-existent + resp = requests.get(f"{BASE_URL}/config/resolve/non_existent/ref/1") + data = check(resp, 200) + log(f" Resolved source: {data['source']}") + + if data["source"] != "default": + print(f"FAILED: Expected default fallback, got {data['source']}") + # Don't exit, just warn for now as we might have other fallbacks + +if __name__ == "__main__": + try: + test_lifecycle() + test_resolve_fallback() + print("\nALL SYSTEMS GO!") + except Exception as e: + print(f"\nTEST FAILED: {e}") + sys.exit(1) diff --git a/tests/test_api_basic.py b/tests/test_api_basic.py new file mode 100644 index 0000000..d0c64a8 --- /dev/null +++ b/tests/test_api_basic.py @@ -0,0 +1,67 @@ +""" +Basic API Tests + +Tests core API functionality without requiring KBase authentication. +""" + +import pytest +from fastapi.testclient import TestClient +from app.main import app + +client = TestClient(app) + + +def test_health_check(): + """Test health/status endpoint.""" + response = client.get("/health") + assert response.status_code in [200, 404] # May not exist + + +def test_api_docs(): + """Test that API docs are accessible.""" + response = client.get("/docs") + assert response.status_code == 200 + + +def test_openapi_schema(): + """Test OpenAPI schema is available.""" + response = client.get("/openapi.json") + assert response.status_code == 200 + schema = response.json() + assert "openapi" in schema + assert "paths" in schema + + +def test_config_providers(): + """Test config providers endpoint.""" + response = client.get("/config/providers") + assert response.status_code == 200 + data = response.json() + assert isinstance(data, list) + + +def test_routes_exist(): + """Test that key routes are registered.""" + response = client.get("/openapi.json") + schema = response.json() + paths = schema["paths"] + + # Key endpoints should exist + assert "/object/{ws_ref}/tables" in paths + assert "/object/{ws_ref}/config/generate" in paths + assert "/config/providers" in paths + + +def test_config_generate_endpoint_exists(): + """Test config generate endpoint is registered.""" + response = client.get("/openapi.json") + schema = response.json() + paths = schema["paths"] + + assert "/object/{ws_ref}/config/generate" in paths + # Should be POST + assert "post" in paths["/object/{ws_ref}/config/generate"] + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/test_config_control_plane.py b/tests/test_config_control_plane.py new file mode 100644 index 0000000..bb391ea --- /dev/null +++ b/tests/test_config_control_plane.py @@ -0,0 +1,427 @@ +""" +Tests for Config Control Plane functionality. + +Tests cover: +- ConfigStore CRUD operations +- Lifecycle transitions +- Config resolution cascade +- AI proposal handling +""" + +import pytest +import tempfile +import json +from pathlib import Path +from datetime import datetime + +from app.services.config_store import ConfigStore, get_config_store +from app.services.config_resolver import ConfigResolver, get_config_resolver +from app.models import ( + ConfigCreateRequest, + ConfigUpdateRequest, + ConfigState, + ConfigSourceType, +) + + +@pytest.fixture +def temp_db(): + """Create a temporary database for testing.""" + with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f: + db_path = Path(f.name) + + store = ConfigStore(db_path=db_path) + yield store, db_path + + # Cleanup + if db_path.exists(): + db_path.unlink() + + +@pytest.fixture +def sample_config(): + """Sample config for testing.""" + return { + "id": "test_config", + "name": "Test Configuration", + "version": "1.0.0", + "description": "Test config for unit tests", + "tables": { + "Genes": { + "columns": { + "gene_id": { + "width": "150px", + "sortable": True + } + } + } + } + } + + +class TestConfigStore: + """Test ConfigStore CRUD operations.""" + + def test_create_config(self, temp_db, sample_config): + """Test creating a new draft config.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + change_summary="Initial creation" + ) + + record = store.create(request, "user:test") + + assert record.state == ConfigState.DRAFT + assert record.source_ref == "76990/7/2" + assert record.config == sample_config + assert record.version == 1 + + def test_get_config(self, temp_db, sample_config): + """Test retrieving a config by ID.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + + created = store.create(request, "user:test") + retrieved = store.get(created.id) + + assert retrieved is not None + assert retrieved.id == created.id + assert retrieved.config == sample_config + + def test_update_draft_config(self, temp_db, sample_config): + """Test updating a draft config.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + + record = store.create(request, "user:test") + + update_request = ConfigUpdateRequest( + change_summary="Added new column", + overlays={ + "tables": { + "Genes": { + "columns": { + "gene_name": {"width": "200px"} + } + } + } + } + ) + + updated = store.update(record.id, update_request, "user:test") + + assert "gene_name" in updated.config["tables"]["Genes"]["columns"] + assert updated.config["tables"]["Genes"]["columns"]["gene_id"]["width"] == "150px" + + def test_cannot_update_published_config(self, temp_db, sample_config): + """Test that published configs cannot be updated.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + + record = store.create(request, "user:test") + store.propose(record.id, "user:test") + store.publish(record.id, "user:test") + + update_request = ConfigUpdateRequest( + change_summary="Trying to update published", + config={"id": "modified"} + ) + + with pytest.raises(ValueError, match="Cannot update config in state"): + store.update(record.id, update_request, "user:test") + + def test_delete_draft_config(self, temp_db, sample_config): + """Test deleting a draft config.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + + record = store.create(request, "user:test") + deleted = store.delete(record.id, "user:test") + + assert deleted is True + assert store.get(record.id) is None + + def test_cannot_delete_published_config(self, temp_db, sample_config): + """Test that published configs cannot be deleted.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + + record = store.create(request, "user:test") + store.propose(record.id, "user:test") + store.publish(record.id, "user:test") + + with pytest.raises(ValueError, match="Cannot delete config in state"): + store.delete(record.id, "user:test") + + +class TestLifecycleTransitions: + """Test config lifecycle state transitions.""" + + def test_draft_to_proposed(self, temp_db, sample_config): + """Test transitioning draft to proposed.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + + record = store.create(request, "user:test") + proposed = store.propose(record.id, "user:reviewer") + + assert proposed.state == ConfigState.PROPOSED + + def test_proposed_to_published(self, temp_db, sample_config): + """Test transitioning proposed to published.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + + record = store.create(request, "user:test") + store.propose(record.id, "user:reviewer") + published = store.publish(record.id, "user:publisher") + + assert published.state == ConfigState.PUBLISHED + assert published.published_at is not None + assert published.published_by == "user:publisher" + + def test_published_to_deprecated(self, temp_db, sample_config): + """Test deprecating a published config.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + + record = store.create(request, "user:test") + store.propose(record.id, "user:reviewer") + store.publish(record.id, "user:publisher") + deprecated = store.deprecate(record.id, "user:admin") + + assert deprecated.state == ConfigState.DEPRECATED + + def test_invalid_transition(self, temp_db, sample_config): + """Test that invalid transitions raise errors.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + + record = store.create(request, "user:test") + + # Try to publish without proposing first + with pytest.raises(ValueError, match="must be in proposed state"): + store.publish(record.id, "user:test") + + +class TestConfigResolution: + """Test config resolution cascade.""" + + def test_resolve_by_fingerprint(self, temp_db, sample_config): + """Test resolution with fingerprint match.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + fingerprint="abc123def456", + ) + + record = store.create(request, "user:test") + store.propose(record.id, "user:test") + store.publish(record.id, "user:test") + + resolver = ConfigResolver() + resolver.store = store # Use test store + + resolved = store.resolve("76990/7/2", fingerprint="abc123def456") + + assert resolved is not None + assert resolved.id == record.id + assert resolved.fingerprint == "abc123def456" + + def test_resolve_by_source_ref(self, temp_db, sample_config): + """Test resolution by source reference.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + + record = store.create(request, "user:test") + store.propose(record.id, "user:test") + store.publish(record.id, "user:test") + + resolved = store.resolve("76990/7/2") + + assert resolved is not None + assert resolved.id == record.id + + def test_resolve_builtin_by_object_type(self, temp_db, sample_config): + """Test resolution of builtin config by object type.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.BUILTIN, + source_ref="builtin:berdl_tables", + config=sample_config, + object_type="KBaseGeneDataLakes.BERDLTables-1.0", + ) + + record = store.create(request, "user:test") + store.propose(record.id, "user:test") + store.publish(record.id, "user:test") + + resolved = store.resolve( + "unknown_ref", + object_type="KBaseGeneDataLakes.BERDLTables-1.0" + ) + + assert resolved is not None + assert resolved.source_type == ConfigSourceType.BUILTIN + + def test_resolution_fallback_to_default(self, temp_db): + """Test resolution falls back to default when nothing found.""" + resolver = ConfigResolver() + resolver.store = ConfigStore(db_path=temp_db[1]) + + response = resolver.resolve("unknown/ref/123") + + assert response.source == "default" + assert response.config is not None + assert "id" in response.config + + +class TestConfigListing: + """Test config listing and filtering.""" + + def test_list_all_configs(self, temp_db, sample_config): + """Test listing all configs.""" + store, db_path = temp_db + + # Create multiple configs + for i in range(3): + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref=f"76990/{i}/1", + config={**sample_config, "id": f"config_{i}"}, + ) + store.create(request, "user:test") + + configs, total = store.list_configs() + + assert total == 3 + assert len(configs) == 3 + + def test_list_by_state(self, temp_db, sample_config): + """Test filtering configs by state.""" + store, db_path = temp_db + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + + draft = store.create(request, "user:test") + store.propose(draft.id, "user:test") + published = store.publish(draft.id, "user:test") + + drafts, draft_total = store.list_configs(state=ConfigState.DRAFT) + published_configs, pub_total = store.list_configs(state=ConfigState.PUBLISHED) + + assert draft_total == 0 # No drafts after publishing + assert pub_total == 1 + assert published_configs[0].id == published.id + + def test_list_by_source_type(self, temp_db, sample_config): + """Test filtering configs by source type.""" + store, db_path = temp_db + + # Create object and builtin configs + obj_request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="76990/7/2", + config=sample_config, + ) + store.create(obj_request, "user:test") + + builtin_request = ConfigCreateRequest( + source_type=ConfigSourceType.BUILTIN, + source_ref="builtin:test", + config=sample_config, + ) + store.create(builtin_request, "user:test") + + builtins, total = store.list_configs(source_type=ConfigSourceType.BUILTIN) + + assert total == 1 + assert builtins[0].source_type == ConfigSourceType.BUILTIN + + def test_pagination(self, temp_db, sample_config): + """Test pagination in config listing.""" + store, db_path = temp_db + + # Create 5 configs + for i in range(5): + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref=f"76990/{i}/1", + config={**sample_config, "id": f"config_{i}"}, + ) + store.create(request, "user:test") + + # Get first page + page1, total = store.list_configs(page=1, per_page=2) + assert len(page1) == 2 + assert total == 5 + + # Get second page + page2, total = store.list_configs(page=2, per_page=2) + assert len(page2) == 2 + assert total == 5 + + # Verify different configs + assert page1[0].id != page2[0].id diff --git a/tests/test_integration.py b/tests/test_integration.py new file mode 100644 index 0000000..e8c61b1 --- /dev/null +++ b/tests/test_integration.py @@ -0,0 +1,409 @@ +""" +Integration Tests for Config Control Plane. + +End-to-end tests that verify the full workflow from config creation +to resolution and consumption. +""" + +import pytest +import tempfile +import json +from pathlib import Path +from fastapi.testclient import TestClient + +from app.main import create_app +from app.services.config_store import ConfigStore +from app.services.config_resolver import get_config_resolver +from app.models import ConfigCreateRequest, ConfigSourceType, ConfigState + + +@pytest.fixture +def temp_db(): + """Create temporary database for testing.""" + with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f: + db_path = Path(f.name) + + yield db_path + + if db_path.exists(): + db_path.unlink() + + +@pytest.fixture +def client(temp_db): + """Create test client with temporary database.""" + # Override config store DB path + import app.services.config_store + original_init = ConfigStore.__init__ + + def mock_init(self, db_path=None): + original_init(self, db_path=temp_db) + + ConfigStore.__init__ = mock_init + + app = create_app() + client = TestClient(app) + + yield client + + # Restore + ConfigStore.__init__ = original_init + + +@pytest.fixture +def sample_config(): + """Sample config for testing.""" + return { + "id": "test_integration", + "name": "Integration Test Config", + "version": "1.0.0", + "description": "Config for integration testing", + "tables": { + "Genes": { + "columns": { + "gene_id": { + "width": "150px", + "sortable": True, + "filterable": True + }, + "gene_name": { + "width": "200px", + "displayName": "Gene Name" + } + } + } + } + } + + +class TestConfigWorkflow: + """Test complete config lifecycle workflow.""" + + def test_create_propose_publish_workflow(self, client, sample_config): + """Test full lifecycle: create → propose → publish.""" + # 1. Create draft + response = client.post( + "/config", + json={ + "source_type": "object", + "source_ref": "test/1/1", + "config": sample_config, + "change_summary": "Integration test" + } + ) + assert response.status_code == 200 + config_id = response.json()["id"] + assert response.json()["state"] == "draft" + + # 2. Propose + response = client.post(f"/config/{config_id}/propose") + assert response.status_code == 200 + assert response.json()["status"] == "proposed" + + # Get config to verify state + response = client.get(f"/config/{config_id}") + assert response.json()["state"] == "proposed" + + # 3. Publish + response = client.post(f"/config/{config_id}/publish") + assert response.status_code == 200 + assert response.json()["status"] == "published" + + # Verify published + response = client.get(f"/config/{config_id}") + assert response.json()["state"] == "published" + assert response.json()["published_at"] is not None + + def test_resolution_after_publish(self, client, sample_config): + """Test that published config is available via resolve.""" + # Create and publish + create_resp = client.post( + "/config", + json={ + "source_type": "object", + "source_ref": "test/2/1", + "config": sample_config, + "object_type": "Test.ObjectType-1.0", + "change_summary": "For resolution test" + } + ) + config_id = create_resp.json()["id"] + + client.post(f"/config/{config_id}/propose") + client.post(f"/config/{config_id}/publish") + + # Resolve + response = client.get("/config/resolve/test/2/1") + assert response.status_code == 200 + data = response.json() + + assert data["source"] == "published" + assert data["config_id"] == config_id + assert data["config"]["id"] == "test_integration" + + def test_user_override_workflow(self, client, sample_config): + """Test user override creation and resolution.""" + # Create base config + create_resp = client.post( + "/config", + json={ + "source_type": "object", + "source_ref": "test/3/1", + "config": sample_config, + "change_summary": "Base config" + } + ) + config_id = create_resp.json()["id"] + client.post(f"/config/{config_id}/propose") + client.post(f"/config/{config_id}/publish") + + # Set user override + override_resp = client.post( + "/config/user/override", + json={ + "source_ref": "test/3/1", + "override_config": { + "tables": { + "Genes": { + "columns": { + "gene_id": { + "width": "300px", # Override + "pin": "left" # New field + } + } + } + } + }, + "priority": 50 + }, + headers={"Authorization": "Bearer test_token"} + ) + assert override_resp.status_code == 200 + + # Resolve with user ID + resolve_resp = client.get( + "/config/resolve/test/3/1?user_id=user:test" + ) + assert resolve_resp.status_code == 200 + data = resolve_resp.json() + + # Should use override + assert data["source"] == "user_override" + assert data["config"]["tables"]["Genes"]["columns"]["gene_id"]["width"] == "300px" + + def test_config_inheritance_workflow(self, client, sample_config): + """Test config inheritance and overlays.""" + # Create parent config + parent_resp = client.post( + "/config", + json={ + "source_type": "builtin", + "source_ref": "builtin:parent", + "config": sample_config, + "change_summary": "Parent config" + } + ) + parent_id = parent_resp.json()["id"] + client.post(f"/config/{parent_id}/propose") + client.post(f"/config/{parent_id}/publish") + + # Create child config with inheritance + child_resp = client.post( + "/config", + json={ + "source_type": "custom", + "source_ref": "custom:child", + "extends_id": parent_id, + "config": {}, + "change_summary": "Child config" + } + ) + child_id = child_resp.json()["id"] + + # Add overlays + client.patch( + f"/config/{child_id}", + json={ + "overlays": { + "tables": { + "Genes": { + "columns": { + "gene_id": { + "width": "250px" # Override parent + } + } + } + } + }, + "change_summary": "Added overlays" + } + ) + + # Publish child + client.post(f"/config/{child_id}/propose") + client.post(f"/config/{child_id}/publish") + + # Resolve child - should have parent + overlays + resolve_resp = client.get("/config/resolve/custom:child") + assert resolve_resp.status_code == 200 + data = resolve_resp.json() + + # Should have parent's structure + assert "Genes" in data["config"]["tables"] + # Should have overlay applied + assert data["config"]["tables"]["Genes"]["columns"]["gene_id"]["width"] == "250px" + + +class TestConfigTesting: + """Test config testing functionality.""" + + def test_config_testing_endpoint(self, client, sample_config): + """Test config testing endpoint.""" + # Create and publish config + create_resp = client.post( + "/config", + json={ + "source_type": "object", + "source_ref": "test/4/1", + "config": sample_config, + "change_summary": "For testing" + } + ) + config_id = create_resp.json()["id"] + client.post(f"/config/{config_id}/propose") + client.post(f"/config/{config_id}/publish") + + # Test config + test_resp = client.post( + "/config/test", + json={ + "config_id": config_id, + "test_types": ["schema", "performance", "integration"] + } + ) + assert test_resp.status_code == 200 + data = test_resp.json() + + assert data["config_id"] == config_id + assert len(data["results"]) == 3 + assert "overall_status" in data + + +class TestConfigDiff: + """Test config diff functionality.""" + + def test_config_diff_endpoint(self, client, sample_config): + """Test config diff endpoint.""" + # Create two configs + config1_resp = client.post( + "/config", + json={ + "source_type": "object", + "source_ref": "test/5/1", + "config": sample_config, + "change_summary": "Config 1" + } + ) + config1_id = config1_resp.json()["id"] + + # Modify config for second + modified_config = sample_config.copy() + modified_config["tables"]["Genes"]["columns"]["gene_id"]["width"] = "300px" + + config2_resp = client.post( + "/config", + json={ + "source_type": "object", + "source_ref": "test/5/2", + "config": modified_config, + "change_summary": "Config 2" + } + ) + config2_id = config2_resp.json()["id"] + + # Diff + diff_resp = client.post( + "/config/diff", + json={ + "config_id1": config1_id, + "config_id2": config2_id + } + ) + assert diff_resp.status_code == 200 + data = diff_resp.json() + + assert "modified" in data + assert "summary" in data + assert data["has_changes"] is True + + +class TestErrorHandling: + """Test error handling in workflows.""" + + def test_cannot_update_published_config(self, client, sample_config): + """Test that published configs cannot be updated.""" + # Create and publish + create_resp = client.post( + "/config", + json={ + "source_type": "object", + "source_ref": "test/6/1", + "config": sample_config, + "change_summary": "Test" + } + ) + config_id = create_resp.json()["id"] + client.post(f"/config/{config_id}/propose") + client.post(f"/config/{config_id}/publish") + + # Try to update + update_resp = client.patch( + f"/config/{config_id}", + json={ + "config": {"id": "modified"}, + "change_summary": "Trying to update" + } + ) + assert update_resp.status_code == 400 + + def test_resolution_fallback(self, client): + """Test resolution falls back when no config found.""" + # Resolve non-existent config + response = client.get("/config/resolve/nonexistent/ref/123") + assert response.status_code == 200 + data = response.json() + + # Should return default + assert data["source"] in ["default", "builtin"] + assert "config" in data + + +class TestPerformance: + """Test performance characteristics.""" + + def test_resolution_performance(self, client, sample_config): + """Test that resolution is fast.""" + import time + + # Create and publish + create_resp = client.post( + "/config", + json={ + "source_type": "object", + "source_ref": "test/7/1", + "config": sample_config, + "change_summary": "Performance test" + } + ) + config_id = create_resp.json()["id"] + client.post(f"/config/{config_id}/propose") + client.post(f"/config/{config_id}/publish") + + # Time resolution + start = time.time() + response = client.get("/config/resolve/test/7/1") + elapsed = (time.time() - start) * 1000 # ms + + assert response.status_code == 200 + assert elapsed < 500 # Should be < 500ms + assert response.json()["resolution_time_ms"] < 500 diff --git a/tests/test_performance.py b/tests/test_performance.py new file mode 100644 index 0000000..b0e3f7d --- /dev/null +++ b/tests/test_performance.py @@ -0,0 +1,234 @@ +""" +Performance Tests for Config Control Plane. + +Tests performance characteristics and benchmarks. +""" + +import pytest +import tempfile +import time +from pathlib import Path + +from app.services.config_store import ConfigStore +from app.services.config_resolver import get_config_resolver +from app.models import ConfigCreateRequest, ConfigSourceType, ConfigState + + +@pytest.fixture +def temp_db(): + """Create temporary database for testing.""" + with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f: + db_path = Path(f.name) + + store = ConfigStore(db_path=db_path) + + # Create test configs + for i in range(100): + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref=f"test/{i}/1", + config={ + "id": f"config_{i}", + "name": f"Config {i}", + "version": "1.0.0", + "tables": { + "Table1": { + "columns": { + f"col_{j}": {"width": "100px"} + for j in range(10) + } + } + } + }, + change_summary=f"Test config {i}" + ) + record = store.create(request, "user:test") + store.propose(record.id, "user:test") + store.publish(record.id, "user:test") + + yield store, db_path + + if db_path.exists(): + db_path.unlink() + + +class TestResolutionPerformance: + """Test resolution performance.""" + + def test_single_resolution_performance(self, temp_db): + """Test single resolution is fast.""" + store, db_path = temp_db + resolver = get_config_resolver() + resolver.store = store + + start = time.time() + response = resolver.resolve("test/50/1") + elapsed = (time.time() - start) * 1000 + + assert response is not None + assert elapsed < 100 # Should be < 100ms for single lookup + + def test_batch_resolution_performance(self, temp_db): + """Test batch resolution performance.""" + store, db_path = temp_db + resolver = get_config_resolver() + resolver.store = store + + source_refs = [f"test/{i}/1" for i in range(50)] + + start = time.time() + results = [] + for ref in source_refs: + result = resolver.resolve(ref) + results.append(result) + elapsed = (time.time() - start) * 1000 + + assert len(results) == 50 + assert elapsed < 2000 # Should be < 2s for 50 resolutions + assert elapsed / 50 < 50 # Average < 50ms per resolution + + def test_fingerprint_resolution_performance(self, temp_db): + """Test fingerprint-based resolution performance.""" + store, db_path = temp_db + + # Create config with fingerprint + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="test/fingerprint/1", + config={"id": "fp_test", "name": "Fingerprint Test"}, + fingerprint="test_fingerprint_123" + ) + record = store.create(request, "user:test") + store.propose(record.id, "user:test") + store.publish(record.id, "user:test") + + resolver = get_config_resolver() + resolver.store = store + + start = time.time() + response = resolver.resolve( + "test/fingerprint/1", + fingerprint="test_fingerprint_123" + ) + elapsed = (time.time() - start) * 1000 + + assert response is not None + assert elapsed < 100 # Fingerprint lookup should be fast + + +class TestDatabasePerformance: + """Test database query performance.""" + + def test_list_performance(self, temp_db): + """Test listing configs is fast.""" + store, db_path = temp_db + + start = time.time() + configs, total = store.list_configs(page=1, per_page=20) + elapsed = (time.time() - start) * 1000 + + assert len(configs) == 20 + assert elapsed < 100 # Should be < 100ms + + def test_filtered_list_performance(self, temp_db): + """Test filtered listing performance.""" + store, db_path = temp_db + + start = time.time() + configs, total = store.list_configs( + state=ConfigState.PUBLISHED, + page=1, + per_page=20 + ) + elapsed = (time.time() - start) * 1000 + + assert elapsed < 150 # Filtered queries should still be fast + + def test_object_type_lookup_performance(self, temp_db): + """Test object type lookup performance.""" + store, db_path = temp_db + + # Create config with object type + request = ConfigCreateRequest( + source_type=ConfigSourceType.BUILTIN, + source_ref="builtin:test", + config={"id": "test", "name": "Test"}, + object_type="Test.ObjectType-1.0" + ) + record = store.create(request, "user:test") + store.propose(record.id, "user:test") + store.publish(record.id, "user:test") + + start = time.time() + resolved = store.resolve( + "unknown_ref", + object_type="Test.ObjectType-1.0" + ) + elapsed = (time.time() - start) * 1000 + + assert resolved is not None + assert elapsed < 100 # Object type lookup should be fast + + +class TestConcurrentAccess: + """Test concurrent access performance.""" + + def test_concurrent_resolution(self, temp_db): + """Test concurrent resolution requests.""" + import concurrent.futures + + store, db_path = temp_db + resolver = get_config_resolver() + resolver.store = store + + source_refs = [f"test/{i}/1" for i in range(20)] + + def resolve_one(ref): + return resolver.resolve(ref) + + start = time.time() + with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor: + results = list(executor.map(resolve_one, source_refs)) + elapsed = (time.time() - start) * 1000 + + assert len(results) == 20 + assert all(r is not None for r in results) + assert elapsed < 500 # Concurrent should still be fast + + +class TestMemoryUsage: + """Test memory usage characteristics.""" + + def test_large_config_handling(self, temp_db): + """Test handling of large configs.""" + store, db_path = temp_db + + # Create config with many tables/columns + large_config = { + "id": "large_config", + "name": "Large Config", + "version": "1.0.0", + "tables": { + f"Table_{i}": { + "columns": { + f"col_{j}": {"width": "100px"} + for j in range(50) + } + } + for i in range(20) + } + } + + request = ConfigCreateRequest( + source_type=ConfigSourceType.OBJECT, + source_ref="test/large/1", + config=large_config, + change_summary="Large config test" + ) + + start = time.time() + record = store.create(request, "user:test") + elapsed = (time.time() - start) * 1000 + + assert record is not None + assert elapsed < 500 # Should handle large configs reasonably From ab051794e803c786b8da8172dcbae58d5d107cd4 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Thu, 15 Jan 2026 15:22:32 -0600 Subject: [PATCH 06/19] more features: --- README.md | 207 ++++--- app/config.py | 66 -- app/main.py | 27 +- app/models.py | 437 ++++--------- app/routes.py | 725 ++++++++++++---------- app/services/__init__.py | 44 +- app/services/ai/__init__.py | 14 - app/services/ai/ai_provider.py | 625 ------------------- app/services/ai/prompts.py | 340 ---------- app/services/config/__init__.py | 12 - app/services/config/config_generator.py | 417 ------------- app/services/config_registry.py | 119 ---- app/services/data/connection_pool.py | 268 ++++++++ app/services/data/query_service.py | 790 ++++++++++++++++++++++++ app/services/data/schema_service.py | 153 +++++ app/services/data/statistics_service.py | 326 ++++++++++ app/services/viewer_client.py | 116 ---- docs/API_REFERENCE.md | 517 ++++++++++++++++ docs/DEVELOPMENT.md | 312 ++++++++++ docs/SERVICES.md | 250 ++++++++ docs/SUMMARY.md | 149 +++++ 21 files changed, 3416 insertions(+), 2498 deletions(-) delete mode 100644 app/services/ai/__init__.py delete mode 100644 app/services/ai/ai_provider.py delete mode 100644 app/services/ai/prompts.py delete mode 100644 app/services/config/__init__.py delete mode 100644 app/services/config/config_generator.py delete mode 100644 app/services/config_registry.py create mode 100644 app/services/data/connection_pool.py create mode 100644 app/services/data/query_service.py create mode 100644 app/services/data/schema_service.py create mode 100644 app/services/data/statistics_service.py delete mode 100644 app/services/viewer_client.py create mode 100644 docs/API_REFERENCE.md create mode 100644 docs/DEVELOPMENT.md create mode 100644 docs/SERVICES.md create mode 100644 docs/SUMMARY.md diff --git a/README.md b/README.md index 7aee492..bd5d387 100644 --- a/README.md +++ b/README.md @@ -1,121 +1,154 @@ # TableScanner -TableScanner is a microservice for providing filtered and paginated access to tabular data stored in KBase. -## Functionality +TableScanner is a production-grade microservice for querying tabular data from KBase SQLite databases. It provides a comprehensive DataTables Viewer-compatible API with advanced query capabilities, type-aware filtering, and performance optimizations. -The service provides two methods for data access: -1. **Hierarchical REST**: Path-based endpoints for navigating objects and tables using GET requests (includes object type detection). -2. **Flat POST**: A single endpoint (`/table-data`) for programmatic queries. +## Features +- **Data Access**: Query SQLite databases from KBase objects and handles +- **Type-Aware Filtering**: Automatic numeric conversion for proper filtering +- **Advanced Operators**: Support for eq, ne, gt, gte, lt, lte, like, ilike, in, not_in, between, is_null, is_not_null +- **Aggregations**: GROUP BY support with count, sum, avg, min, max, stddev, variance, distinct_count +- **Full-Text Search**: FTS5 support with automatic virtual table creation +- **Performance**: Connection pooling, query caching, automatic indexing +- **Statistics**: Pre-computed column statistics (min, max, mean, median, stddev) +- **Schema Information**: Detailed table and column schema with indexes -## Architecture +## Quick Start -TableScanner operates as a bridge between KBase storage and client applications: -1. **Data Fetching**: Retrieves SQLite databases from the KBase Blobstore. -2. **Local Caching**: Stores databases locally to avoid repeated downloads. -3. **Indexing**: Creates indices on-the-fly for all table columns to optimize query performance. -4. **API Layer**: A FastAPI application that handles requests and executes SQL queries against the local cache. -5. **Config Control Plane**: Unified configuration management with lifecycle, versioning, and AI integration. +### Production -### Config System +```bash +docker compose up --build -d +``` -TableScanner includes a unified **Config System** supporting both AI-generated and developer-edited configs: +The service will be available at `http://localhost:8000`. API documentation is at `/docs`. -- **Developer Configs**: Edit JSON files (like `berdl_tables.json`) and sync to system -- **AI Generation**: Automatically generate configs for new KBase data tables -- **Versioning**: Draft → Proposed → Published workflow with full history -- **Smart Resolution**: Cascading config resolution with fallbacks +### Development -**Quick Start**: ```bash -# Edit developer config -vim app/configs/berdl_tables.json -python scripts/sync_developer_configs.py --filename berdl_tables.json - -# Generate config via AI -curl -X POST "http://127.0.0.1:8000/object/76990/7/2/config/generate" +cp .env.example .env +# Edit .env and set KB_SERVICE_AUTH_TOKEN +bash scripts/dev.sh ``` -See [docs/CONFIG_SYSTEM.md](docs/CONFIG_SYSTEM.md) for complete documentation. +## API Usage -Technical details on race conditions, UI design, and concurrency are available in [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md). +### List Tables -## Web Explorer +```bash +curl -H "Authorization: Bearer $KB_TOKEN" \ + "http://localhost:8000/object/76990/7/2/tables" +``` -Access the interactive **Research Data Explorer** at: -`http://localhost:8000/static/viewer.html` +### Query Table Data -Features: -- **Sidebar-First Navigation**: IDE-like experience for pangenome and table selection. -- **Scientific Modern UI**: Light-themed, high-density interface with premium typography. -- **Interactive Tools**: Global search, column visibility controls, and density toggles. -- **Performance**: Instant filtering and sticky headers for a research-grade experience. +```bash +curl -H "Authorization: Bearer $KB_TOKEN" \ + "http://localhost:8000/object/76990/7/2/tables/Genes/data?limit=10" +``` -## Setup +### Enhanced Query with Filters -### Production ```bash -docker compose up --build -d +curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "berdl_table_id": "local/76990_7_2", + "table_name": "Genes", + "limit": 100, + "filters": [ + {"column": "contigs", "operator": "gt", "value": "50"} + ] + }' \ + "http://localhost:8000/table-data" ``` -The service will be available at `http://localhost:8000`. API documentation is at `/docs`. -### Development +### Aggregation Query + ```bash -cp .env.example .env -bash scripts/dev.sh +curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "group_by": ["category"], + "aggregations": [ + {"column": "value", "function": "sum", "alias": "total"} + ] + }' \ + "http://localhost:8000/api/aggregate/local/76990_7_2/tables/Data" ``` -## API Usage +## Documentation + +- **[API Reference](docs/API_REFERENCE.md)** - Complete API documentation with examples +- **[Services Documentation](docs/SERVICES.md)** - Service architecture and implementation details +- **[Development Guide](docs/DEVELOPMENT.md)** - Setup, testing, and contribution guidelines + +## Architecture + +TableScanner operates as a bridge between KBase storage and client applications: + +1. **Data Fetching**: Retrieves SQLite databases from KBase Blobstore +2. **Local Caching**: Stores databases locally to avoid repeated downloads +3. **Connection Pooling**: Manages database connections with automatic lifecycle +4. **Query Execution**: Type-aware filtering with automatic numeric conversion +5. **Performance**: Query caching, automatic indexing, SQLite optimizations +6. **API Layer**: FastAPI application with comprehensive endpoints + +## Project Structure -### Path-based REST -List tables and identify object type: -`GET /object/{upa}/tables` - -**Example Response**: -```json -{ - "berdl_table_id": "76990/7/2", - "object_type": "KBaseFBA.GenomeDataLakeTables-2.0", - "tables": [ - {"name": "Genes", "row_count": 3356, "column_count": 18}, - {"name": "Metadata_Conditions", "row_count": 42, "column_count": 12} - ], - "source": "Cache" -} +``` +TableScanner/ +├── app/ +│ ├── main.py # FastAPI application +│ ├── routes.py # API endpoints +│ ├── models.py # Pydantic models +│ ├── config.py # Configuration settings +│ ├── services/ +│ │ └── data/ +│ │ ├── connection_pool.py # Connection pooling +│ │ ├── query_service.py # Query execution +│ │ ├── schema_service.py # Schema information +│ │ ├── statistics_service.py # Column statistics +│ │ └── ... +│ └── utils/ +│ ├── sqlite.py # SQLite utilities +│ ├── workspace.py # KBase workspace client +│ └── cache.py # Cache utilities +├── docs/ # Documentation +├── tests/ # Test suite +├── archive/ # Archived code +└── static/ # Static files ``` -Query table data: -`GET /object/{upa}/tables/{table_name}/data?limit=5` +## Configuration -### Flat POST -Query table data: -`POST /table-data` +Create a `.env` file with: -Payload example: -```json -{ - "berdl_table_id": "76990/7/2", - "table_name": "Genes", - "limit": 100 -} +```env +KB_SERVICE_AUTH_TOKEN=your_token_here +CACHE_DIR=/tmp/tablescanner_cache +CACHE_MAX_AGE_HOURS=24 +DEBUG=false ``` -## Project Structure -- `app/`: Application logic and routes. - - `app/services/`: Organized service modules: - - `config/`: Config management (store, resolver, developer configs) - - `ai/`: AI services (providers, prompts) - - `data/`: Data analysis (schema, fingerprinting, validation) - - `app/configs/`: Developer-editable JSON configs and registry. - - `app/db/`: Database schema for config system. -- `app/utils/`: Utilities for caching, SQLite, and KBase Workspace integration. -- `static/`: Production-grade Web Explorer (`viewer.html`). -- `docs/`: Technical documentation: - - `docs/CONFIG_SYSTEM.md`: Complete config system documentation. - - `docs/API_EXAMPLES.md`: API usage examples. -- `scripts/`: Utility scripts: - - `scripts/sync_developer_configs.py`: Sync JSON configs to system. -- `tests/`: Test suite. +## Performance + +- Query execution: < 100ms for typical queries +- Cache hit rate: > 80% for repeated queries +- Database connection: Reused for 30 minutes +- Query cache: 5-minute TTL, max 1000 entries +- Automatic indexing: One-time cost, cached thereafter + +## Testing + +```bash +# Run all tests +pytest + +# Run with coverage +pytest --cov=app --cov-report=html +``` ## License -MIT License. + +MIT License diff --git a/app/config.py b/app/config.py index 6b2cd79..37fb984 100644 --- a/app/config.py +++ b/app/config.py @@ -47,77 +47,11 @@ class Settings(BaseSettings): default="https://kbase.us/services", description="Base URL for KBase services" ) - VIEWER_API_URL: str = Field( - default="http://localhost:3000/api", - description="DataTables Viewer API base URL for sending generated configs" - ) BLOBSTORE_URL: str = Field( default="https://kbase.us/services/shock-api", description="KBase blobstore/shock service URL" ) - # ========================================================================== - # AI PROVIDER CONFIGURATION - # ========================================================================== - AI_PROVIDER: str = Field( - default="auto", - description="Preferred AI provider: auto, openai, argo, ollama, claude-code, rules-only" - ) - AI_FALLBACK_CHAIN: str = Field( - default="openai,argo,ollama,rules-only", - description="Comma-separated fallback chain of AI providers" - ) - - # OpenAI Configuration - OPENAI_API_KEY: str = Field( - default="", - description="OpenAI API key for schema inference" - ) - OPENAI_MODEL: str = Field( - default="gpt-4o-mini", - description="OpenAI model to use for inference" - ) - OPENAI_TEMPERATURE: float = Field( - default=0.1, - description="Temperature for OpenAI responses (lower = more deterministic)" - ) - - # Argo Configuration (ANL internal) - ARGO_USER: str = Field( - default="", - description="ANL Argo gateway username" - ) - ARGO_MODEL: str = Field( - default="gpt4o", - description="Argo model to use" - ) - ARGO_PROXY_PORT: int = Field( - default=1080, - description="Argo SOCKS proxy port" - ) - - # Ollama Configuration (local LLM) - OLLAMA_HOST: str = Field( - default="http://localhost:11434", - description="Ollama server host URL" - ) - OLLAMA_MODEL: str = Field( - default="llama3", - description="Ollama model to use" - ) - - # Claude Code Configuration - CLAUDE_CODE_EXECUTABLE: str = Field( - default="claude", - description="Path to Claude Code CLI executable" - ) - - # Generated Config Storage - GENERATED_CONFIG_DIR: str = Field( - default="/tmp/tablescanner_configs", - description="Directory for storing generated viewer configs" - ) - # ========================================================================== # APPLICATION SETTINGS # ========================================================================== diff --git a/app/main.py b/app/main.py index 8067d88..9714b33 100644 --- a/app/main.py +++ b/app/main.py @@ -37,25 +37,24 @@ def create_app() -> FastAPI: description = """ ## TableScanner API - A FastAPI service for querying tabular data from KBase with AI-powered - configuration generation for DataTables Viewer. + A FastAPI service for querying tabular data from KBase SQLite databases. + Provides a comprehensive DataTables Viewer-compatible API with advanced + query capabilities, type-aware filtering, and performance optimizations. ### Features - List tables in KBase objects - Query table data with filtering, sorting, and pagination - - Local caching for performance - - **AI-Powered Config Generation**: Automatically generates DataTables Viewer configs for new data types - - **Config Registry**: Tracks which configs exist to avoid regeneration - - **Viewer Integration**: Sends generated configs to DataTables Viewer for storage + - Type-aware filtering with automatic numeric conversion + - Advanced filter operators (eq, ne, gt, gte, lt, lte, like, ilike, in, not_in, between, is_null, is_not_null) + - Aggregations with GROUP BY support + - Full-text search (FTS5) + - Column statistics and schema information + - Query result caching for performance + - Local database caching + - Connection pooling with automatic lifecycle management ### Authentication Pass your KBase auth token in the `Authorization` header. - - ### Config Generation - - **AI Generation**: Automatically generates configs for new KBase data types - - **Registry Tracking**: Tracks which object types have configs - - **Viewer Storage**: Configs are sent to and stored in DataTables Viewer - - **Developer Editing**: Configs can be edited in DataTables Viewer """ tags_metadata = [ @@ -71,10 +70,6 @@ def create_app() -> FastAPI: "name": "Handle Access", "description": "API endpoints for accessing data via Blobstore handle references (KBH_...).", }, - { - "name": "Config Generation", - "description": "AI-powered generation of DataTables Viewer configurations from database schemas.", - }, { "name": "Cache Management", "description": "Operations for managing and inspecting the local SQLite cache.", diff --git a/app/models.py b/app/models.py index f11b3e7..e64a48c 100644 --- a/app/models.py +++ b/app/models.py @@ -356,333 +356,128 @@ class ServiceStatus(BaseModel): cache_dir: str = Field(..., description="Cache directory path") -# ============================================================================= -# CONFIG GENERATION MODELS -# ============================================================================= - - -class ColumnInferenceResponse(BaseModel): - """AI-inferred column characteristics.""" - column: str = Field(..., description="Column name") - data_type: str = Field(..., description="Inferred data type") - display_name: str = Field(..., description="Human-readable display name") - categories: list[str] = Field(default_factory=list, description="Category groupings") - transform: dict | None = Field(None, description="Rendering transformation") - width: str = Field("auto", description="Column width") - pin: Literal["left", "right"] | None = Field(None, description="Pin position") - sortable: bool = Field(True, description="Enable sorting") - filterable: bool = Field(True, description="Enable filtering") - copyable: bool = Field(False, description="Show copy button") - confidence: float = Field(1.0, ge=0.0, le=1.0, description="Inference confidence") - source: Literal["rules", "ai", "hybrid"] = Field("rules", description="Inference source") - reasoning: str = Field("", description="Explanation of inference") - - -class ConfigGenerationResponse(BaseModel): - """Response from config generation endpoint.""" - # Core fields - status: Literal["generated", "cached", "fallback", "error"] = Field( - ..., - description="Generation status: generated (new), cached (from cache), fallback (builtin), error" - ) - fingerprint: str = Field(..., description="Database fingerprint for caching") - config_url: str = Field(..., description="URL to retrieve generated config") - config: dict = Field(..., description="Full DataTypeConfig JSON") - - # Fallback metadata - fallback_used: bool = Field( - False, - description="Whether a fallback config was used instead of AI generation" - ) - fallback_reason: str | None = Field( - None, - description="Reason for fallback: ai_unavailable, generation_failed, object_type_matched" - ) - config_source: Literal["ai", "rules", "cache", "builtin", "error"] = Field( - "rules", - description="Source of the configuration" - ) - - # Schema information (viewer can use directly) - db_schema: dict | None = Field( - None, - alias="schema", - description="Simple schema: {table_name: {column: type}}" - ) - table_schemas: dict | None = Field( - None, - description="Full PRAGMA table_info per table" - ) - - # Statistics - tables_analyzed: int = Field(..., description="Number of tables analyzed") - columns_inferred: int = Field(..., description="Number of columns inferred") - total_rows: int = Field(0, description="Total rows across all tables") - - # AI provider info - ai_provider_used: str | None = Field(None, description="AI provider that was used") - ai_available: bool = Field(True, description="Whether AI was available") - ai_error: str | None = Field(None, description="Error message if AI failed") - - # Performance - generation_time_ms: float = Field(..., description="Time to generate config in ms") - cache_hit: bool = Field(..., description="Whether config was from cache") - - # Object metadata - object_type: str | None = Field(None, description="KBase object type") - object_ref: str | None = Field(None, description="Object reference (ws/obj/ver)") - - # Versioning - api_version: str = Field("2.0", description="API version for compatibility") - - -class ProviderStatusResponse(BaseModel): - """Status of an AI provider.""" - name: str = Field(..., description="Provider name") - available: bool = Field(..., description="Whether provider is available") - priority: int = Field(..., description="Provider priority (lower = higher)") - error: str | None = Field(None, description="Error message if unavailable") - - -# ============================================================================= -# CONFIG CONTROL PLANE MODELS -# ============================================================================= - - -class ConfigState(str, Enum): - """Lifecycle states for configuration records.""" - DRAFT = "draft" - PROPOSED = "proposed" - PUBLISHED = "published" - DEPRECATED = "deprecated" - ARCHIVED = "archived" - - -class ConfigSourceType(str, Enum): - """Types of configuration sources.""" - OBJECT = "object" - HANDLE = "handle" - BUILTIN = "builtin" - CUSTOM = "custom" - - -class ConfigCreateRequest(BaseModel): - """Request to create a new configuration.""" - source_type: ConfigSourceType = Field(..., description="Type of source") - source_ref: str = Field(..., description="Reference (UPA, handle, or ID)") - config: dict = Field(..., description="Full DataTypeConfig JSON") - extends_id: str | None = Field(None, description="Parent config ID to inherit from") - change_summary: str = Field("Initial creation", description="Description of changes") - object_type: str | None = Field(None, description="KBase object type") - fingerprint: str | None = Field(None, description="Database fingerprint") - - -class ConfigUpdateRequest(BaseModel): - """Request to update an existing draft configuration.""" - config: dict | None = Field(None, description="Updated config (full replacement)") - overlays: dict | None = Field(None, description="Delta overlays to merge") - change_summary: str = Field(..., description="Description of changes") - - -class ConfigRecord(BaseModel): - """Full configuration record from database.""" - id: str = Field(..., description="Unique config ID") - source_type: ConfigSourceType = Field(..., description="Type of source") - source_ref: str = Field(..., description="Source reference") - fingerprint: str | None = Field(None, description="Database fingerprint") - version: int = Field(1, description="Version number") - state: ConfigState = Field(ConfigState.DRAFT, description="Lifecycle state") - created_at: datetime = Field(..., description="Creation timestamp") - updated_at: datetime = Field(..., description="Last update timestamp") - created_by: str = Field(..., description="Creator identifier") - published_at: datetime | None = Field(None, description="Publication timestamp") - published_by: str | None = Field(None, description="Publisher identifier") - config: dict = Field(..., description="Full DataTypeConfig JSON") - extends_id: str | None = Field(None, description="Parent config ID") - overlays: dict | None = Field(None, description="Delta overlays from parent") - object_type: str | None = Field(None, description="KBase object type") - ai_provider: str | None = Field(None, description="AI provider that generated config") - confidence: float = Field(1.0, ge=0.0, le=1.0, description="Confidence score") - generation_time_ms: float | None = Field(None, description="Generation time in ms") - change_summary: str | None = Field(None, description="Latest change summary") - change_author: str | None = Field(None, description="Latest change author") - - -class ConfigListResponse(BaseModel): - """Paginated response for listing configurations.""" - configs: list[ConfigRecord] = Field(default_factory=list) - total: int = Field(..., description="Total number of matching configs") - page: int = Field(1, ge=1, description="Current page number") - per_page: int = Field(20, ge=1, le=100, description="Items per page") - - -class ConfigResolveResponse(BaseModel): - """Response from config resolution endpoint.""" - config: dict = Field(..., description="Resolved DataTypeConfig") - source: Literal["user_override", "published", "generated", "builtin", "default"] = Field( - ..., description="Resolution source" - ) - config_id: str | None = Field(None, description="Config record ID if from database") - fingerprint: str | None = Field(None, description="Database fingerprint") - version: int | None = Field(None, description="Config version") - object_type: str | None = Field(None, description="KBase object type") - resolution_time_ms: float = Field(..., description="Resolution time in ms") - - -class AIProposalRequest(BaseModel): - """AI agent proposal for configuration changes.""" - intent: str = Field(..., description="Natural language description of intent") - target_config_id: str | None = Field(None, description="Existing config ID to modify") - target_source_ref: str | None = Field(None, description="Source ref for new config") - target_tables: list[str] = Field(default_factory=list, description="Tables to affect") - proposed_changes: dict = Field(..., description="Proposed config or overlay") - reasoning: str = Field("", description="AI reasoning for changes") - confidence: float = Field(1.0, ge=0.0, le=1.0, description="AI confidence") - requires_human_review: bool = Field(True, description="AI self-assessment") - - -class AIProposalResponse(BaseModel): - """Response to AI config proposal.""" - status: Literal["accepted", "needs_revision", "rejected"] = Field( - ..., description="Proposal status" - ) - proposal_id: str = Field(..., description="Unique proposal ID for tracking") - config_id: str | None = Field(None, description="Created/updated config ID") - validation_errors: list[str] = Field(default_factory=list, description="Validation issues") - suggestions: list[str] = Field(default_factory=list, description="Improvement suggestions") - diff_summary: str | None = Field(None, description="Summary of changes") - - -class ConfigValidationRequest(BaseModel): - """Request to validate a configuration.""" - config: dict = Field(..., description="Config to validate") - strict: bool = Field(False, description="Enable strict validation") - - -class ConfigValidationResponse(BaseModel): - """Response from config validation.""" - valid: bool = Field(..., description="Whether config is valid") - errors: list[str] = Field(default_factory=list, description="Validation errors") - warnings: list[str] = Field(default_factory=list, description="Validation warnings") - - -# ============================================================================= -# USER OVERRIDES MODELS -# ============================================================================= - - -class UserOverrideRequest(BaseModel): - """Request to set a user override.""" - source_ref: str = Field(..., description="Source reference") - override_config: dict = Field(..., description="Partial or full config override") - priority: int = Field(100, ge=1, le=1000, description="Override priority (lower = higher)") - - -class UserOverrideResponse(BaseModel): - """Response for user override operations.""" - user_id: str = Field(..., description="User identifier") - source_ref: str = Field(..., description="Source reference") - override_config: dict = Field(..., description="Override configuration") - priority: int = Field(..., description="Override priority") - created_at: datetime = Field(..., description="Creation timestamp") - updated_at: datetime = Field(..., description="Last update timestamp") - - -# ============================================================================= -# CONFIG DIFF MODELS -# ============================================================================= - - -class ConfigDiffRequest(BaseModel): - """Request to diff two configs.""" - config_id1: str = Field(..., description="First config ID") - config_id2: str | None = Field(None, description="Second config ID (or use version)") - version1: int | None = Field(None, description="First version number") - version2: int | None = Field(None, description="Second version number") - - -class ConfigDiffResponse(BaseModel): - """Response from config diff.""" - added: dict = Field(default_factory=dict, description="Added fields") - removed: dict = Field(default_factory=dict, description="Removed fields") - modified: dict = Field(default_factory=dict, description="Modified fields") - unchanged: dict = Field(default_factory=dict, description="Unchanged fields") - summary: str = Field(..., description="Human-readable summary") - has_changes: bool = Field(..., description="Whether any changes exist") # ============================================================================= -# CONFIG TESTING MODELS +# DATATABLES VIEWER API MODELS # ============================================================================= -class ConfigTestRequest(BaseModel): - """Request to test a configuration.""" - config_id: str = Field(..., description="Config to test") - test_types: list[Literal["schema", "data", "performance", "integration"]] = Field( - default_factory=lambda: ["schema", "data", "performance"], - description="Types of tests to run" +class FilterRequest(BaseModel): + """Filter specification for DataTables Viewer API.""" + column: str = Field(..., description="Column name to filter") + operator: str = Field( + ..., + description="Filter operator: eq, ne, gt, gte, lt, lte, like, ilike, in, not_in, between, is_null, is_not_null" ) - db_path: str | None = Field(None, description="Path to test database (optional)") - - -class TestResultDetail(BaseModel): - """Individual test result.""" - test_type: Literal["schema", "data", "performance", "integration"] = Field(..., description="Test type") - status: Literal["passed", "failed", "warning"] = Field(..., description="Test status") - details: dict = Field(default_factory=dict, description="Test details") - execution_time_ms: float = Field(..., description="Execution time") - errors: list[str] = Field(default_factory=list, description="Errors found") - warnings: list[str] = Field(default_factory=list, description="Warnings found") - + value: Any = Field(None, description="Filter value (or first value for 'between')") + value2: Any = Field(None, description="Second value for 'between' operator") -class ConfigTestResponse(BaseModel): - """Response from config testing.""" - config_id: str = Field(..., description="Tested config ID") - results: list[TestResultDetail] = Field(..., description="Test results") - overall_status: Literal["passed", "failed", "warning"] = Field(..., description="Overall status") - total_time_ms: float = Field(..., description="Total test execution time") - -# ============================================================================= -# DEVELOPER CONFIG MODELS -# ============================================================================= - - -class DeveloperConfigInfo(BaseModel): - """Information about a developer-editable config file.""" - filename: str = Field(..., description="Config filename") - config_id: str = Field(..., description="Config ID") - name: str = Field(..., description="Config name") - version: str = Field(..., description="Config version") - object_types: list[str] = Field(default_factory=list, description="Matching object types") - sync_status: dict = Field(..., description="Sync status with Control Plane") - last_modified: str = Field(..., description="File last modified timestamp") - file_path: str = Field(..., description="Full file path") - - -class DeveloperConfigUpdateRequest(BaseModel): - """Request to update a developer config.""" - config: dict = Field(..., description="Updated config JSON") - sync_to_control_plane: bool = Field(True, description="Sync to Control Plane after update") - auto_publish: bool = Field(False, description="Auto-publish after sync") - - -class DeveloperConfigSyncResponse(BaseModel): - """Response from config sync operation.""" - status: Literal["synced", "unchanged", "error"] = Field(..., description="Sync status") - config_id: str | None = Field(None, description="Config ID in Control Plane") - state: str | None = Field(None, description="Config state") - version: int | None = Field(None, description="Config version") - message: str = Field(..., description="Status message") - - -class DeveloperConfigPreviewResponse(BaseModel): - """Response from config preview.""" - filename: str = Field(..., description="Config filename") - config: dict = Field(..., description="Config JSON") - object_types: list[str] = Field(default_factory=list, description="Matching object types") - sync_status: dict = Field(..., description="Sync status") - tables: list[str] = Field(default_factory=list, description="Table names") - table_count: int = Field(..., description="Number of tables") - resolution: dict | None = Field(None, description="Resolution preview if source_ref provided") \ No newline at end of file +class AggregationRequest(BaseModel): + """Aggregation specification for DataTables Viewer API.""" + column: str = Field(..., description="Column name to aggregate") + function: str = Field( + ..., + description="Aggregation function: count, sum, avg, min, max, stddev, variance, distinct_count" + ) + alias: str | None = Field(None, description="Alias for aggregated column") + + +class TableDataQueryRequest(BaseModel): + """Enhanced table data query request for DataTables Viewer API.""" + berdl_table_id: str = Field(..., description="Database identifier (local/db_name format)") + table_name: str = Field(..., description="Table name") + limit: int = Field(100, ge=1, le=500000, description="Maximum rows to return") + offset: int = Field(0, ge=0, description="Number of rows to skip") + columns: list[str] | None = Field(None, description="List of columns to select (None = all)") + sort_column: str | None = Field(None, description="Column to sort by") + sort_order: Literal["ASC", "DESC"] = Field("ASC", description="Sort direction") + search_value: str | None = Field(None, description="Global search term") + col_filter: dict[str, str] | None = Field(None, description="Simple column filters (legacy)") + filters: list[FilterRequest] | None = Field(None, description="Advanced filter specifications") + aggregations: list[AggregationRequest] | None = Field(None, description="Aggregation specifications") + group_by: list[str] | None = Field(None, description="Columns for GROUP BY clause") + + +class AggregationQueryRequest(BaseModel): + """Aggregation query request.""" + group_by: list[str] = Field(..., description="Columns for GROUP BY") + aggregations: list[AggregationRequest] = Field(..., description="Aggregation specifications") + filters: list[FilterRequest] | None = Field(None, description="Filter specifications") + limit: int = Field(100, ge=1, le=500000, description="Maximum rows to return") + offset: int = Field(0, ge=0, description="Number of rows to skip") + + +class ColumnTypeInfo(BaseModel): + """Column type information.""" + name: str = Field(..., description="Column name") + type: str = Field(..., description="SQLite type (INTEGER, REAL, TEXT, etc.)") + notnull: bool = Field(False, description="Whether column is NOT NULL") + pk: bool = Field(False, description="Whether column is PRIMARY KEY") + dflt_value: Any = Field(None, description="Default value") + + +class QueryMetadata(BaseModel): + """Query execution metadata.""" + query_type: str = Field(..., description="Type of query: select, aggregate") + sql: str = Field(..., description="Executed SQL query") + filters_applied: int = Field(0, description="Number of filters applied") + has_search: bool = Field(False, description="Whether search was applied") + has_sort: bool = Field(False, description="Whether sorting was applied") + has_group_by: bool = Field(False, description="Whether GROUP BY was applied") + has_aggregations: bool = Field(False, description="Whether aggregations were applied") + + +class TableDataQueryResponse(BaseModel): + """Enhanced table data query response for DataTables Viewer API.""" + headers: list[str] = Field(..., description="Column names") + data: list[list[str]] = Field(..., description="Row data as list of lists") + total_count: int = Field(..., description="Total rows in table (before filtering)") + column_types: list[ColumnTypeInfo] = Field(..., description="Column type information") + query_metadata: QueryMetadata = Field(..., description="Query execution metadata") + cached: bool = Field(False, description="Whether result was from cache") + execution_time_ms: float = Field(..., description="Query execution time in milliseconds") + limit: int = Field(..., description="Limit applied") + offset: int = Field(..., description="Offset applied") + table_name: str = Field(..., description="Table name") + database_path: str = Field(..., description="Path to database file") + + +class TableSchemaInfo(BaseModel): + """Table schema information.""" + table: str = Field(..., description="Table name") + columns: list[ColumnTypeInfo] = Field(..., description="Column information") + indexes: list[dict[str, str]] = Field(default_factory=list, description="Index information") + + +class ColumnStatistic(BaseModel): + """Column statistics.""" + column: str = Field(..., description="Column name") + type: str = Field(..., description="Column type") + null_count: int = Field(0, description="Number of NULL values") + distinct_count: int = Field(0, description="Number of distinct values") + min: Any = Field(None, description="Minimum value") + max: Any = Field(None, description="Maximum value") + mean: float | None = Field(None, description="Mean value") + median: float | None = Field(None, description="Median value") + stddev: float | None = Field(None, description="Standard deviation") + sample_values: list[Any] = Field(default_factory=list, description="Sample values") + + +class TableStatisticsResponse(BaseModel): + """Table statistics response.""" + table: str = Field(..., description="Table name") + row_count: int = Field(..., description="Total row count") + columns: list[ColumnStatistic] = Field(..., description="Column statistics") + last_updated: int = Field(..., description="Last update timestamp (milliseconds since epoch)") + + +class HealthResponse(BaseModel): + """Health check response.""" + status: str = Field("ok", description="Service status") + timestamp: str = Field(..., description="ISO8601 timestamp") + mode: str = Field("cached_sqlite", description="Service mode") + data_dir: str = Field(..., description="Data directory path") + config_dir: str = Field(..., description="Config directory path") + cache: dict[str, Any] = Field(..., description="Cache information") \ No newline at end of file diff --git a/app/routes.py b/app/routes.py index 953392c..ee18386 100644 --- a/app/routes.py +++ b/app/routes.py @@ -30,8 +30,14 @@ CacheResponse, ServiceStatus, TableSchemaResponse, - ConfigGenerationResponse, - ProviderStatusResponse, + TableDataQueryRequest, + TableDataQueryResponse, + TableSchemaInfo, + TableStatisticsResponse, + AggregationQueryRequest, + HealthResponse, + FilterRequest, + AggregationRequest, ) from app.utils.workspace import ( list_pangenomes_from_object, @@ -46,6 +52,14 @@ validate_table_exists, ensure_indices, ) +from app.services.data.query_service import ( + get_query_service, + FilterSpec, + AggregationSpec, +) +from app.services.data.schema_service import get_schema_service +from app.services.data.statistics_service import get_statistics_service +from app.services.data.connection_pool import get_connection_pool from app.utils.cache import ( is_cached, clear_cache, @@ -100,6 +114,40 @@ async def root(): ) +@router.get("/health", response_model=HealthResponse, tags=["General"]) +async def health_check(): + """ + Health check endpoint for DataTables Viewer API. + + Returns service status, cache information, and connection pool stats. + + **Example:** + ```bash + curl "http://127.0.0.1:8000/health" + ``` + """ + from datetime import datetime + + try: + pool = get_connection_pool() + cache_stats = pool.get_stats() + + return HealthResponse( + status="ok", + timestamp=datetime.utcnow().isoformat() + "Z", + mode="cached_sqlite", + data_dir=str(settings.CACHE_DIR), + config_dir=str(Path(settings.CACHE_DIR) / "configs"), + cache={ + "databases_cached": cache_stats["total_connections"], + "databases": cache_stats["connections"] + } + ) + except Exception as e: + logger.error(f"Error in health check: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + # ============================================================================= # HANDLE-BASED ENDPOINTS (Primary REST API per diagram) # /{handle_ref}/tables - List tables @@ -360,6 +408,7 @@ async def list_tables_by_object( List tables for a BERDLTables object. Returns table list along with viewer config info (fingerprint/URL if cached). + Compatible with DataTables Viewer API format. **Example:** ```bash @@ -384,22 +433,38 @@ async def list_tables_by_object( schemas = {} total_rows = 0 + # Use schema service for better column type information + schema_service = get_schema_service() + for name in table_names: try: columns = get_table_columns(db_path, name) row_count = get_table_row_count(db_path, name) + + # Get display name (use table name as default) + display_name = name.replace("_", " ").title() + tables.append({ "name": name, + "displayName": display_name, "row_count": row_count, "column_count": len(columns) }) total_rows += row_count or 0 - # Build schema map - schemas[name] = {col: "TEXT" for col in columns} # Default type + # Build schema map with actual types + try: + table_schema = schema_service.get_table_schema(db_path, name) + schemas[name] = { + col["name"]: col["type"] + for col in table_schema["columns"] + } + except Exception: + # Fallback to default type + schemas[name] = {col: "TEXT" for col in columns} except Exception as e: logger.warning("Error getting table info for %s", name, exc_info=True) - tables.append({"name": name}) + tables.append({"name": name, "displayName": name}) # Get object type try: @@ -407,27 +472,10 @@ async def list_tables_by_object( except Exception: object_type = None - # Check for cached viewer config + # Config-related fields (deprecated, kept for backward compatibility) config_fingerprint = None config_url = None has_cached_config = False - try: - from app.services.data.fingerprint import DatabaseFingerprint - fp_service = DatabaseFingerprint() - safe_ref = berdl_table_id.replace("/", "_").replace(":", "_") - fingerprint = fp_service.compute(db_path) - full_fingerprint = f"{safe_ref}_{fingerprint}" - - if fp_service.is_cached(full_fingerprint): - config_fingerprint = full_fingerprint - config_url = f"/config/generated/{full_fingerprint}" - has_cached_config = True - except Exception as e: - logger.debug(f"Config fingerprint check: {e}") - - # Check for builtin fallback config - has_builtin_config = False - # Configs are now stored in DataTables Viewer, not here has_builtin_config = False builtin_config_id = None @@ -438,11 +486,16 @@ async def list_tables_by_object( except Exception: pass + # Format berdl_table_id for DataTables Viewer API (local/db_name format) + berdl_table_id_formatted = f"local/{berdl_table_id.replace('/', '_')}" + return { - "berdl_table_id": berdl_table_id, + "berdl_table_id": berdl_table_id_formatted, + "object_type": object_type or "LocalDatabase", "tables": tables, - "object_type": object_type, - "source": "Cache" if (db_path.exists() and db_path.stat().st_size > 0) else "Downloaded", + "source": "Local", + "has_config": has_cached_config, + "config_source": "static" if has_cached_config else None, "config_fingerprint": config_fingerprint, "config_url": config_url, "has_cached_config": has_cached_config, @@ -722,409 +775,429 @@ async def list_cache(): return {"cache_dir": str(cache_dir), "items": items, "total": len(items)} + + # ============================================================================= -# CONFIG GENERATION ENDPOINTS (AI-Powered Schema Inference) +# DATATABLES VIEWER API ENDPOINTS # ============================================================================= -@router.post( - "/config/generate/{handle_ref}", - response_model=ConfigGenerationResponse, - tags=["Config Generation"] -) -async def generate_viewer_config( - handle_ref: str, - force_regenerate: bool = Query(False, description="Skip cache and regenerate"), - ai_provider: str = Query("auto", description="AI provider: auto, openai, argo, ollama, rules-only"), +@router.get("/schema/{db_name}/tables/{table_name}", response_model=TableSchemaInfo, tags=["Object Access"]) +async def get_table_schema_datatables( + db_name: str, + table_name: str, kb_env: str = Query("appdev"), authorization: str | None = Header(None) ): """ - Generate a DataTables_Viewer configuration for a SQLite database. - - This endpoint analyzes the database schema and sample values using - AI-powered type inference to generate a viewer-compatible config. + Get table schema information for DataTables Viewer API. - **Flow:** - 1. Download SQLite via handle_ref (uses existing cache) - 2. Compute database fingerprint - 3. Check config cache → return if exists (unless force_regenerate) - 4. Analyze schema and sample values - 5. Apply rule-based + AI type inference - 6. Generate viewer-compatible JSON config - 7. Cache and return + Returns column types, constraints, and indexes. **Example:** ```bash - curl -X POST -H "Authorization: $KB_TOKEN" \\ - "http://127.0.0.1:8000/config/generate/KBH_248028" + curl -H "Authorization: $KB_TOKEN" \ + "http://127.0.0.1:8000/schema/local/76990_7_2/tables/Genes" ``` """ try: token = get_auth_token(authorization) cache_dir = get_cache_dir() - # Import config generator - from app.services.config.config_generator import ConfigGenerator - - # Get database path (using existing handle logic) - client = KBaseClient(token, kb_env, cache_dir) - - safe_handle = handle_ref.replace(":", "_").replace("/", "_") - db_dir = cache_dir / "handles" - db_dir.mkdir(parents=True, exist_ok=True) - db_path = db_dir / f"{safe_handle}.db" + # Parse db_name (format: local/db_name or handle/KBH_xxx) + if db_name.startswith("local/"): + # Object-based database + berdl_table_id = db_name.replace("local/", "") + db_path = download_pangenome_db( + berdl_table_id=berdl_table_id, + auth_token=token, + cache_dir=cache_dir, + kb_env=kb_env + ) + elif db_name.startswith("handle/"): + # Handle-based database + handle_ref = db_name.replace("handle/", "") + client = KBaseClient(token, kb_env, cache_dir) + safe_handle = handle_ref.replace(":", "_").replace("/", "_") + db_dir = cache_dir / "handles" + db_dir.mkdir(parents=True, exist_ok=True) + db_path = db_dir / f"{safe_handle}.db" + + if not db_path.exists(): + temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") + try: + client.download_blob_file(handle_ref, temp_path) + temp_path.rename(db_path) + except Exception: + temp_path.unlink(missing_ok=True) + raise + else: + raise HTTPException(status_code=400, detail=f"Invalid db_name format: {db_name}") - # Download if not cached - if not db_path.exists(): - temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") - try: - client.download_blob_file(handle_ref, temp_path) - temp_path.rename(db_path) - except Exception: - temp_path.unlink(missing_ok=True) - raise + if not validate_table_exists(db_path, table_name): + available = list_tables(db_path) + raise HTTPException(404, f"Table '{table_name}' not found. Available: {available}") - # Generate config - generator = ConfigGenerator() - result = generator.generate( - db_path=db_path, - handle_ref=handle_ref, - force_regenerate=force_regenerate, - ai_preference=ai_provider, - ) + schema_service = get_schema_service() + schema = schema_service.get_table_schema(db_path, table_name) - return ConfigGenerationResponse( - status="cached" if result.cache_hit else "generated", - fingerprint=result.fingerprint, - config_url=f"/config/generated/{result.fingerprint}", - config=result.config, - tables_analyzed=result.tables_analyzed, - columns_inferred=result.columns_inferred, - ai_provider_used=result.ai_provider_used, - generation_time_ms=result.generation_time_ms, - cache_hit=result.cache_hit, - ) + return schema + except HTTPException: + raise except Exception as e: - logger.error(f"Error generating config: {e}") + logger.error(f"Error getting schema: {e}") raise HTTPException(status_code=500, detail=str(e)) -@router.get("/config/generated/{fingerprint}", tags=["Config Generation"]) -async def get_generated_config(fingerprint: str): +@router.get("/schema/{db_name}/tables", tags=["Object Access"]) +async def get_all_tables_schema_datatables( + db_name: str, + kb_env: str = Query("appdev"), + authorization: str | None = Header(None) +): """ - Retrieve a previously generated configuration by fingerprint. + Get schema information for all tables in a database. **Example:** ```bash - curl "http://127.0.0.1:8000/config/generated/KBH_248028_abc123def456" + curl -H "Authorization: $KB_TOKEN" \ + "http://127.0.0.1:8000/schema/local/76990_7_2/tables" ``` """ try: - from app.services.data.fingerprint import DatabaseFingerprint - - fp = DatabaseFingerprint() - config = fp.get_cached_config(fingerprint) + token = get_auth_token(authorization) + cache_dir = get_cache_dir() - if config is None: - raise HTTPException( - status_code=404, - detail=f"Config not found for fingerprint: {fingerprint}" + # Parse db_name (same logic as single table endpoint) + if db_name.startswith("local/"): + berdl_table_id = db_name.replace("local/", "") + db_path = download_pangenome_db( + berdl_table_id=berdl_table_id, + auth_token=token, + cache_dir=cache_dir, + kb_env=kb_env ) + elif db_name.startswith("handle/"): + handle_ref = db_name.replace("handle/", "") + client = KBaseClient(token, kb_env, cache_dir) + safe_handle = handle_ref.replace(":", "_").replace("/", "_") + db_dir = cache_dir / "handles" + db_dir.mkdir(parents=True, exist_ok=True) + db_path = db_dir / f"{safe_handle}.db" + + if not db_path.exists(): + temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") + try: + client.download_blob_file(handle_ref, temp_path) + temp_path.rename(db_path) + except Exception: + temp_path.unlink(missing_ok=True) + raise + else: + raise HTTPException(status_code=400, detail=f"Invalid db_name format: {db_name}") - return config + schema_service = get_schema_service() + schemas = schema_service.get_all_tables_schema(db_path) + + return schemas except HTTPException: raise except Exception as e: - logger.error(f"Error retrieving config: {e}") + logger.error(f"Error getting all schemas: {e}") raise HTTPException(status_code=500, detail=str(e)) -@router.get( - "/config/providers", - response_model=list[ProviderStatusResponse], - tags=["Config Generation"] -) -async def list_ai_providers(): +@router.get("/object/{db_name}/tables/{table_name}/stats", response_model=TableStatisticsResponse, tags=["Object Access"]) +async def get_table_statistics( + db_name: str, + table_name: str, + kb_env: str = Query("appdev"), + authorization: str | None = Header(None) +): """ - List available AI providers and their status. + Get column statistics for a table. - Returns the availability and priority of each AI provider. - Lower priority numbers indicate higher preference. + Returns pre-computed statistics including null_count, distinct_count, + min, max, mean, median, stddev, and sample values. **Example:** ```bash - curl "http://127.0.0.1:8000/config/providers" - ``` - """ - try: - from app.services.ai.ai_provider import list_ai_providers - - providers = list_ai_providers() - return [ - ProviderStatusResponse( - name=p.name, - available=p.available, - priority=p.priority, - error=p.error, - ) - for p in providers - ] - - except Exception as e: - logger.error(f"Error listing providers: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -@router.get("/config/cached", tags=["Config Generation"]) -async def list_cached_configs(): - """ - List all cached generated configurations. - - **Example:** - ```bash - curl "http://127.0.0.1:8000/config/cached" + curl -H "Authorization: $KB_TOKEN" \ + "http://127.0.0.1:8000/object/local/76990_7_2/tables/Genes/stats" ``` """ try: - from app.services.data.fingerprint import DatabaseFingerprint - - fp = DatabaseFingerprint() - cached = fp.list_cached() + token = get_auth_token(authorization) + cache_dir = get_cache_dir() - return { - "configs": cached, - "total": len(cached), - } + # Parse db_name + if db_name.startswith("local/"): + berdl_table_id = db_name.replace("local/", "") + db_path = download_pangenome_db( + berdl_table_id=berdl_table_id, + auth_token=token, + cache_dir=cache_dir, + kb_env=kb_env + ) + elif db_name.startswith("handle/"): + handle_ref = db_name.replace("handle/", "") + client = KBaseClient(token, kb_env, cache_dir) + safe_handle = handle_ref.replace(":", "_").replace("/", "_") + db_dir = cache_dir / "handles" + db_dir.mkdir(parents=True, exist_ok=True) + db_path = db_dir / f"{safe_handle}.db" + + if not db_path.exists(): + temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") + try: + client.download_blob_file(handle_ref, temp_path) + temp_path.rename(db_path) + except Exception: + temp_path.unlink(missing_ok=True) + raise + else: + # Try as berdl_table_id directly + db_path = download_pangenome_db( + berdl_table_id=db_name, + auth_token=token, + cache_dir=cache_dir, + kb_env=kb_env + ) - except Exception as e: - logger.error(f"Error listing cached configs: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -@router.delete("/config/cached/{fingerprint}", tags=["Config Generation"]) -async def delete_cached_config(fingerprint: str): - """ - Delete a specific cached configuration. - - Use this to invalidate a cached config and force regeneration on next request. - - **Example:** - ```bash - curl -X DELETE "http://127.0.0.1:8000/config/cached/76990_7_2_abc123" - ``` - """ - try: - from app.services.data.fingerprint import DatabaseFingerprint + if not validate_table_exists(db_path, table_name): + available = list_tables(db_path) + raise HTTPException(404, f"Table '{table_name}' not found. Available: {available}") - fp = DatabaseFingerprint() - deleted = fp.clear_cache(fingerprint) + stats_service = get_statistics_service() + stats = stats_service.get_table_statistics(db_path, table_name) - if deleted > 0: - return {"status": "success", "message": f"Deleted config: {fingerprint}"} - else: - raise HTTPException(status_code=404, detail=f"Config not found: {fingerprint}") + return stats except HTTPException: raise except Exception as e: - logger.error(f"Error deleting config: {e}") + logger.error(f"Error getting statistics: {e}") raise HTTPException(status_code=500, detail=str(e)) -@router.post( - "/object/{ws_ref:path}/config/generate", - response_model=ConfigGenerationResponse, - tags=["Config Generation"] -) -async def generate_config_for_object( - ws_ref: str, - force_regenerate: bool = Query(False, description="Skip cache and regenerate"), +@router.post("/api/aggregate/{db_name}/tables/{table_name}", response_model=TableDataQueryResponse, tags=["Object Access"]) +async def execute_aggregation( + db_name: str, + table_name: str, + request: AggregationQueryRequest, kb_env: str = Query("appdev"), authorization: str | None = Header(None) ): """ - Generate a DataTables_Viewer configuration for a KBase object. - - This is the object-based alternative to the handle-based config generation. - It downloads the SQLite database from the workspace object and generates - an AI-powered viewer configuration. - - **Flow:** - 1. Download SQLite from workspace object (uses existing cache) - 2. Compute database fingerprint - 3. Check config cache → return if exists (unless force_regenerate) - 4. Analyze schema and sample values - 5. Apply rule-based + Argo AI type inference - 6. Validate and cache generated config - 7. Return + Execute aggregation query with GROUP BY. **Example:** ```bash - curl -X POST -H "Authorization: $KB_TOKEN" \\ - "http://127.0.0.1:8000/object/76990/7/2/config/generate" + curl -X POST -H "Authorization: $KB_TOKEN" -H "Content-Type: application/json" \ + -d '{ + "group_by": ["category"], + "aggregations": [ + {"column": "value", "function": "sum", "alias": "total"} + ], + "filters": [{"column": "value", "operator": "gt", "value": 100}] + }' \ + "http://127.0.0.1:8000/api/aggregate/local/76990_7_2/tables/Data" ``` """ try: token = get_auth_token(authorization) cache_dir = get_cache_dir() - berdl_table_id = ws_ref - - # Download database - db_path = download_pangenome_db( - berdl_table_id=berdl_table_id, - auth_token=token, - cache_dir=cache_dir, - kb_env=kb_env - ) - - # Get object type for config metadata - try: - object_type = get_object_type(berdl_table_id, token, kb_env) - except Exception: - object_type = None - - # Generate config - from app.services.config.config_generator import ConfigGenerator - - generator = ConfigGenerator() - - # Build schema info for response - schema = {} - table_schemas = {} - total_rows = 0 - try: - table_names = list_tables(db_path) - for tbl in table_names: - cols = get_table_columns(db_path, tbl) - schema[tbl] = {c: "TEXT" for c in cols} - total_rows += get_table_row_count(db_path, tbl) or 0 - except Exception as e: - logger.warning(f"Error building schema: {e}") - - # Check if config already exists in DataTables Viewer - from app.services.config_registry import get_config_registry - from app.services.viewer_client import get_viewer_client - - registry = get_config_registry() - viewer = get_viewer_client() - - # Check registry first - if not force_regenerate and registry.has_config(object_type): - # Verify with viewer - if viewer.check_config_exists(object_type): - logger.info(f"Config already exists for {object_type}, skipping generation") - return ConfigGenerationResponse( - status="exists", - fingerprint="", - config_url="", - config={}, - tables_analyzed=0, - columns_inferred=0, - ai_provider_used=None, - generation_time_ms=0, - cache_hit=True, - ) - else: - # Registry says it exists but viewer doesn't - update registry - registry.mark_no_config(object_type) - # Generate config with AI - try: - result = generator.generate( - db_path=db_path, - handle_ref=berdl_table_id, - force_regenerate=True, # Always generate fresh for new configs - ai_preference="argo", + # Parse db_name + if db_name.startswith("local/"): + berdl_table_id = db_name.replace("local/", "") + db_path = download_pangenome_db( + berdl_table_id=berdl_table_id, + auth_token=token, + cache_dir=cache_dir, + kb_env=kb_env ) + elif db_name.startswith("handle/"): + handle_ref = db_name.replace("handle/", "") + client = KBaseClient(token, kb_env, cache_dir) + safe_handle = handle_ref.replace(":", "_").replace("/", "_") + db_dir = cache_dir / "handles" + db_dir.mkdir(parents=True, exist_ok=True) + db_path = db_dir / f"{safe_handle}.db" - # Add object type to config - if object_type and "objectType" not in result.config: - result.config["objectType"] = object_type - - # Send config to DataTables Viewer - try: - viewer.send_config( - object_type=object_type, - source_ref=berdl_table_id, - config=result.config - ) - # Mark as having config - registry.mark_has_config(object_type) - status = "generated_and_sent" - except Exception as e: - logger.error(f"Failed to send config to viewer: {e}") - # Still return the config even if viewer send failed - status = "generated_but_send_failed" - - return ConfigGenerationResponse( - status=status, - fingerprint=result.fingerprint, - config_url="", - config=result.config, - tables_analyzed=result.tables_analyzed, - columns_inferred=result.columns_inferred, - ai_provider_used=result.ai_provider_used, - generation_time_ms=result.generation_time_ms, - cache_hit=False, + if not db_path.exists(): + temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") + try: + client.download_blob_file(handle_ref, temp_path) + temp_path.rename(db_path) + except Exception: + temp_path.unlink(missing_ok=True) + raise + else: + # Try as berdl_table_id directly + db_path = download_pangenome_db( + berdl_table_id=db_name, + auth_token=token, + cache_dir=cache_dir, + kb_env=kb_env ) - - except Exception as gen_error: - logger.error(f"Config generation failed: {gen_error}") - raise HTTPException( - status_code=500, - detail=f"Config generation failed: {gen_error}" + + if not validate_table_exists(db_path, table_name): + available = list_tables(db_path) + raise HTTPException(404, f"Table '{table_name}' not found. Available: {available}") + + # Convert request to query service format + query_service = get_query_service() + + filters = None + if request.filters: + filters = [ + FilterSpec( + column=f.column, + operator=f.operator, + value=f.value, + value2=f.value2 + ) + for f in request.filters + ] + + aggregations = [ + AggregationSpec( + column=a.column, + function=a.function, + alias=a.alias ) + for a in request.aggregations + ] + + result = query_service.execute_query( + db_path=db_path, + table_name=table_name, + limit=request.limit, + offset=request.offset, + filters=filters, + aggregations=aggregations, + group_by=request.group_by + ) + + return TableDataQueryResponse(**result) except HTTPException: raise except Exception as e: - logger.error(f"Error generating config for object: {e}") + logger.error(f"Error executing aggregation: {e}") raise HTTPException(status_code=500, detail=str(e)) -@router.get("/object/{ws_ref:path}/config", tags=["Config Generation"]) -async def get_config_for_object( - ws_ref: str, - kb_env: str = Query("appdev"), +@router.post("/table-data", response_model=TableDataQueryResponse, tags=["Legacy"]) +async def query_table_data_enhanced( + request: TableDataQueryRequest, authorization: str | None = Header(None) ): """ - Get cached viewer config for a KBase object. + Enhanced table data query endpoint with full DataTables Viewer API support. - Returns 404 if no config has been generated yet. Use the - POST /object/{ws_ref}/config/generate endpoint to create one. + Supports type-aware filtering, aggregations, and comprehensive metadata. **Example:** ```bash - curl -H "Authorization: $KB_TOKEN" \\ - "http://127.0.0.1:8000/object/76990/7/2/config" + curl -X POST -H "Authorization: $KB_TOKEN" -H "Content-Type: application/json" \ + -d '{ + "berdl_table_id": "local/76990_7_2", + "table_name": "Genes", + "limit": 100, + "offset": 0, + "filters": [ + {"column": "contigs", "operator": "gt", "value": "50"} + ] + }' \ + "http://127.0.0.1:8000/table-data" ``` """ + start_time = time.time() + try: token = get_auth_token(authorization) cache_dir = get_cache_dir() - berdl_table_id = ws_ref + kb_env = "appdev" # Default, could be from request - # Download database (needed for fingerprint computation) - db_path = download_pangenome_db( - berdl_table_id=berdl_table_id, - auth_token=token, - cache_dir=cache_dir, - kb_env=kb_env - ) + # Parse berdl_table_id + if request.berdl_table_id.startswith("local/"): + berdl_table_id = request.berdl_table_id.replace("local/", "") + else: + berdl_table_id = request.berdl_table_id - # Configs are now stored in DataTables Viewer - # This endpoint is deprecated - configs should be retrieved from viewer - raise HTTPException( - status_code=404, - detail=f"Configs are now stored in DataTables Viewer. Use POST /object/{ws_ref}/config/generate to create one, then retrieve from viewer." + # Download (or get cached) DB + try: + db_path = download_pangenome_db( + berdl_table_id, token, cache_dir, kb_env + ) + except ValueError as e: + raise HTTPException(status_code=404, detail=str(e)) + + if not validate_table_exists(db_path, request.table_name): + available = list_tables(db_path) + raise HTTPException(404, f"Table '{request.table_name}' not found. Available: {available}") + + # Convert request to query service format + query_service = get_query_service() + + # Convert filters + filters = None + if request.filters: + filters = [ + FilterSpec( + column=f.column, + operator=f.operator, + value=f.value, + value2=f.value2 + ) + for f in request.filters + ] + elif request.col_filter: + # Legacy col_filter format + filters = [ + FilterSpec( + column=col, + operator="like", + value=val + ) + for col, val in request.col_filter.items() + ] + + # Convert aggregations + aggregations = None + if request.aggregations: + aggregations = [ + AggregationSpec( + column=a.column, + function=a.function, + alias=a.alias + ) + for a in request.aggregations + ] + + # Execute query + result = query_service.execute_query( + db_path=db_path, + table_name=request.table_name, + limit=request.limit, + offset=request.offset, + columns=request.columns, + sort_column=request.sort_column, + sort_order=request.sort_order, + search_value=request.search_value, + filters=filters, + aggregations=aggregations, + group_by=request.group_by ) + return TableDataQueryResponse(**result) + except HTTPException: raise except Exception as e: - logger.error(f"Error getting config for object: {e}") + logger.error(f"Error querying table data: {e}") raise HTTPException(status_code=500, detail=str(e)) diff --git a/app/services/__init__.py b/app/services/__init__.py index f2394fc..c05a668 100644 --- a/app/services/__init__.py +++ b/app/services/__init__.py @@ -1,59 +1,25 @@ """ TableScanner Services Package. -This package contains the AI-powered schema inference and config generation services. +This package contains data query and schema analysis services. Modules: - - type_inference: Rule-based pattern detection for column types + - connection_pool: Database connection pooling and management + - query_service: Enhanced query execution with type-aware filtering + - schema_service: Schema information retrieval + - statistics_service: Column statistics computation - schema_analyzer: Database schema introspection and profiling - - ai_provider: Scalable AI backend abstraction layer - - config_generator: DataTables_Viewer config JSON generation - fingerprint: Database fingerprinting for caching """ -from .data.type_inference import TypeInferenceEngine, InferredType, DataType from .data.schema_analyzer import SchemaAnalyzer, ColumnProfile, TableProfile -from .ai.ai_provider import ( - AIProvider, - AIProviderFactory, - get_ai_provider, - list_ai_providers, - ColumnInference, - ProviderStatus, -) from .data.fingerprint import DatabaseFingerprint -from .config.config_generator import ConfigGenerator, GenerationResult -from .ai.prompts import build_table_config_prompt, detect_value_patterns, compute_numeric_stats -from .data.validation import validate_config, validate_table_config, validate_ai_response, sanitize_config __all__ = [ - # Type inference - "TypeInferenceEngine", - "InferredType", - "DataType", # Schema analysis "SchemaAnalyzer", "ColumnProfile", "TableProfile", - # AI providers - "AIProvider", - "AIProviderFactory", - "get_ai_provider", - "list_ai_providers", - "ColumnInference", - "ProviderStatus", # Fingerprinting "DatabaseFingerprint", - # Config generation - "ConfigGenerator", - "GenerationResult", - # Prompts - "build_table_config_prompt", - "detect_value_patterns", - "compute_numeric_stats", - # Validation - "validate_config", - "validate_table_config", - "validate_ai_response", - "sanitize_config", ] diff --git a/app/services/ai/__init__.py b/app/services/ai/__init__.py deleted file mode 100644 index 16e79bc..0000000 --- a/app/services/ai/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -""" -AI Services. - -AI-powered config generation and inference. -""" - -from .ai_provider import AIProvider, list_ai_providers -from ..config.config_generator import ConfigGenerator - -__all__ = [ - "AIProvider", - "list_ai_providers", - "ConfigGenerator", -] diff --git a/app/services/ai/ai_provider.py b/app/services/ai/ai_provider.py deleted file mode 100644 index b7cc7e5..0000000 --- a/app/services/ai/ai_provider.py +++ /dev/null @@ -1,625 +0,0 @@ -""" -AI Provider Layer. - -Scalable abstraction for AI-powered schema inference with multiple backend -support and automatic fallback. Supports: -- OpenAI API (GPT-4o-mini, GPT-4, etc.) -- Argo Gateway (ANL internal) -- Ollama (local LLMs) -- Claude Code CLI -- Rule-based fallback (no AI) -""" - -from __future__ import annotations - -import json -import logging -import os -import subprocess -import tempfile -from abc import ABC, abstractmethod -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Literal - -from ..data.schema_analyzer import ColumnProfile, TableProfile -from ..data.type_inference import DataType, InferredType, TransformConfig, TypeInferenceEngine - -logger = logging.getLogger(__name__) - - -# ============================================================================= -# DATA STRUCTURES -# ============================================================================= - -@dataclass -class ColumnInference: - """AI-enhanced column inference result.""" - column: str - data_type: str - display_name: str - categories: list[str] - transform: dict | None = None - width: str = "auto" - pin: Literal["left", "right"] | None = None - sortable: bool = True - filterable: bool = True - copyable: bool = False - confidence: float = 1.0 - source: Literal["rules", "ai", "hybrid"] = "rules" - reasoning: str = "" - - -@dataclass -class ProviderStatus: - """Status of an AI provider.""" - name: str - available: bool - priority: int - error: str | None = None - - -# ============================================================================= -# ABSTRACT BASE -# ============================================================================= - -class AIProvider(ABC): - """Abstract base class for AI providers.""" - - @property - @abstractmethod - def name(self) -> str: - """Provider name.""" - ... - - @property - @abstractmethod - def priority(self) -> int: - """Provider priority (lower = higher priority).""" - ... - - @abstractmethod - def is_available(self) -> bool: - """Check if provider is configured and responding.""" - ... - - @abstractmethod - def analyze_columns( - self, - table: TableProfile, - columns: list[ColumnProfile] - ) -> list[ColumnInference]: - """ - Analyze columns using AI. - - Args: - table: Table profile with metadata - columns: List of column profiles to analyze - - Returns: - List of AI-enhanced column inferences - """ - ... - - def get_status(self) -> ProviderStatus: - """Get provider status.""" - try: - available = self.is_available() - return ProviderStatus( - name=self.name, - available=available, - priority=self.priority, - ) - except Exception as e: - return ProviderStatus( - name=self.name, - available=False, - priority=self.priority, - error=str(e), - ) - - -# ============================================================================= -# RULE-BASED PROVIDER (Fallback) -# ============================================================================= - -class RuleBasedProvider(AIProvider): - """ - Rule-based inference without AI. - - Uses the TypeInferenceEngine for pattern-based type detection. - Always available as a fallback. - """ - - def __init__(self) -> None: - self._engine = TypeInferenceEngine() - - @property - def name(self) -> str: - return "rules-only" - - @property - def priority(self) -> int: - return 100 # Lowest priority (fallback) - - def is_available(self) -> bool: - return True # Always available - - def analyze_columns( - self, - table: TableProfile, - columns: list[ColumnProfile] - ) -> list[ColumnInference]: - """Analyze columns using rule-based inference.""" - results: list[ColumnInference] = [] - - for col in columns: - inference = self._engine.infer( - column_name=col.name, - sample_values=col.sample_values, - sqlite_type=col.sqlite_type, - ) - - results.append(ColumnInference( - column=col.name, - data_type=inference.data_type.value, - display_name=inference.display_name, - categories=inference.categories, - transform=self._transform_to_dict(inference.transform), - width=inference.width, - pin=inference.pin, - sortable=inference.sortable, - filterable=inference.filterable, - copyable=inference.copyable, - confidence=inference.confidence, - source="rules", - reasoning="Pattern-based inference from column name and sample values", - )) - - return results - - def _transform_to_dict(self, transform: TransformConfig | None) -> dict | None: - """Convert TransformConfig to dict for JSON serialization.""" - if transform is None: - return None - return { - "type": transform.type, - "options": transform.options, - } - - -# ============================================================================= -# OPENAI PROVIDER -# ============================================================================= - -class OpenAIProvider(AIProvider): - """ - OpenAI API provider. - - Uses GPT-4o-mini or other OpenAI models for intelligent schema inference. - """ - - SYSTEM_PROMPT = """You are an expert database schema analyst for a scientific data visualization system. -Your task is to analyze column metadata and sample values to determine optimal rendering configurations. - -For each column, determine: -1. dataType: One of: string, number, integer, float, boolean, date, datetime, sequence, id, url, email, ontology, percentage -2. displayName: Human-readable name (Title Case) -3. categories: Category groupings like "core", "metadata", "external", "functional", "sequence", "statistics" -4. transform: Rendering transformation if applicable (links, badges, formatting) -5. confidence: 0.0-1.0 confidence score -6. reasoning: Brief explanation - -Respond in valid JSON only. No additional text.""" - - USER_PROMPT_TEMPLATE = """Analyze this table schema: - -TABLE: {table_name} -ROW COUNT: {row_count} - -COLUMNS: -{columns_json} - -Return a JSON array of column configurations matching this schema: -[ - {{ - "column": "ColumnName", - "dataType": "string", - "displayName": "Column Name", - "categories": ["core"], - "transform": {{"type": "link", "options": {{"urlTemplate": "https://..."}}}}, - "width": "120px", - "sortable": true, - "filterable": true, - "copyable": false, - "confidence": 0.9, - "reasoning": "Description of column appears to contain..." - }} -]""" - - def __init__( - self, - api_key: str | None = None, - model: str = "gpt-4o-mini", - temperature: float = 0.1, - ) -> None: - self.api_key = api_key or os.getenv("OPENAI_API_KEY", "") - self.model = model - self.temperature = temperature - self._client = None - self._rule_engine = TypeInferenceEngine() - - @property - def name(self) -> str: - return "openai" - - @property - def priority(self) -> int: - return 10 - - def is_available(self) -> bool: - if not self.api_key: - return False - try: - # Try to import openai and create client - import openai - self._client = openai.OpenAI(api_key=self.api_key) - # Quick test with a minimal request - return True - except ImportError: - logger.warning("OpenAI package not installed") - return False - except Exception as e: - logger.warning(f"OpenAI not available: {e}") - return False - - def analyze_columns( - self, - table: TableProfile, - columns: list[ColumnProfile] - ) -> list[ColumnInference]: - """Analyze columns using OpenAI.""" - if not self._client: - if not self.is_available(): - raise RuntimeError("OpenAI provider not available") - - # Prepare column data for prompt - columns_data = [] - for col in columns: - columns_data.append({ - "name": col.name, - "type": col.sqlite_type, - "samples": col.sample_values[:5], - "null_ratio": round(col.null_ratio, 2), - "unique_ratio": round(col.unique_ratio, 2), - "patterns": col.detected_patterns, - }) - - prompt = self.USER_PROMPT_TEMPLATE.format( - table_name=table.name, - row_count=table.row_count, - columns_json=json.dumps(columns_data, indent=2), - ) - - try: - import openai - response = self._client.chat.completions.create( - model=self.model, - messages=[ - {"role": "system", "content": self.SYSTEM_PROMPT}, - {"role": "user", "content": prompt}, - ], - temperature=self.temperature, - response_format={"type": "json_object"}, - ) - - content = response.choices[0].message.content - result = json.loads(content) - - # Handle both array and object with "columns" key - if isinstance(result, dict) and "columns" in result: - ai_columns = result["columns"] - elif isinstance(result, list): - ai_columns = result - else: - logger.warning(f"Unexpected AI response format: {type(result)}") - return self._fallback_inference(columns) - - return self._parse_ai_response(ai_columns, columns) - - except Exception as e: - logger.error(f"OpenAI analysis failed: {e}") - return self._fallback_inference(columns) - - def _parse_ai_response( - self, - ai_columns: list[dict], - original_columns: list[ColumnProfile] - ) -> list[ColumnInference]: - """Parse AI response into ColumnInference objects.""" - results: list[ColumnInference] = [] - - # Create lookup for original columns - col_map = {col.name: col for col in original_columns} - - for ai_col in ai_columns: - col_name = ai_col.get("column", "") - if col_name not in col_map: - continue - - results.append(ColumnInference( - column=col_name, - data_type=ai_col.get("dataType", "string"), - display_name=ai_col.get("displayName", col_name), - categories=ai_col.get("categories", ["data"]), - transform=ai_col.get("transform"), - width=ai_col.get("width", "auto"), - pin=ai_col.get("pin"), - sortable=ai_col.get("sortable", True), - filterable=ai_col.get("filterable", True), - copyable=ai_col.get("copyable", False), - confidence=ai_col.get("confidence", 0.8), - source="ai", - reasoning=ai_col.get("reasoning", ""), - )) - - # Fill in any missing columns with rule-based inference - covered_cols = {r.column for r in results} - for col in original_columns: - if col.name not in covered_cols: - rule_result = RuleBasedProvider().analyze_columns( - TableProfile(name=""), [col] - ) - if rule_result: - results.append(rule_result[0]) - - return results - - def _fallback_inference(self, columns: list[ColumnProfile]) -> list[ColumnInference]: - """Fall back to rule-based inference.""" - return RuleBasedProvider().analyze_columns( - TableProfile(name=""), columns - ) - - -# ============================================================================= -# OLLAMA PROVIDER (Local LLM) -# ============================================================================= - -class OllamaProvider(AIProvider): - """ - Ollama provider for local LLM inference. - - Uses locally running Ollama with models like llama3, codellama, etc. - """ - - def __init__( - self, - host: str | None = None, - model: str = "llama3", - ) -> None: - self.host = host or os.getenv("OLLAMA_HOST", "http://localhost:11434") - self.model = model - self._rule_engine = TypeInferenceEngine() - - @property - def name(self) -> str: - return "ollama" - - @property - def priority(self) -> int: - return 30 - - def is_available(self) -> bool: - try: - import httpx - response = httpx.get(f"{self.host}/api/tags", timeout=5) - return response.status_code == 200 - except Exception: - return False - - def analyze_columns( - self, - table: TableProfile, - columns: list[ColumnProfile] - ) -> list[ColumnInference]: - """Analyze columns using Ollama.""" - # Ollama analysis similar to OpenAI but with local API - # For now, fall back to rule-based to keep implementation focused - return RuleBasedProvider().analyze_columns(table, columns) - - -# ============================================================================= -# ARGO PROVIDER (ANL Internal) -# ============================================================================= - -class ArgoProvider(AIProvider): - """ - ANL Argo Gateway provider. - - Wraps the existing ArgoUtils from KBUtilLib. - """ - - def __init__( - self, - user: str | None = None, - model: str = "gpt4o", - proxy_port: int = 1080, - ) -> None: - self.user = user or os.getenv("ARGO_USER", "") - self.model = model - self.proxy_port = proxy_port - self._argo_client = None - - @property - def name(self) -> str: - return "argo" - - @property - def priority(self) -> int: - return 20 - - def is_available(self) -> bool: - if not self.user: - return False - try: - # Try to import and initialize ArgoUtils - from lib.KBUtilLib.src.kbutillib.argo_utils import ArgoUtils - self._argo_client = ArgoUtils( - model=self.model, - user=self.user, - proxy_port=self.proxy_port, - ) - return self._argo_client.ping() - except ImportError: - logger.warning("ArgoUtils not available") - return False - except Exception as e: - logger.warning(f"Argo not available: {e}") - return False - - def analyze_columns( - self, - table: TableProfile, - columns: list[ColumnProfile] - ) -> list[ColumnInference]: - """Analyze columns using Argo.""" - # Fall back to rule-based for now - return RuleBasedProvider().analyze_columns(table, columns) - - -# ============================================================================= -# CLAUDE CODE PROVIDER -# ============================================================================= - -class ClaudeCodeProvider(AIProvider): - """ - Claude Code CLI provider. - - Uses Claude Code executable for local inference. - """ - - def __init__(self, executable: str | None = None) -> None: - self.executable = executable or os.getenv("CLAUDE_CODE_EXECUTABLE", "claude") - - @property - def name(self) -> str: - return "claude-code" - - @property - def priority(self) -> int: - return 25 - - def is_available(self) -> bool: - try: - result = subprocess.run( - [self.executable, "--version"], - capture_output=True, - text=True, - timeout=5, - ) - return result.returncode == 0 - except Exception: - return False - - def analyze_columns( - self, - table: TableProfile, - columns: list[ColumnProfile] - ) -> list[ColumnInference]: - """Analyze columns using Claude Code.""" - # Fall back to rule-based for now - return RuleBasedProvider().analyze_columns(table, columns) - - -# ============================================================================= -# PROVIDER FACTORY -# ============================================================================= - -class AIProviderFactory: - """ - Factory for creating AI providers with automatic fallback. - - Supports configuration via environment variables: - - AI_PROVIDER: Preferred provider (auto, openai, argo, ollama, claude-code, rules-only) - - AI_FALLBACK_CHAIN: Comma-separated fallback chain - """ - - DEFAULT_CHAIN = "openai,argo,ollama,rules-only" - - PROVIDERS = { - "openai": OpenAIProvider, - "argo": ArgoProvider, - "ollama": OllamaProvider, - "claude-code": ClaudeCodeProvider, - "rules-only": RuleBasedProvider, - } - - def __init__(self) -> None: - self._instances: dict[str, AIProvider] = {} - - def get_provider(self, preference: str = "auto") -> AIProvider: - """ - Get an available AI provider. - - Args: - preference: Preferred provider or "auto" for automatic selection - - Returns: - An available AIProvider instance - - Raises: - RuntimeError: If no providers are available - """ - if preference == "auto": - preference = os.getenv("AI_PROVIDER", "auto") - - # If specific provider requested - if preference != "auto" and preference in self.PROVIDERS: - provider = self._get_or_create(preference) - if provider.is_available(): - return provider - logger.warning(f"Preferred provider '{preference}' not available, trying fallback chain") - - # Try fallback chain - chain = os.getenv("AI_FALLBACK_CHAIN", self.DEFAULT_CHAIN) - for provider_name in chain.split(","): - provider_name = provider_name.strip() - if provider_name in self.PROVIDERS: - provider = self._get_or_create(provider_name) - if provider.is_available(): - logger.info(f"Using AI provider: {provider_name}") - return provider - - # Last resort: rule-based (always available) - return self._get_or_create("rules-only") - - def list_providers(self) -> list[ProviderStatus]: - """Get status of all providers.""" - statuses: list[ProviderStatus] = [] - for name in self.PROVIDERS: - provider = self._get_or_create(name) - statuses.append(provider.get_status()) - return sorted(statuses, key=lambda s: s.priority) - - def _get_or_create(self, name: str) -> AIProvider: - """Get cached or create new provider instance.""" - if name not in self._instances: - provider_class = self.PROVIDERS.get(name) - if provider_class: - self._instances[name] = provider_class() - return self._instances[name] - - -# Module-level factory instance -_factory = AIProviderFactory() - - -def get_ai_provider(preference: str = "auto") -> AIProvider: - """Get an available AI provider.""" - return _factory.get_provider(preference) - - -def list_ai_providers() -> list[ProviderStatus]: - """List all AI providers and their status.""" - return _factory.list_providers() diff --git a/app/services/ai/prompts.py b/app/services/ai/prompts.py deleted file mode 100644 index 607d78c..0000000 --- a/app/services/ai/prompts.py +++ /dev/null @@ -1,340 +0,0 @@ -""" -Prompt Engineering Module for AI-Powered Config Generation. - -Provides structured prompts for Argo AI to analyze pre-computed schema data -and generate DataTables Viewer configurations. Argo cannot execute SQL -commands, so all analysis must be pre-computed before prompt generation. -""" - -from __future__ import annotations - -import json -from typing import Any - - -# ============================================================================= -# SYSTEM PROMPT - Argo-Optimized -# ============================================================================= - -SYSTEM_PROMPT = """You are a Senior Bioinformatics Data Engineer creating DataTables Viewer configurations. - -## Your Role -Analyze the PRE-COMPUTED schema analysis and sample values provided below to generate -optimal column configurations for scientific data visualization. You CANNOT run SQL -commands - all data analysis has been pre-computed and provided to you. - -## Column Configuration Rules - -### Data Type Detection (analyze provided samples) -| Pattern | data_type | Transform | -|---------|-----------|-----------| -| UniRef IDs with prefix (e.g., "UniRef:UniRef90_...") | id | chain: replace prefix → link | -| GO terms (GO:0008150) | ontology | ontology with AmiGO URL | -| KEGG IDs (K00001, ko:K00001) | id | link to KEGG | -| Pfam IDs (PF00001, pfam:PF00001) | id | link to InterPro | -| NCBI IDs (numeric or NP_/WP_) | id | link to NCBI | -| DNA sequences (20+ chars of ATCG) | sequence | sequence transformer | -| Protein sequences (20+ amino acids) | sequence | sequence transformer | -| URLs (http://...) | url | link transformer | -| Email addresses | email | null | -| Numeric with high precision | float | number with decimals | -| Integer values | integer | null or number | -| +/- or strand indicators | string | badge with color mapping | -| Boolean (true/false, yes/no, 1/0) | boolean | boolean transformer | -| P-values, FDR (scientific notation) | float | number with scientific notation | -| Log2 fold change | float | heatmap (diverging, min:-4 max:4) | - -### Category Assignment Rules -| Column Pattern | Category | -|----------------|----------| -| Primary ID column (usually first) | core | -| Names (gene_name, organism, etc.) | core | -| Products, functions, descriptions | functional | -| UniRef, UniProt, NCBI, KEGG refs | external | -| GO, Pfam, COG annotations | ontology | -| DNA/AA sequence columns | sequence | -| Scores, p-values, fold changes | statistics | -| Coordinates (start, end, strand) | core | -| System columns, timestamps | metadata | - -### Width Guidelines -| Type | Width | -|------|-------| -| ID columns | 100-140px | -| Short strings | 120-180px | -| Long text (descriptions) | 250-400px | -| Numbers | 80-120px | -| Sequences | 150px | -| Boolean | 80px | - -### Essential Transform Examples - -**UniRef with prefix stripping:** -```json -{"type": "chain", "options": {"transforms": [ - {"type": "replace", "options": {"find": "UniRef:", "replace": ""}}, - {"type": "link", "options": {"urlTemplate": "https://www.uniprot.org/uniref/{value}", "icon": "bi-link-45deg"}} -]}} -``` - -**GO term ontology:** -```json -{"type": "ontology", "options": {"prefix": "GO", "urlTemplate": "https://amigo.geneontology.org/amigo/term/{value}", "style": "badge"}} -``` - -**KEGG ID link:** -```json -{"type": "link", "options": {"urlTemplate": "https://www.genome.jp/entry/{value}", "target": "_blank"}} -``` - -**Strand badge:** -```json -{"type": "badge", "options": {"colorMap": {"+": {"color": "#22c55e", "bgColor": "#dcfce7"}, "-": {"color": "#ef4444", "bgColor": "#fee2e2"}}}} -``` - -**Heatmap for fold change:** -```json -{"type": "heatmap", "options": {"min": -4, "max": 4, "colorScale": "diverging", "showValue": true, "decimals": 2}} -``` - -**Scientific notation for p-values:** -```json -{"type": "number", "options": {"notation": "scientific", "decimals": 2}} -``` - -## Output Format -Return ONLY valid JSON with this exact structure. No markdown, no explanation. - -```json -{ - "columns": [ - { - "column": "exact_sql_column_name", - "displayName": "Human Readable Name", - "dataType": "string|number|integer|float|boolean|date|id|sequence|ontology|url", - "categories": ["core|functional|external|ontology|sequence|statistics|metadata"], - "sortable": true, - "filterable": true, - "copyable": false, - "width": "auto", - "pin": null, - "transform": null - } - ] -} -``` - -## Critical Rules -1. Column names MUST exactly match the SQL schema - case sensitive -2. Pin the primary identifier column to "left" -3. Set copyable: true for IDs and sequences -4. Right-align numeric columns (handled by viewer based on dataType) -5. Detect prefixes in sample values that need stripping (UniRef:, GO:, ko:, etc.) -6. If samples show patterns like "UniRef:UniRef90_..." always use chain transform -7. For columns with many nulls, still provide full config based on non-null samples""" - - -# ============================================================================= -# PROMPT BUILDERS -# ============================================================================= - -def build_table_config_prompt( - table_name: str, - schema_info: list[dict[str, Any]], - sample_values: dict[str, list[Any]], - detected_patterns: dict[str, list[str]], - statistics: dict[str, dict[str, Any]], - row_count: int = 0, -) -> str: - """ - Build a complete prompt for Argo to generate table configuration. - - All data must be pre-computed before calling this function since - Argo cannot execute SQL commands. - - Args: - table_name: Name of the table being configured - schema_info: Pre-computed from PRAGMA table_info (list of column defs) - sample_values: Pre-computed samples per column (10-20 non-null values each) - detected_patterns: Pre-detected patterns like prefixes, URLs, sequences - statistics: Pre-computed min/max/avg for numeric columns - row_count: Total rows in table for context - - Returns: - Complete prompt string for Argo - """ - # Format schema as readable list - schema_summary = [] - for col in schema_info: - col_info = f"- {col['name']} ({col.get('type', 'TEXT')})" - if col.get('pk'): - col_info += " [PRIMARY KEY]" - if col.get('notnull'): - col_info += " [NOT NULL]" - schema_summary.append(col_info) - - prompt = f"""{SYSTEM_PROMPT} - ---- -## Analysis Data for Table: `{table_name}` -Row Count: {row_count:,} - -### Schema Definition -{chr(10).join(schema_summary)} - -### Sample Values (10 non-null per column) -{json.dumps(sample_values, indent=2, default=str)} - -### Detected Patterns -{json.dumps(detected_patterns, indent=2)} - -### Numeric Statistics (min/max/avg) -{json.dumps(statistics, indent=2)} - ---- -## Task -Generate complete column configurations for table `{table_name}`. -Return ONLY the JSON object with "columns" array. No markdown code fences.""" - - return prompt - - -def build_multi_table_prompt( - tables: dict[str, dict[str, Any]], - database_name: str = "database", -) -> str: - """ - Build prompt for configuring multiple tables at once. - - Args: - tables: Dict mapping table names to their analysis data - database_name: Name for the overall config - - Returns: - Complete prompt for multi-table config generation - """ - tables_section = [] - - for table_name, data in tables.items(): - table_block = f""" -### Table: `{table_name}` ({data.get('row_count', 0):,} rows) - -**Schema:** -{json.dumps(data.get('schema', []), indent=2)} - -**Sample Values:** -{json.dumps(data.get('samples', {}), indent=2)} - -**Detected Patterns:** -{json.dumps(data.get('patterns', {}), indent=2)} -""" - tables_section.append(table_block) - - prompt = f"""{SYSTEM_PROMPT} - ---- -## Database: {database_name} -Tables: {', '.join(tables.keys())} - -{chr(10).join(tables_section)} - ---- -## Task -Generate configurations for ALL tables. Return JSON with this structure: -{{"tables": {{"table_name": {{"displayName": "...", "columns": [...]}}}}}} - -Return ONLY the JSON. No markdown.""" - - return prompt - - -# ============================================================================= -# PATTERN DETECTION HELPERS -# ============================================================================= - -def detect_value_patterns(values: list[Any]) -> list[str]: - """ - Detect patterns in sample values for prompt enhancement. - - Args: - values: List of sample values from a column - - Returns: - List of detected pattern descriptions - """ - import re - - patterns = [] - str_values = [str(v) for v in values if v is not None and str(v).strip()] - - if not str_values: - return ["all_null"] - - # Check for common prefixes - prefixes = { - "UniRef:": "UniRef prefix (needs stripping)", - "GO:": "GO term format", - "ko:": "KEGG orthology prefix", - "pfam:": "Pfam prefix", - "PF": "Pfam ID format", - "K0": "KEGG K number", - "http": "URL format", - "NP_": "NCBI RefSeq protein", - "WP_": "NCBI protein", - } - - for prefix, desc in prefixes.items(): - if any(v.startswith(prefix) for v in str_values[:5]): - patterns.append(desc) - - # Check for sequences - seq_pattern = re.compile(r'^[ATCGU]{20,}$', re.IGNORECASE) - protein_pattern = re.compile(r'^[ACDEFGHIKLMNPQRSTVWY]{15,}$', re.IGNORECASE) - - for v in str_values[:3]: - if seq_pattern.match(v): - patterns.append("DNA/RNA sequence") - break - if protein_pattern.match(v): - patterns.append("Protein sequence") - break - - # Check value characteristics - if all(v in ('+', '-', '.') for v in str_values): - patterns.append("Strand indicator (+/-)") - - if len(set(str_values)) <= 5 and len(str_values) > 3: - patterns.append(f"Categorical ({len(set(str_values))} unique values)") - - return patterns if patterns else ["no_special_pattern"] - - -def compute_numeric_stats(values: list[Any]) -> dict[str, Any] | None: - """ - Compute statistics for numeric columns. - - Args: - values: List of values from a column - - Returns: - Dict with min, max, avg, or None if not numeric - """ - numeric_values = [] - for v in values: - if v is None: - continue - try: - numeric_values.append(float(v)) - except (ValueError, TypeError): - return None - - if not numeric_values: - return None - - return { - "min": min(numeric_values), - "max": max(numeric_values), - "avg": sum(numeric_values) / len(numeric_values), - "count": len(numeric_values), - "has_decimals": any(v != int(v) for v in numeric_values if v == v), - } diff --git a/app/services/config/__init__.py b/app/services/config/__init__.py deleted file mode 100644 index 7190ea4..0000000 --- a/app/services/config/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -""" -Config Generation Service. - -AI-powered config generation for DataTables Viewer. -""" - -from .config_generator import ConfigGenerator, GenerationResult - -__all__ = [ - "ConfigGenerator", - "GenerationResult", -] diff --git a/app/services/config/config_generator.py b/app/services/config/config_generator.py deleted file mode 100644 index f16fa03..0000000 --- a/app/services/config/config_generator.py +++ /dev/null @@ -1,417 +0,0 @@ -""" -Config Generator. - -Generates DataTables_Viewer-compatible JSON configurations from -analyzed database schemas and AI-enhanced column inferences. - -Output matches the DataTypeConfig interface from the viewer's schema.ts. -""" - -from __future__ import annotations - -import logging -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any, Literal - -from ..ai.ai_provider import AIProvider, ColumnInference, get_ai_provider -from ..data.schema_analyzer import SchemaAnalyzer, TableProfile -from ..data.fingerprint import DatabaseFingerprint - -logger = logging.getLogger(__name__) - - -# ============================================================================= -# CATEGORY DEFINITIONS -# ============================================================================= - -@dataclass -class CategoryConfig: - """Category configuration matching viewer CategorySchema.""" - id: str - name: str - icon: str = "bi-folder" - color: str = "#6366f1" - description: str = "" - defaultVisible: bool = True - order: int = 1 - - def to_dict(self) -> dict: - return { - "id": self.id, - "name": self.name, - "icon": self.icon, - "color": self.color, - "description": self.description, - "defaultVisible": self.defaultVisible, - "order": self.order, - } - - -# Standard categories used across configs -STANDARD_CATEGORIES: dict[str, CategoryConfig] = { - "core": CategoryConfig( - id="core", - name="Core Info", - icon="bi-database", - color="#6366f1", - description="Essential identifiers and names", - order=1, - ), - "functional": CategoryConfig( - id="functional", - name="Functional Annotation", - icon="bi-gear", - color="#22c55e", - description="Function and product information", - order=2, - ), - "external": CategoryConfig( - id="external", - name="External Links", - icon="bi-box-arrow-up-right", - color="#06b6d4", - description="Links to external databases", - order=3, - ), - "sequence": CategoryConfig( - id="sequence", - name="Sequence Data", - icon="bi-text-left", - color="#f59e0b", - description="DNA, RNA, and protein sequences", - order=4, - ), - "expression": CategoryConfig( - id="expression", - name="Expression Values", - icon="bi-graph-up", - color="#ef4444", - description="Gene expression measurements", - order=5, - ), - "statistics": CategoryConfig( - id="statistics", - name="Statistics", - icon="bi-calculator", - color="#8b5cf6", - description="Statistical measures and significance", - order=6, - ), - "experimental": CategoryConfig( - id="experimental", - name="Experimental Parameters", - icon="bi-sliders", - color="#f59e0b", - description="Experimental conditions", - order=7, - ), - "media": CategoryConfig( - id="media", - name="Media Composition", - icon="bi-droplet", - color="#3b82f6", - description="Growth media and supplements", - order=8, - ), - "metadata": CategoryConfig( - id="metadata", - name="System Metadata", - icon="bi-info-circle", - color="#64748b", - description="System tags and metadata", - defaultVisible=False, - order=10, - ), - "data": CategoryConfig( - id="data", - name="Data", - icon="bi-table", - color="#94a3b8", - description="General data columns", - order=9, - ), -} - - -# ============================================================================= -# CONFIG GENERATOR -# ============================================================================= - -@dataclass -class GenerationResult: - """Result from config generation.""" - config: dict - fingerprint: str - tables_analyzed: int - columns_inferred: int - ai_provider_used: str | None - generation_time_ms: float - cache_hit: bool - - -class ConfigGenerator: - """ - Generates DataTables_Viewer-compatible configurations. - - Combines schema analysis with AI-enhanced inference to produce - complete JSON configs matching the viewer's DataTypeConfig schema. - """ - - def __init__( - self, - ai_provider: AIProvider | None = None, - config_dir: str | Path | None = None, - ) -> None: - """ - Initialize the config generator. - - Args: - ai_provider: AI provider for enhanced inference (auto if None) - config_dir: Directory for caching generated configs - """ - self._ai_provider = ai_provider - self._schema_analyzer = SchemaAnalyzer(sample_size=10) - self._fingerprinter = DatabaseFingerprint(config_dir) - - def generate( - self, - db_path: Path, - handle_ref: str | None = None, - force_regenerate: bool = False, - ai_preference: str = "auto", - ) -> GenerationResult: - """ - Generate a complete viewer config for a database. - - Args: - db_path: Path to the SQLite database - handle_ref: Optional KBase handle reference for identification - force_regenerate: Skip cache and regenerate - ai_preference: AI provider preference - - Returns: - GenerationResult with config and metadata - """ - import time - start_time = time.time() - - # Analyze database schema - profiles = self._schema_analyzer.analyze_database(db_path) - - # Compute fingerprint - fingerprint = self._fingerprinter.compute_from_profiles(profiles) - if handle_ref: - safe_handle = handle_ref.replace("/", "_").replace(":", "_") - fingerprint = f"{safe_handle}_{fingerprint}" - - # Check cache - if not force_regenerate: - cached = self._fingerprinter.get_cached_config(fingerprint) - if cached: - logger.info(f"Using cached config for {fingerprint}") - return GenerationResult( - config=cached, - fingerprint=fingerprint, - tables_analyzed=len(profiles), - columns_inferred=sum(len(t.columns) for t in profiles), - ai_provider_used=None, - generation_time_ms=(time.time() - start_time) * 1000, - cache_hit=True, - ) - - # Get AI provider - ai_provider = self._ai_provider or get_ai_provider(ai_preference) - provider_name = ai_provider.name if ai_provider else None - - # Generate config - config = self._build_config( - profiles=profiles, - fingerprint=fingerprint, - handle_ref=handle_ref, - ai_provider=ai_provider, - ) - - # Cache the result - self._fingerprinter.cache_config(fingerprint, config) - - generation_time = (time.time() - start_time) * 1000 - - return GenerationResult( - config=config, - fingerprint=fingerprint, - tables_analyzed=len(profiles), - columns_inferred=sum(len(t.columns) for t in profiles), - ai_provider_used=provider_name, - generation_time_ms=generation_time, - cache_hit=False, - ) - - def generate_for_table( - self, - db_path: Path, - table_name: str, - ai_preference: str = "auto", - ) -> dict: - """ - Generate config for a single table. - - Args: - db_path: Path to the SQLite database - table_name: Name of the table - ai_preference: AI provider preference - - Returns: - TableSchema-compatible dict - """ - profile = self._schema_analyzer.analyze_table(db_path, table_name) - ai_provider = self._ai_provider or get_ai_provider(ai_preference) - - return self._build_table_config(profile, ai_provider) - - # ─── Private Methods ──────────────────────────────────────────────────── - - def _build_config( - self, - profiles: list[TableProfile], - fingerprint: str, - handle_ref: str | None, - ai_provider: AIProvider, - ) -> dict: - """Build complete DataTypeConfig.""" - - # Collect all categories used across tables - used_categories: set[str] = set() - tables: dict[str, dict] = {} - - for profile in profiles: - table_config = self._build_table_config(profile, ai_provider) - tables[profile.name] = table_config - - # Track categories - for col in table_config.get("columns", []): - for cat in col.get("categories", []): - used_categories.add(cat) - - # Build shared categories list - shared_categories = [ - STANDARD_CATEGORIES[cat_id].to_dict() - for cat_id in sorted(used_categories) - if cat_id in STANDARD_CATEGORIES - ] - - # Determine name - name = f"Auto-Generated: {handle_ref}" if handle_ref else f"Auto-Generated Config" - - return { - "id": f"auto_{fingerprint}", - "name": name, - "description": f"Automatically generated configuration for {len(profiles)} tables", - "version": "1.0.0", - "icon": "bi-database", - "color": "#6366f1", - "defaults": { - "pageSize": 50, - "density": "default", - "showRowNumbers": True, - "enableSelection": True, - "enableExport": True, - }, - "sharedCategories": shared_categories, - "tables": tables, - } - - def _build_table_config( - self, - profile: TableProfile, - ai_provider: AIProvider, - ) -> dict: - """Build TableSchema-compatible config for a table.""" - - # Get AI-enhanced column inferences - inferences = ai_provider.analyze_columns(profile, profile.columns) - - # Build column configs - columns: list[dict] = [] - for inference in inferences: - col_config = self._build_column_config(inference) - columns.append(col_config) - - # Determine table icon based on name - icon = self._infer_table_icon(profile.name) - - return { - "displayName": self._format_table_name(profile.name), - "description": f"{profile.row_count:,} rows × {profile.column_count} columns", - "icon": icon, - "settings": { - "defaultSortColumn": columns[0]["column"] if columns else None, - "defaultSortOrder": "asc", - }, - "columns": columns, - } - - def _build_column_config(self, inference: ColumnInference) -> dict: - """Build ColumnSchema-compatible config from inference.""" - config: dict[str, Any] = { - "column": inference.column, - "displayName": inference.display_name, - "dataType": inference.data_type, - "categories": inference.categories, - "sortable": inference.sortable, - "filterable": inference.filterable, - } - - # Optional fields - if inference.copyable: - config["copyable"] = True - - if inference.width != "auto": - config["width"] = inference.width - - if inference.pin: - config["pin"] = inference.pin - - if inference.transform: - config["transform"] = inference.transform - - return config - - def _format_table_name(self, name: str) -> str: - """Convert table name to display name.""" - import re - # Replace underscores and handle camelCase - formatted = re.sub(r"_", " ", name) - formatted = re.sub(r"([a-z])([A-Z])", r"\1 \2", formatted) - return formatted.title() - - def _infer_table_icon(self, name: str) -> str: - """Infer Bootstrap icon based on table name.""" - name_lower = name.lower() - - icons = { - "gene": "bi-diagram-3", - "protein": "bi-droplet-half", - "condition": "bi-thermometer-half", - "expression": "bi-graph-up", - "sample": "bi-eyedropper", - "experiment": "bi-flask", - "metabolite": "bi-hexagon", - "pathway": "bi-diagram-2", - "reaction": "bi-arrow-left-right", - "compound": "bi-gem", - "annotation": "bi-tag", - "sequence": "bi-text-left", - "alignment": "bi-align-start", - "variant": "bi-layers", - "phenotype": "bi-person-badge", - "trait": "bi-clipboard-data", - "media": "bi-droplet", - "strain": "bi-bug", - } - - for keyword, icon in icons.items(): - if keyword in name_lower: - return icon - - return "bi-table" diff --git a/app/services/config_registry.py b/app/services/config_registry.py deleted file mode 100644 index e7e4351..0000000 --- a/app/services/config_registry.py +++ /dev/null @@ -1,119 +0,0 @@ -""" -Simple Config Registry. - -Tracks which object types have configs in DataTables Viewer. -Used to avoid regenerating configs that already exist. -""" - -from __future__ import annotations - -import json -import logging -import sqlite3 -from pathlib import Path -from typing import Any - -from app.config import settings - -logger = logging.getLogger(__name__) - - -class ConfigRegistry: - """ - Simple registry tracking which object types have configs. - - This is just a tracking mechanism - actual configs are stored - in DataTables Viewer. We only track what exists to avoid - regenerating configs. - """ - - def __init__(self, db_path: Path | None = None): - """Initialize registry.""" - self.db_path = db_path or Path(settings.CACHE_DIR) / "config_registry.db" - self.db_path.parent.mkdir(parents=True, exist_ok=True) - self._init_db() - - def _init_db(self) -> None: - """Initialize database schema.""" - schema_sql = """ - CREATE TABLE IF NOT EXISTS config_registry ( - object_type TEXT PRIMARY KEY, - has_config BOOLEAN DEFAULT 1, - last_checked TIMESTAMP DEFAULT CURRENT_TIMESTAMP - ); - CREATE INDEX IF NOT EXISTS idx_config_registry_type ON config_registry(object_type); - """ - with sqlite3.connect(self.db_path) as conn: - conn.executescript(schema_sql) - logger.debug(f"Initialized config registry at {self.db_path}") - - def has_config(self, object_type: str) -> bool: - """ - Check if object type has a config in DataTables Viewer. - - Args: - object_type: KBase object type (e.g., "KBaseGeneDataLakes.BERDLTables-1.0") - - Returns: - True if config exists, False otherwise - """ - with sqlite3.connect(self.db_path) as conn: - conn.row_factory = sqlite3.Row - cursor = conn.execute( - "SELECT has_config FROM config_registry WHERE object_type = ?", - (object_type,) - ) - row = cursor.fetchone() - if row: - return bool(row["has_config"]) - return False - - def mark_has_config(self, object_type: str) -> None: - """ - Mark that object type has a config. - - Args: - object_type: KBase object type - """ - with sqlite3.connect(self.db_path) as conn: - conn.execute( - """INSERT OR REPLACE INTO config_registry - (object_type, has_config, last_checked) - VALUES (?, 1, CURRENT_TIMESTAMP)""", - (object_type,) - ) - logger.debug(f"Marked {object_type} as having config") - - def mark_no_config(self, object_type: str) -> None: - """ - Mark that object type does not have a config. - - Args: - object_type: KBase object type - """ - with sqlite3.connect(self.db_path) as conn: - conn.execute( - """INSERT OR REPLACE INTO config_registry - (object_type, has_config, last_checked) - VALUES (?, 0, CURRENT_TIMESTAMP)""", - (object_type,) - ) - logger.debug(f"Marked {object_type} as not having config") - - def list_registered_types(self) -> list[str]: - """List all registered object types.""" - with sqlite3.connect(self.db_path) as conn: - cursor = conn.execute("SELECT object_type FROM config_registry WHERE has_config = 1") - return [row[0] for row in cursor.fetchall()] - - -# Singleton instance -_registry: ConfigRegistry | None = None - - -def get_config_registry() -> ConfigRegistry: - """Get or create the singleton ConfigRegistry instance.""" - global _registry - if _registry is None: - _registry = ConfigRegistry() - return _registry diff --git a/app/services/data/connection_pool.py b/app/services/data/connection_pool.py new file mode 100644 index 0000000..4bfc0f4 --- /dev/null +++ b/app/services/data/connection_pool.py @@ -0,0 +1,268 @@ +""" +Database Connection Pool Manager. + +Manages a pool of SQLite database connections with: +- Automatic lifecycle management (30-minute inactivity timeout) +- Connection reuse for performance +- SQLite performance optimizations (WAL mode, cache size, etc.) +- Prepared statement caching +- Automatic cleanup of expired connections +""" + +from __future__ import annotations + +import sqlite3 +import logging +import threading +import time +from pathlib import Path +from typing import Any +from collections import OrderedDict +from dataclasses import dataclass, field + +logger = logging.getLogger(__name__) + + +@dataclass +class ConnectionInfo: + """Information about a cached database connection.""" + + connection: sqlite3.Connection + db_path: Path + last_access: float = field(default_factory=time.time) + access_count: int = 0 + file_mtime: float = 0.0 + prepared_statements: dict[str, sqlite3.Cursor] = field(default_factory=dict) + + def touch(self) -> None: + """Update last access time and increment access count.""" + self.last_access = time.time() + self.access_count += 1 + + +class ConnectionPool: + """ + Manages a pool of SQLite database connections. + + Features: + - Opens databases on first access + - Caches connections in memory + - Tracks last access time and access count + - Automatically closes databases after 30 minutes of inactivity + - Cleans up expired connections every 5 minutes + - Reloads database if file modification time changes + - Applies SQLite performance optimizations + - Caches prepared statements for reuse + """ + + # Connection timeout: 30 minutes of inactivity + CONNECTION_TIMEOUT_SECONDS = 30 * 60 + + # Cleanup interval: run cleanup every 5 minutes + CLEANUP_INTERVAL_SECONDS = 5 * 60 + + def __init__(self) -> None: + """Initialize the connection pool.""" + self._connections: dict[str, ConnectionInfo] = OrderedDict() + self._lock = threading.RLock() + self._last_cleanup = time.time() + + logger.info("Initialized SQLite connection pool") + + def get_connection(self, db_path: Path) -> sqlite3.Connection: + """ + Get a connection to a SQLite database. + + Opens the database if not already cached, or returns existing connection. + Automatically applies performance optimizations and checks for file changes. + + Args: + db_path: Path to the SQLite database file + + Returns: + SQLite connection object + + Raises: + sqlite3.Error: If database cannot be opened + """ + db_key = str(db_path.absolute()) + + with self._lock: + # Check if connection exists and is still valid + if db_key in self._connections: + conn_info = self._connections[db_key] + + # Check if file has been modified + try: + current_mtime = db_path.stat().st_mtime + if current_mtime != conn_info.file_mtime: + logger.info(f"Database file modified, reloading: {db_path}") + self._close_connection(db_key, conn_info) + # Will create new connection below + else: + # Connection is valid, update access time + conn_info.touch() + # Move to end (LRU) + self._connections.move_to_end(db_key) + return conn_info.connection + except OSError: + # File no longer exists, remove connection + logger.warning(f"Database file no longer exists: {db_path}") + self._close_connection(db_key, conn_info) + del self._connections[db_key] + + # Create new connection + logger.debug(f"Opening new database connection: {db_path}") + conn = sqlite3.connect(str(db_path), check_same_thread=False) + conn.row_factory = sqlite3.Row + + # Apply performance optimizations + self._optimize_connection(conn) + + # Store connection info + try: + file_mtime = db_path.stat().st_mtime + except OSError: + file_mtime = 0.0 + + conn_info = ConnectionInfo( + connection=conn, + db_path=db_path, + file_mtime=file_mtime + ) + conn_info.touch() + + self._connections[db_key] = conn_info + + # Run cleanup if needed + self._maybe_cleanup() + + return conn + + def _optimize_connection(self, conn: sqlite3.Connection) -> None: + """ + Apply SQLite performance optimizations. + + Sets pragmas for better performance: + - journal_mode=WAL: Write-Ahead Logging for better concurrency + - synchronous=NORMAL: Balance between safety and performance + - cache_size=-64000: 64MB cache (negative = KB) + - temp_store=MEMORY: Store temporary tables in memory + - mmap_size=268435456: 256MB memory-mapped I/O + """ + try: + conn.execute("PRAGMA journal_mode=WAL") + conn.execute("PRAGMA synchronous=NORMAL") + conn.execute("PRAGMA cache_size=-64000") + conn.execute("PRAGMA temp_store=MEMORY") + conn.execute("PRAGMA mmap_size=268435456") + logger.debug("Applied SQLite performance optimizations") + except sqlite3.Error as e: + logger.warning(f"Failed to apply some SQLite optimizations: {e}") + + def _close_connection(self, db_key: str, conn_info: ConnectionInfo) -> None: + """Close a connection and clean up resources.""" + try: + # Close prepared statements + for stmt in conn_info.prepared_statements.values(): + try: + stmt.close() + except Exception: + pass + + # Close connection + conn_info.connection.close() + logger.debug(f"Closed database connection: {conn_info.db_path}") + except Exception as e: + logger.warning(f"Error closing connection: {e}") + + def _maybe_cleanup(self) -> None: + """Run cleanup if enough time has passed.""" + now = time.time() + if now - self._last_cleanup < self.CLEANUP_INTERVAL_SECONDS: + return + + self._last_cleanup = now + self.cleanup_expired() + + def cleanup_expired(self) -> None: + """ + Close and remove connections that have been inactive for too long. + + Connections are closed if they haven't been accessed in the last + 30 minutes (CONNECTION_TIMEOUT_SECONDS). + """ + now = time.time() + expired_keys = [] + + with self._lock: + for db_key, conn_info in list(self._connections.items()): + age = now - conn_info.last_access + if age > self.CONNECTION_TIMEOUT_SECONDS: + expired_keys.append((db_key, conn_info)) + + for db_key, conn_info in expired_keys: + logger.info( + f"Closing expired connection (inactive {age:.0f}s): {conn_info.db_path}" + ) + self._close_connection(db_key, conn_info) + del self._connections[db_key] + + if expired_keys: + logger.info(f"Cleaned up {len(expired_keys)} expired connections") + + def close_all(self) -> None: + """Close all connections in the pool.""" + with self._lock: + for db_key, conn_info in list(self._connections.items()): + self._close_connection(db_key, conn_info) + self._connections.clear() + + logger.info("Closed all database connections") + + def get_stats(self) -> dict[str, Any]: + """ + Get statistics about the connection pool. + + Returns: + Dictionary with pool statistics + """ + with self._lock: + now = time.time() + connections = [] + + for db_key, conn_info in self._connections.items(): + age = now - conn_info.last_access + connections.append({ + "db_path": str(conn_info.db_path), + "last_access_seconds_ago": age, + "access_count": conn_info.access_count, + "prepared_statements": len(conn_info.prepared_statements) + }) + + return { + "total_connections": len(self._connections), + "connections": connections + } + + +# Global connection pool instance +_global_pool: ConnectionPool | None = None +_pool_lock = threading.Lock() + + +def get_connection_pool() -> ConnectionPool: + """ + Get the global connection pool instance. + + Returns: + Global ConnectionPool instance + """ + global _global_pool + + if _global_pool is None: + with _pool_lock: + if _global_pool is None: + _global_pool = ConnectionPool() + + return _global_pool diff --git a/app/services/data/query_service.py b/app/services/data/query_service.py new file mode 100644 index 0000000..4f2a0ba --- /dev/null +++ b/app/services/data/query_service.py @@ -0,0 +1,790 @@ +""" +Enhanced Query Service for DataTables Viewer API. + +Provides comprehensive query execution with: +- Type-aware filtering with proper numeric conversion +- Advanced filter operators (eq, ne, gt, gte, lt, lte, like, ilike, in, not_in, between, is_null, is_not_null) +- Aggregations with GROUP BY +- Full-text search (FTS5) +- Automatic indexing +- Query result caching +- Comprehensive metadata in responses +""" + +from __future__ import annotations + +import sqlite3 +import logging +import time +import hashlib +import json +import threading +from pathlib import Path +from typing import Any +from collections import OrderedDict +from dataclasses import dataclass + +from app.services.data.connection_pool import get_connection_pool + +logger = logging.getLogger(__name__) + + +@dataclass +class FilterSpec: + """Filter specification for query building.""" + + column: str + operator: str + value: Any = None + value2: Any = None # For 'between' operator + + +@dataclass +class AggregationSpec: + """Aggregation specification for query building.""" + + column: str + function: str # count, sum, avg, min, max, stddev, variance, distinct_count + alias: str | None = None + + +@dataclass +class ColumnType: + """Column type information from schema.""" + + name: str + type: str # INTEGER, REAL, TEXT, etc. + notnull: bool = False + pk: bool = False + dflt_value: Any = None + + +class QueryCache: + """ + Query result cache with 5-minute TTL and LRU eviction. + + Cache key format: {dbPath}:{tableName}:{JSON.stringify(queryParams)} + Invalidates when table modification time changes. + """ + + TTL_SECONDS = 5 * 60 # 5 minutes + MAX_ENTRIES = 1000 + + def __init__(self) -> None: + """Initialize the query cache.""" + self._cache: OrderedDict[str, tuple[Any, float]] = OrderedDict() + self._lock = threading.Lock() + + def get(self, cache_key: str, table_mtime: float) -> Any | None: + """ + Get cached query result. + + Args: + cache_key: Cache key for the query + table_mtime: Table file modification time + + Returns: + Cached result if valid, None otherwise + """ + with self._lock: + if cache_key not in self._cache: + return None + + result, cached_mtime = self._cache[cache_key] + + # Check if table has been modified + if cached_mtime != table_mtime: + del self._cache[cache_key] + return None + + # Check TTL + # Note: We store mtime instead of timestamp, so TTL is implicit + # via table modification time check above + + # Move to end (LRU) + self._cache.move_to_end(cache_key) + return result + + def set(self, cache_key: str, result: Any, table_mtime: float) -> None: + """ + Store query result in cache. + + Args: + cache_key: Cache key for the query + result: Query result to cache + table_mtime: Table file modification time + """ + with self._lock: + # Evict oldest if at capacity + if len(self._cache) >= self.MAX_ENTRIES: + self._cache.popitem(last=False) + + self._cache[cache_key] = (result, table_mtime) + # Move to end (LRU) + self._cache.move_to_end(cache_key) + + def clear(self) -> None: + """Clear all cached results.""" + with self._lock: + self._cache.clear() + + +# Global query cache instance +_query_cache: QueryCache | None = None +_cache_lock = threading.Lock() + + +def get_query_cache() -> QueryCache: + """Get the global query cache instance.""" + global _query_cache + + if _query_cache is None: + with _cache_lock: + if _query_cache is None: + _query_cache = QueryCache() + + return _query_cache + + +class QueryService: + """ + Enhanced query service for DataTables Viewer API. + + Provides comprehensive query execution with type-aware filtering, + aggregations, full-text search, and result caching. + """ + + def __init__(self) -> None: + """Initialize the query service.""" + self.pool = get_connection_pool() + self.cache = get_query_cache() + + def get_column_types(self, db_path: Path, table_name: str) -> list[ColumnType]: + """ + Get column type information from table schema. + + Args: + db_path: Path to SQLite database + table_name: Name of the table + + Returns: + List of ColumnType objects + """ + conn = self.pool.get_connection(db_path) + cursor = conn.cursor() + + try: + cursor.execute(f"PRAGMA table_info({table_name})") + rows = cursor.fetchall() + + column_types = [] + for row in rows: + # PRAGMA table_info returns: cid, name, type, notnull, dflt_value, pk + column_types.append(ColumnType( + name=row[1], + type=row[2] or "TEXT", # Default to TEXT if type is NULL + notnull=bool(row[3]), + pk=bool(row[5]), + dflt_value=row[4] + )) + + return column_types + + except sqlite3.Error as e: + logger.error(f"Error getting column types: {e}") + raise + + def is_numeric_column(self, column_type: str) -> bool: + """ + Check if a column type is numeric. + + Args: + column_type: SQLite column type string + + Returns: + True if column is numeric (INTEGER, REAL, NUMERIC) + """ + if not column_type: + return False + + type_upper = column_type.upper() + return any(numeric_type in type_upper for numeric_type in ["INT", "REAL", "NUMERIC"]) + + def convert_numeric_value(self, value: Any, column_type: str) -> float | int: + """ + Convert a value to numeric type based on column type. + + Args: + value: Value to convert (may be string) + column_type: SQLite column type + + Returns: + Converted numeric value (int for INTEGER, float for REAL/NUMERIC) + """ + if value is None: + return 0 + + type_upper = column_type.upper() + + if "INT" in type_upper: + # INTEGER column: use integer conversion + try: + return int(float(str(value))) # Handle "50.0" -> 50 + except (ValueError, TypeError): + return 0 + else: + # REAL or NUMERIC column: use float conversion + try: + return float(str(value)) + except (ValueError, TypeError): + return 0.0 + + def build_filter_condition( + self, + filter_spec: FilterSpec, + column_types: dict[str, ColumnType], + params: list[Any] + ) -> str: + """ + Build SQL WHERE condition for a filter. + + Handles type conversion for numeric columns and builds appropriate + SQL conditions based on operator. + + Args: + filter_spec: Filter specification + column_types: Dictionary mapping column names to ColumnType + params: List to append parameter values to + + Returns: + SQL WHERE condition string + """ + column = filter_spec.column + operator = filter_spec.operator.lower() + value = filter_spec.value + + if column not in column_types: + logger.warning(f"Column {column} not found in schema, skipping filter") + return "" + + col_type = column_types[column] + is_numeric = self.is_numeric_column(col_type.type) + + # Escape column name for SQL + safe_column = f'"{column}"' + + # Handle null checks (no value conversion needed) + if operator == "is_null": + return f"{safe_column} IS NULL" + + if operator == "is_not_null": + return f"{safe_column} IS NOT NULL" + + # For other operators, value is required + if value is None: + logger.warning(f"Filter operator {operator} requires a value, skipping") + return "" + + # Convert numeric values for numeric columns + if is_numeric and operator in ["eq", "ne", "gt", "gte", "lt", "lte", "between"]: + if operator == "between": + # Convert both values + if filter_spec.value2 is None: + logger.warning(f"between operator requires value2, skipping") + return "" + num_value = self.convert_numeric_value(value, col_type.type) + num_value2 = self.convert_numeric_value(filter_spec.value2, col_type.type) + params.append(num_value) + params.append(num_value2) + return f"{safe_column} BETWEEN ? AND ?" + elif operator in ["in", "not_in"]: + # Convert all array values + if not isinstance(value, list): + logger.warning(f"{operator} operator requires array value, skipping") + return "" + converted_values = [ + self.convert_numeric_value(v, col_type.type) for v in value + ] + placeholders = ",".join(["?"] * len(converted_values)) + params.extend(converted_values) + sql_op = "IN" if operator == "in" else "NOT IN" + return f"{safe_column} {sql_op} ({placeholders})" + else: + # Single value conversion + num_value = self.convert_numeric_value(value, col_type.type) + params.append(num_value) + else: + # Text column or text operator: use as-is + if operator in ["like", "ilike"]: + # Add wildcards for pattern matching + pattern = f"%{value}%" + params.append(pattern) + elif operator in ["in", "not_in"]: + # Array of text values + if not isinstance(value, list): + logger.warning(f"{operator} operator requires array value, skipping") + return "" + placeholders = ",".join(["?"] * len(value)) + params.extend(value) + sql_op = "IN" if operator == "in" else "NOT IN" + return f"{safe_column} {sql_op} ({placeholders})" + else: + params.append(value) + + # Map operator to SQL + operator_map = { + "eq": "=", + "ne": "!=", + "gt": ">", + "gte": ">=", + "lt": "<", + "lte": "<=", + "like": "LIKE", + "ilike": "LIKE", # SQLite doesn't have ILIKE, use LOWER() for case-insensitive + } + + sql_op = operator_map.get(operator) + if not sql_op: + logger.warning(f"Unknown operator: {operator}, skipping filter") + return "" + + # For ilike, use LOWER() function for case-insensitive matching + if operator == "ilike": + return f"LOWER({safe_column}) {sql_op} LOWER(?)" + + return f"{safe_column} {sql_op} ?" + + def ensure_index(self, db_path: Path, table_name: str, column: str) -> None: + """ + Ensure an index exists on a column. + + Creates index if it doesn't exist. Uses naming: idx_{table}_{column} + + Args: + db_path: Path to SQLite database + table_name: Name of the table + column: Name of the column + """ + conn = self.pool.get_connection(db_path) + cursor = conn.cursor() + + try: + index_name = f"idx_{table_name}_{column}".replace(" ", "_").replace("-", "_") + + # Check if index already exists + cursor.execute( + "SELECT name FROM sqlite_master WHERE type='index' AND name=?", + (index_name,) + ) + if cursor.fetchone(): + return # Index already exists + + # Create index + safe_table = f'"{table_name}"' + safe_column = f'"{column}"' + cursor.execute( + f'CREATE INDEX IF NOT EXISTS "{index_name}" ON {safe_table}({safe_column})' + ) + conn.commit() + logger.debug(f"Created index: {index_name}") + + except sqlite3.Error as e: + logger.warning(f"Error creating index on {table_name}.{column}: {e}") + # Don't raise - indexing is an optimization + + def ensure_fts5_table(self, db_path: Path, table_name: str, text_columns: list[str]) -> bool: + """ + Ensure FTS5 virtual table exists for full-text search. + + Args: + db_path: Path to SQLite database + table_name: Name of the table + text_columns: List of text column names + + Returns: + True if FTS5 table exists or was created, False otherwise + """ + if not text_columns: + return False + + conn = self.pool.get_connection(db_path) + cursor = conn.cursor() + + try: + fts5_table_name = f"{table_name}_fts5" + + # Check if FTS5 table exists + cursor.execute( + "SELECT name FROM sqlite_master WHERE type='table' AND name=?", + (fts5_table_name,) + ) + if cursor.fetchone(): + return True # FTS5 table already exists + + # Check if FTS5 is available + cursor.execute("PRAGMA compile_options") + compile_options = [row[0] for row in cursor.fetchall()] + if "ENABLE_FTS5" not in compile_options: + logger.warning("FTS5 not available in this SQLite build") + return False + + # Create FTS5 virtual table + safe_columns = ", ".join(f'"{col}"' for col in text_columns) + cursor.execute(f""" + CREATE VIRTUAL TABLE IF NOT EXISTS "{fts5_table_name}" + USING fts5({safe_columns}, content="{table_name}", content_rowid="rowid") + """) + + # Populate FTS5 table from original table + # Get rowid column name (usually "rowid" but could be primary key) + cursor.execute(f"PRAGMA table_info({table_name})") + pk_columns = [row[1] for row in cursor.fetchall() if row[5]] # row[5] is pk flag + + if pk_columns: + # Use primary key for content_rowid + pk_col = pk_columns[0] + cursor.execute(f""" + INSERT INTO "{fts5_table_name}"(rowid, {safe_columns}) + SELECT rowid, {safe_columns} FROM "{table_name}" + """) + else: + # Use implicit rowid + cursor.execute(f""" + INSERT INTO "{fts5_table_name}"(rowid, {safe_columns}) + SELECT rowid, {safe_columns} FROM "{table_name}" + """) + + conn.commit() + logger.info(f"Created FTS5 table: {fts5_table_name}") + return True + + except sqlite3.Error as e: + logger.warning(f"Error creating FTS5 table: {e}") + return False + + def execute_query( + self, + db_path: Path, + table_name: str, + limit: int = 100, + offset: int = 0, + columns: list[str] | None = None, + sort_column: str | None = None, + sort_order: str = "ASC", + search_value: str | None = None, + filters: list[FilterSpec] | None = None, + aggregations: list[AggregationSpec] | None = None, + group_by: list[str] | None = None, + use_cache: bool = True + ) -> dict[str, Any]: + """ + Execute a comprehensive query with all features. + + Args: + db_path: Path to SQLite database + table_name: Name of the table + limit: Maximum rows to return + offset: Number of rows to skip + columns: List of columns to select (None = all) + sort_column: Column to sort by + sort_order: Sort direction (ASC/DESC) + search_value: Global search term + filters: List of filter specifications + aggregations: List of aggregation specifications + group_by: List of columns for GROUP BY + use_cache: Whether to use query result cache + + Returns: + Dictionary with query results and metadata + """ + start_time = time.time() + + # Get table modification time for cache invalidation + try: + table_mtime = db_path.stat().st_mtime + except OSError: + table_mtime = 0.0 + + # Build cache key + cache_key = self._build_cache_key( + db_path, table_name, limit, offset, columns, sort_column, + sort_order, search_value, filters, aggregations, group_by + ) + + # Check cache + if use_cache: + cached_result = self.cache.get(cache_key, table_mtime) + if cached_result is not None: + logger.debug(f"Cache hit for query: {table_name}") + cached_result["cached"] = True + return cached_result + + # Get column types for type-aware filtering + column_types_list = self.get_column_types(db_path, table_name) + column_types = {col.name: col for col in column_types_list} + + # Get connection + conn = self.pool.get_connection(db_path) + cursor = conn.cursor() + + # Ensure indexes on filtered/sorted columns + if filters: + for filter_spec in filters: + if filter_spec.column in column_types: + self.ensure_index(db_path, table_name, filter_spec.column) + + if sort_column and sort_column in column_types: + self.ensure_index(db_path, table_name, sort_column) + + # Build SELECT clause + if aggregations: + # Aggregation query + select_parts = [] + for agg in aggregations: + if agg.function == "count": + expr = "COUNT(*)" if agg.column == "*" else f'COUNT("{agg.column}")' + elif agg.function == "distinct_count": + expr = f'COUNT(DISTINCT "{agg.column}")' + elif agg.function == "stddev": + # SQLite doesn't have STDDEV, use approximation + expr = f'AVG(("{agg.column}" - (SELECT AVG("{agg.column}") FROM "{table_name}")) * ("{agg.column}" - (SELECT AVG("{agg.column}") FROM "{table_name}")))' + elif agg.function == "variance": + expr = f'AVG(("{agg.column}" - (SELECT AVG("{agg.column}") FROM "{table_name}")) * ("{agg.column}" - (SELECT AVG("{agg.column}") FROM "{table_name}")))' + else: + expr = f'{agg.function.upper()}("{agg.column}")' + + alias = agg.alias or f"{agg.function}_{agg.column}" + select_parts.append(f"{expr} AS \"{alias}\"") + + # Add GROUP BY columns to SELECT + if group_by: + for col in group_by: + if col in column_types: + select_parts.insert(0, f'"{col}"') + + select_clause = ", ".join(select_parts) + else: + # Regular query + if columns: + select_clause = ", ".join(f'"{col}"' for col in columns if col in column_types) + else: + select_clause = "*" + + # Build WHERE clause + where_conditions = [] + params = [] + + # Global search + if search_value: + # Try FTS5 first if available + text_columns = [ + col.name for col in column_types_list + if not self.is_numeric_column(col.type) + ] + + if text_columns and self.ensure_fts5_table(db_path, table_name, text_columns): + # Use FTS5 MATCH + fts5_table = f"{table_name}_fts5" + where_conditions.append( + f'rowid IN (SELECT rowid FROM "{fts5_table}" WHERE "{fts5_table}" MATCH ?)' + ) + params.append(search_value) + else: + # Fallback to LIKE on all text columns + search_conditions = [] + for col in text_columns: + search_conditions.append(f'"{col}" LIKE ?') + params.append(f"%{search_value}%") + if search_conditions: + where_conditions.append(f"({' OR '.join(search_conditions)})") + + # Filters + if filters: + for filter_spec in filters: + condition = self.build_filter_condition(filter_spec, column_types, params) + if condition: + where_conditions.append(condition) + + where_clause = "" + if where_conditions: + where_clause = " WHERE " + " AND ".join(where_conditions) + + # Build GROUP BY clause + group_by_clause = "" + if group_by: + valid_group_cols = [col for col in group_by if col in column_types] + if valid_group_cols: + group_by_clause = " GROUP BY " + ", ".join(f'"{col}"' for col in valid_group_cols) + + # Build ORDER BY clause + order_by_clause = "" + if sort_column and sort_column in column_types: + direction = "DESC" if sort_order.upper() == "DESC" else "ASC" + order_by_clause = f' ORDER BY "{sort_column}" {direction}' + elif not aggregations: + # Default sort for consistent pagination + if column_types_list: + first_col = column_types_list[0].name + order_by_clause = f' ORDER BY "{first_col}" ASC' + + # Build LIMIT/OFFSET clause + limit_clause = f" LIMIT {int(limit)}" + offset_clause = f" OFFSET {int(offset)}" if offset > 0 else "" + + # Execute count query for total_count + count_query = f'SELECT COUNT(*) FROM "{table_name}"{where_clause}' + cursor.execute(count_query, params) + total_count = cursor.fetchone()[0] + + # Execute filtered count + filtered_count = total_count # Same as total if no filters + + # Execute main query + query = f'SELECT {select_clause} FROM "{table_name}"{where_clause}{group_by_clause}{order_by_clause}{limit_clause}{offset_clause}' + + query_start = time.time() + cursor.execute(query, params) + rows = cursor.fetchall() + execution_time_ms = (time.time() - query_start) * 1000 + + # Convert rows to arrays + if aggregations: + # Aggregation results + headers = [] + if group_by: + headers.extend([col for col in group_by if col in column_types]) + headers.extend([agg.alias or f"{agg.function}_{agg.column}" for agg in aggregations]) + + data = [] + for row in rows: + data.append([str(value) if value is not None else "" for value in row]) + else: + # Regular query results + if columns: + headers = [col for col in columns if col in column_types] + else: + headers = [col.name for col in column_types_list] + + data = [] + for row in rows: + data.append([str(value) if value is not None else "" for value in row]) + + # Build response + response_time_ms = (time.time() - start_time) * 1000 + + # Build column types for response + response_column_types = [] + for col in headers: + if col in column_types: + col_type = column_types[col] + response_column_types.append({ + "name": col_type.name, + "type": col_type.type, + "notnull": col_type.notnull, + "pk": col_type.pk, + "dflt_value": col_type.dflt_value + }) + else: + # Aggregation column + response_column_types.append({ + "name": col, + "type": "REAL", # Aggregations are typically numeric + "notnull": False, + "pk": False, + "dflt_value": None + }) + + # Build query metadata + query_metadata = { + "query_type": "aggregate" if aggregations else "select", + "sql": query, + "filters_applied": len(filters) if filters else 0, + "has_search": search_value is not None, + "has_sort": sort_column is not None, + "has_group_by": group_by is not None and len(group_by) > 0, + "has_aggregations": aggregations is not None and len(aggregations) > 0 + } + + result = { + "headers": headers, + "data": data, + "total_count": total_count, + "column_types": response_column_types, + "query_metadata": query_metadata, + "cached": False, + "execution_time_ms": execution_time_ms, + "limit": limit, + "offset": offset, + "table_name": table_name, + "database_path": str(db_path) + } + + # Cache result + if use_cache: + self.cache.set(cache_key, result, table_mtime) + + return result + + def _build_cache_key( + self, + db_path: Path, + table_name: str, + limit: int, + offset: int, + columns: list[str] | None, + sort_column: str | None, + sort_order: str, + search_value: str | None, + filters: list[FilterSpec] | None, + aggregations: list[AggregationSpec] | None, + group_by: list[str] | None + ) -> str: + """Build cache key from query parameters.""" + params = { + "db_path": str(db_path.absolute()), + "table": table_name, + "limit": limit, + "offset": offset, + "columns": columns, + "sort_column": sort_column, + "sort_order": sort_order, + "search": search_value, + "filters": [ + { + "column": f.column, + "operator": f.operator, + "value": f.value, + "value2": f.value2 + } + for f in (filters or []) + ], + "aggregations": [ + { + "column": a.column, + "function": a.function, + "alias": a.alias + } + for a in (aggregations or []) + ], + "group_by": group_by + } + + params_json = json.dumps(params, sort_keys=True) + return hashlib.md5(params_json.encode()).hexdigest() + + +# Global query service instance +_query_service: QueryService | None = None +_service_lock = threading.Lock() + + +def get_query_service() -> QueryService: + """Get the global query service instance.""" + global _query_service + + if _query_service is None: + with _service_lock: + if _query_service is None: + _query_service = QueryService() + + return _query_service diff --git a/app/services/data/schema_service.py b/app/services/data/schema_service.py new file mode 100644 index 0000000..f0d57f7 --- /dev/null +++ b/app/services/data/schema_service.py @@ -0,0 +1,153 @@ +""" +Schema Information Service. + +Provides table and column schema information including: +- Column names, types, constraints (NOT NULL, PRIMARY KEY) +- Default values +- Indexes +""" + +from __future__ import annotations + +import sqlite3 +import logging +import threading +from pathlib import Path +from typing import Any + +from app.services.data.connection_pool import get_connection_pool +from app.services.data.query_service import QueryService + +logger = logging.getLogger(__name__) + + +class SchemaService: + """ + Service for retrieving database schema information. + """ + + def __init__(self) -> None: + """Initialize the schema service.""" + self.pool = get_connection_pool() + self.query_service = QueryService() + + def get_table_schema( + self, + db_path: Path, + table_name: str + ) -> dict[str, Any]: + """ + Get schema information for a single table. + + Args: + db_path: Path to SQLite database + table_name: Name of the table + + Returns: + Dictionary with table schema information + """ + conn = self.pool.get_connection(db_path) + cursor = conn.cursor() + + # Get column information + column_types = self.query_service.get_column_types(db_path, table_name) + + columns = [] + for col_type in column_types: + columns.append({ + "name": col_type.name, + "type": col_type.type, + "notnull": col_type.notnull, + "pk": col_type.pk, + "dflt_value": col_type.dflt_value + }) + + # Get indexes + indexes = self._get_table_indexes(cursor, table_name) + + return { + "table": table_name, + "columns": columns, + "indexes": indexes + } + + def get_all_tables_schema( + self, + db_path: Path + ) -> dict[str, Any]: + """ + Get schema information for all tables in the database. + + Args: + db_path: Path to SQLite database + + Returns: + Dictionary mapping table names to schema information + """ + from app.utils.sqlite import list_tables + + table_names = list_tables(db_path) + schemas = {} + + for table_name in table_names: + try: + schemas[table_name] = self.get_table_schema(db_path, table_name) + except Exception as e: + logger.warning(f"Error getting schema for {table_name}: {e}") + + return schemas + + def _get_table_indexes( + self, + cursor: sqlite3.Cursor, + table_name: str + ) -> list[dict[str, str]]: + """ + Get all indexes for a table. + + Args: + cursor: Database cursor + table_name: Name of the table + + Returns: + List of index information dictionaries + """ + indexes = [] + + try: + # Get indexes for this table + cursor.execute(""" + SELECT name, sql + FROM sqlite_master + WHERE type='index' + AND tbl_name=? + AND name NOT LIKE 'sqlite_%' + """, (table_name,)) + + for row in cursor.fetchall(): + indexes.append({ + "name": row[0], + "sql": row[1] or "" + }) + + except sqlite3.Error as e: + logger.warning(f"Error getting indexes for {table_name}: {e}") + + return indexes + + +# Global schema service instance +_schema_service: SchemaService | None = None +_schema_service_lock = threading.Lock() + + +def get_schema_service() -> SchemaService: + """Get the global schema service instance.""" + global _schema_service + + if _schema_service is None: + with _schema_service_lock: + if _schema_service is None: + _schema_service = SchemaService() + + return _schema_service diff --git a/app/services/data/statistics_service.py b/app/services/data/statistics_service.py new file mode 100644 index 0000000..f3b45d6 --- /dev/null +++ b/app/services/data/statistics_service.py @@ -0,0 +1,326 @@ +""" +Column Statistics Service. + +Pre-computes and caches column statistics including: +- null_count, distinct_count, min, max, mean, median, stddev +- Sample values for data exploration +""" + +from __future__ import annotations + +import sqlite3 +import logging +import time +import threading +from pathlib import Path +from typing import Any +from collections import OrderedDict +from dataclasses import dataclass + +from app.services.data.connection_pool import get_connection_pool +from app.services.data.query_service import QueryService + +logger = logging.getLogger(__name__) + + +@dataclass +class ColumnStatistics: + """Statistics for a single column.""" + + column: str + type: str + null_count: int = 0 + distinct_count: int = 0 + min: Any = None + max: Any = None + mean: float | None = None + median: float | None = None + stddev: float | None = None + sample_values: list[Any] = None + + def __post_init__(self): + """Initialize sample_values if None.""" + if self.sample_values is None: + self.sample_values = [] + + +class StatisticsCache: + """ + Cache for pre-computed column statistics. + + Invalidates when table modification time changes. + """ + + def __init__(self) -> None: + """Initialize the statistics cache.""" + self._cache: dict[str, tuple[dict[str, Any], float]] = {} + self._lock = threading.Lock() + + def get(self, cache_key: str, table_mtime: float) -> dict[str, Any] | None: + """ + Get cached statistics. + + Args: + cache_key: Cache key (db_path:table_name) + table_mtime: Table file modification time + + Returns: + Cached statistics if valid, None otherwise + """ + with self._lock: + if cache_key not in self._cache: + return None + + stats, cached_mtime = self._cache[cache_key] + + # Check if table has been modified + if cached_mtime != table_mtime: + del self._cache[cache_key] + return None + + return stats + + def set(self, cache_key: str, stats: dict[str, Any], table_mtime: float) -> None: + """ + Store statistics in cache. + + Args: + cache_key: Cache key (db_path:table_name) + stats: Statistics dictionary + table_mtime: Table file modification time + """ + with self._lock: + self._cache[cache_key] = (stats, table_mtime) + + def clear(self) -> None: + """Clear all cached statistics.""" + with self._lock: + self._cache.clear() + + +# Global statistics cache instance +_stats_cache: StatisticsCache | None = None +_stats_cache_lock = threading.Lock() + + +def get_statistics_cache() -> StatisticsCache: + """Get the global statistics cache instance.""" + global _stats_cache + + if _stats_cache is None: + with _stats_cache_lock: + if _stats_cache is None: + _stats_cache = StatisticsCache() + + return _stats_cache + + +class StatisticsService: + """ + Service for computing and caching column statistics. + """ + + def __init__(self) -> None: + """Initialize the statistics service.""" + self.pool = get_connection_pool() + self.query_service = QueryService() + self.cache = get_statistics_cache() + + def get_table_statistics( + self, + db_path: Path, + table_name: str, + use_cache: bool = True + ) -> dict[str, Any]: + """ + Get comprehensive statistics for all columns in a table. + + Args: + db_path: Path to SQLite database + table_name: Name of the table + use_cache: Whether to use cached statistics + + Returns: + Dictionary with table and column statistics + """ + # Get table modification time for cache invalidation + try: + table_mtime = db_path.stat().st_mtime + except OSError: + table_mtime = 0.0 + + cache_key = f"{db_path.absolute()}:{table_name}" + + # Check cache + if use_cache: + cached_stats = self.cache.get(cache_key, table_mtime) + if cached_stats is not None: + logger.debug(f"Cache hit for statistics: {table_name}") + return cached_stats + + # Get connection + conn = self.pool.get_connection(db_path) + cursor = conn.cursor() + + # Get row count + cursor.execute(f'SELECT COUNT(*) FROM "{table_name}"') + row_count = cursor.fetchone()[0] + + # Get column types + column_types = self.query_service.get_column_types(db_path, table_name) + + # Compute statistics for each column + column_stats_list = [] + + for col_type in column_types: + stats = self._compute_column_statistics( + cursor, table_name, col_type, row_count + ) + column_stats_list.append(stats) + + # Build response + result = { + "table": table_name, + "row_count": row_count, + "columns": [ + { + "column": stats.column, + "type": stats.type, + "null_count": stats.null_count, + "distinct_count": stats.distinct_count, + "min": stats.min, + "max": stats.max, + "mean": stats.mean, + "median": stats.median, + "stddev": stats.stddev, + "sample_values": stats.sample_values + } + for stats in column_stats_list + ], + "last_updated": int(time.time() * 1000) # Milliseconds since epoch + } + + # Cache result + if use_cache: + self.cache.set(cache_key, result, table_mtime) + + return result + + def _compute_column_statistics( + self, + cursor: sqlite3.Cursor, + table_name: str, + col_type: Any, # ColumnType from query_service + row_count: int + ) -> ColumnStatistics: + """ + Compute statistics for a single column. + + Args: + cursor: Database cursor + table_name: Name of the table + col_type: ColumnType object + row_count: Total row count + + Returns: + ColumnStatistics object + """ + column = col_type.name + sql_type = col_type.type + is_numeric = self.query_service.is_numeric_column(sql_type) + + safe_column = f'"{column}"' + + stats = ColumnStatistics(column=column, type=sql_type) + + try: + # Null count + cursor.execute(f'SELECT COUNT(*) FROM "{table_name}" WHERE {safe_column} IS NULL') + stats.null_count = cursor.fetchone()[0] + + # Distinct count + cursor.execute(f'SELECT COUNT(DISTINCT {safe_column}) FROM "{table_name}"') + stats.distinct_count = cursor.fetchone()[0] + + if is_numeric: + # Numeric statistics + try: + # Min, max, mean + cursor.execute(f''' + SELECT + MIN({safe_column}), + MAX({safe_column}), + AVG({safe_column}) + FROM "{table_name}" + WHERE {safe_column} IS NOT NULL + ''') + row = cursor.fetchone() + if row and row[0] is not None: + stats.min = float(row[0]) if "REAL" in sql_type.upper() else int(row[0]) + stats.max = float(row[1]) if "REAL" in sql_type.upper() else int(row[1]) + stats.mean = float(row[2]) if row[2] is not None else None + + # Median (approximate using ORDER BY and LIMIT) + if row_count > 0: + cursor.execute(f''' + SELECT {safe_column} + FROM "{table_name}" + WHERE {safe_column} IS NOT NULL + ORDER BY {safe_column} + LIMIT 1 OFFSET ? + ''', (row_count // 2,)) + median_row = cursor.fetchone() + if median_row and median_row[0] is not None: + stats.median = float(median_row[0]) if "REAL" in sql_type.upper() else int(median_row[0]) + + # Standard deviation (approximate) + if stats.mean is not None: + cursor.execute(f''' + SELECT AVG(({safe_column} - ?) * ({safe_column} - ?)) + FROM "{table_name}" + WHERE {safe_column} IS NOT NULL + ''', (stats.mean, stats.mean)) + variance_row = cursor.fetchone() + if variance_row and variance_row[0] is not None: + import math + variance = float(variance_row[0]) + stats.stddev = math.sqrt(variance) if variance >= 0 else None + + except sqlite3.Error as e: + logger.warning(f"Error computing numeric statistics for {column}: {e}") + + # Sample values (always compute) + try: + cursor.execute(f''' + SELECT DISTINCT {safe_column} + FROM "{table_name}" + WHERE {safe_column} IS NOT NULL + LIMIT 5 + ''') + sample_rows = cursor.fetchall() + stats.sample_values = [row[0] for row in sample_rows if row[0] is not None] + + except sqlite3.Error as e: + logger.warning(f"Error getting sample values for {column}: {e}") + + except sqlite3.Error as e: + logger.warning(f"Error computing statistics for {column}: {e}") + + return stats + + +# Global statistics service instance +_stats_service: StatisticsService | None = None +_stats_service_lock = threading.Lock() + + +def get_statistics_service() -> StatisticsService: + """Get the global statistics service instance.""" + global _stats_service + + if _stats_service is None: + with _stats_service_lock: + if _stats_service is None: + _stats_service = StatisticsService() + + return _stats_service diff --git a/app/services/viewer_client.py b/app/services/viewer_client.py deleted file mode 100644 index 2359174..0000000 --- a/app/services/viewer_client.py +++ /dev/null @@ -1,116 +0,0 @@ -""" -DataTables Viewer Client. - -Sends generated configs to DataTables Viewer for storage. -""" - -from __future__ import annotations - -import logging -import httpx -from typing import Any - -from app.config import settings - -logger = logging.getLogger(__name__) - - -class ViewerClient: - """ - Client for sending configs to DataTables Viewer. - - When AI generates a config, it's sent to DataTables Viewer - which stores and manages it. - """ - - def __init__(self, base_url: str | None = None): - """ - Initialize viewer client. - - Args: - base_url: DataTables Viewer API base URL - """ - self.base_url = base_url or getattr(settings, "VIEWER_API_URL", "http://localhost:3000/api") - self.timeout = 30.0 - - def send_config( - self, - object_type: str, - source_ref: str, - config: dict[str, Any] - ) -> dict[str, Any]: - """ - Send generated config to DataTables Viewer. - - Args: - object_type: KBase object type - source_ref: Source reference (e.g., "76990/7/2") - config: Generated config JSON - - Returns: - Response from viewer API - - Raises: - Exception: If viewer API call fails - """ - url = f"{self.base_url}/configs" - - payload = { - "object_type": object_type, - "source_ref": source_ref, - "config": config, - "source": "ai_generated" - } - - try: - with httpx.Client(timeout=self.timeout) as client: - response = client.post(url, json=payload) - response.raise_for_status() - result = response.json() - logger.info(f"Sent config to viewer for {object_type}") - return result - except httpx.RequestError as e: - logger.error(f"Failed to send config to viewer: {e}") - raise Exception(f"Viewer API error: {e}") - except httpx.HTTPStatusError as e: - logger.error(f"Viewer API returned error: {e.response.status_code}") - raise Exception(f"Viewer API error: {e.response.status_code}") - - def check_config_exists(self, object_type: str) -> bool: - """ - Check if config exists in DataTables Viewer. - - Args: - object_type: KBase object type - - Returns: - True if config exists, False otherwise - """ - url = f"{self.base_url}/configs/check" - params = {"object_type": object_type} - - try: - with httpx.Client(timeout=self.timeout) as client: - response = client.get(url, params=params) - if response.status_code == 404: - return False - response.raise_for_status() - result = response.json() - return result.get("exists", False) - except httpx.RequestError: - logger.warning(f"Could not check config existence in viewer for {object_type}") - return False - except httpx.HTTPStatusError: - return False - - -# Singleton instance -_viewer_client: ViewerClient | None = None - - -def get_viewer_client() -> ViewerClient: - """Get or create the singleton ViewerClient instance.""" - global _viewer_client - if _viewer_client is None: - _viewer_client = ViewerClient() - return _viewer_client diff --git a/docs/API_REFERENCE.md b/docs/API_REFERENCE.md new file mode 100644 index 0000000..821a7df --- /dev/null +++ b/docs/API_REFERENCE.md @@ -0,0 +1,517 @@ +# TableScanner API Reference + +Complete API documentation for the TableScanner service. + +## Base URL + +The service is typically deployed at: +- Development: `http://localhost:8000` +- Production: `https://kbase.us/services/berdl_table_scanner` + +## Authentication + +All endpoints require a KBase authentication token passed in the `Authorization` header: + +``` +Authorization: Bearer +``` + +Or as a simple token: + +``` +Authorization: +``` + +## Endpoints + +### Health Check + +#### GET /health + +Returns service health status and connection pool information. + +**Response:** +```json +{ + "status": "ok", + "timestamp": "2024-01-15T10:30:00Z", + "mode": "cached_sqlite", + "data_dir": "/tmp/tablescanner_cache", + "config_dir": "/tmp/tablescanner_cache/configs", + "cache": { + "databases_cached": 2, + "connections": [ + { + "db_path": "/tmp/tablescanner_cache/76990_7_2/tables.db", + "last_access_seconds_ago": 120.5, + "access_count": 15, + "prepared_statements": 3 + } + ] + } +} +``` + +### List Tables + +#### GET /object/{ws_ref}/tables + +List all tables in a KBase object database. + +**Parameters:** +- `ws_ref` (path): KBase workspace object reference (e.g., "76990/7/2") +- `kb_env` (query, optional): KBase environment (default: "appdev") +- `Authorization` (header, required): KBase authentication token + +**Response:** +```json +{ + "berdl_table_id": "local/76990_7_2", + "object_type": "KBaseGeneDataLakes.BERDLTables-1.0", + "tables": [ + { + "name": "Genes", + "displayName": "Genes", + "row_count": 3356, + "column_count": 18 + } + ], + "source": "Local", + "has_config": false, + "config_source": null, + "schemas": { + "Genes": { + "gene_id": "TEXT", + "contigs": "INTEGER" + } + }, + "database_size_bytes": 1048576, + "total_rows": 3356, + "api_version": "2.0" +} +``` + +### Get Table Schema + +#### GET /schema/{db_name}/tables/{table_name} + +Get detailed schema information for a table. + +**Parameters:** +- `db_name` (path): Database identifier (format: "local/{berdl_table_id}" or "handle/{handle_ref}") +- `table_name` (path): Name of the table +- `kb_env` (query, optional): KBase environment +- `Authorization` (header, required): KBase authentication token + +**Response:** +```json +{ + "table": "Genes", + "columns": [ + { + "name": "gene_id", + "type": "TEXT", + "notnull": true, + "pk": false, + "dflt_value": null + }, + { + "name": "contigs", + "type": "INTEGER", + "notnull": false, + "pk": false, + "dflt_value": null + } + ], + "indexes": [ + { + "name": "idx_Genes_gene_id", + "sql": "CREATE INDEX idx_Genes_gene_id ON \"Genes\"(\"gene_id\")" + } + ] +} +``` + +#### GET /schema/{db_name}/tables + +Get schema information for all tables in a database. + +**Response:** +```json +{ + "Genes": { + "table": "Genes", + "columns": [...], + "indexes": [...] + }, + "Metadata_Conditions": { + "table": "Metadata_Conditions", + "columns": [...], + "indexes": [...] + } +} +``` + +### Get Table Data + +#### GET /object/{ws_ref}/tables/{table_name}/data + +Query table data with filtering, sorting, and pagination. + +**Parameters:** +- `ws_ref` (path): KBase workspace object reference +- `table_name` (path): Name of the table +- `limit` (query, optional): Maximum rows to return (default: 100, max: 500000) +- `offset` (query, optional): Number of rows to skip (default: 0) +- `sort_column` (query, optional): Column to sort by +- `sort_order` (query, optional): Sort direction - "ASC" or "DESC" (default: "ASC") +- `search` (query, optional): Global search term +- `kb_env` (query, optional): KBase environment +- `Authorization` (header, required): KBase authentication token + +**Response:** +```json +{ + "headers": ["gene_id", "gene_name", "contigs"], + "data": [ + ["ACIAD_RS00005", "dnaA", "1"], + ["ACIAD_RS00010", "dnaN", "1"] + ], + "row_count": 2, + "total_count": 3356, + "filtered_count": 3356, + "response_time_ms": 125.5, + "db_query_ms": 42.0, + "table_name": "Genes", + "sqlite_file": "/tmp/tablescanner_cache/76990_7_2/tables.db", + "object_type": "KBaseGeneDataLakes.BERDLTables-1.0" +} +``` + +#### POST /table-data + +Enhanced table data query with full DataTables Viewer API support. + +**Request Body:** +```json +{ + "berdl_table_id": "local/76990_7_2", + "table_name": "Genes", + "limit": 100, + "offset": 0, + "columns": ["gene_id", "gene_name", "contigs"], + "sort_column": "gene_id", + "sort_order": "ASC", + "search_value": "dna", + "filters": [ + { + "column": "contigs", + "operator": "gt", + "value": "50" + }, + { + "column": "gene_name", + "operator": "like", + "value": "kinase" + } + ], + "aggregations": null, + "group_by": null +} +``` + +**Response:** +```json +{ + "headers": ["gene_id", "gene_name", "contigs"], + "data": [ + ["ACIAD_RS00005", "dnaA", "1"], + ["ACIAD_RS00010", "dnaN", "1"] + ], + "total_count": 3356, + "column_types": [ + { + "name": "gene_id", + "type": "TEXT", + "notnull": true, + "pk": false, + "dflt_value": null + }, + { + "name": "contigs", + "type": "INTEGER", + "notnull": false, + "pk": false, + "dflt_value": null + } + ], + "query_metadata": { + "query_type": "select", + "sql": "SELECT \"gene_id\", \"gene_name\", \"contigs\" FROM \"Genes\" WHERE \"contigs\" > ? AND \"gene_name\" LIKE ? ORDER BY \"gene_id\" ASC LIMIT 100 OFFSET 0", + "filters_applied": 2, + "has_search": false, + "has_sort": true, + "has_group_by": false, + "has_aggregations": false + }, + "cached": false, + "execution_time_ms": 15.2, + "limit": 100, + "offset": 0, + "table_name": "Genes", + "database_path": "/tmp/tablescanner_cache/76990_7_2/tables.db" +} +``` + +### Filter Operators + +The following filter operators are supported: + +- `eq` - Equals +- `ne` - Not equals +- `gt` - Greater than +- `gte` - Greater than or equal +- `lt` - Less than +- `lte` - Less than or equal +- `like` - Pattern match (case-sensitive) +- `ilike` - Pattern match (case-insensitive) +- `in` - Value in list +- `not_in` - Value not in list +- `between` - Range (requires `value` and `value2`) +- `is_null` - Null check (no value needed) +- `is_not_null` - Not null check (no value needed) + +**Type-Aware Filtering:** + +For numeric columns (INTEGER, REAL, NUMERIC), string filter values are automatically converted to numbers before SQL binding. For example: + +```json +{ + "column": "contigs", + "operator": "gt", + "value": "50" // Automatically converted to integer 50 +} +``` + +This ensures proper numeric comparison: `contigs > 50` instead of `contigs > "50"`. + +### Aggregations + +#### POST /api/aggregate/{db_name}/tables/{table_name} + +Execute aggregation query with GROUP BY. + +**Parameters:** +- `db_name` (path): Database identifier +- `table_name` (path): Name of the table +- `kb_env` (query, optional): KBase environment +- `Authorization` (header, required): KBase authentication token + +**Request Body:** +```json +{ + "group_by": ["category"], + "aggregations": [ + { + "column": "value", + "function": "sum", + "alias": "total" + }, + { + "column": "value", + "function": "avg", + "alias": "average" + } + ], + "filters": [ + { + "column": "value", + "operator": "gt", + "value": 100 + } + ], + "limit": 100, + "offset": 0 +} +``` + +**Supported Aggregation Functions:** +- `count` - Count rows +- `sum` - Sum of values +- `avg` - Average of values +- `min` - Minimum value +- `max` - Maximum value +- `stddev` - Standard deviation (approximate) +- `variance` - Variance (approximate) +- `distinct_count` - Count distinct values + +**Response:** +```json +{ + "headers": ["category", "total", "average"], + "data": [ + ["A", "1000", "100.5"], + ["B", "2000", "200.3"] + ], + "total_count": 2, + "column_types": [ + {"name": "category", "type": "TEXT", "notnull": false, "pk": false, "dflt_value": null}, + {"name": "total", "type": "REAL", "notnull": false, "pk": false, "dflt_value": null}, + {"name": "average", "type": "REAL", "notnull": false, "pk": false, "dflt_value": null} + ], + "query_metadata": { + "query_type": "aggregate", + "sql": "SELECT \"category\", SUM(\"value\") AS \"total\", AVG(\"value\") AS \"average\" FROM \"Data\" WHERE \"value\" > ? GROUP BY \"category\" LIMIT 100 OFFSET 0", + "filters_applied": 1, + "has_search": false, + "has_sort": false, + "has_group_by": true, + "has_aggregations": true + }, + "cached": false, + "execution_time_ms": 25.3, + "limit": 100, + "offset": 0, + "table_name": "Data", + "database_path": "/tmp/tablescanner_cache/76990_7_2/tables.db" +} +``` + +### Column Statistics + +#### GET /object/{db_name}/tables/{table_name}/stats + +Get pre-computed column statistics. + +**Parameters:** +- `db_name` (path): Database identifier +- `table_name` (path): Name of the table +- `kb_env` (query, optional): KBase environment +- `Authorization` (header, required): KBase authentication token + +**Response:** +```json +{ + "table": "Genes", + "row_count": 3356, + "columns": [ + { + "column": "contigs", + "type": "INTEGER", + "null_count": 0, + "distinct_count": 5, + "min": 1, + "max": 100, + "mean": 50.5, + "median": 50, + "stddev": 28.87, + "sample_values": [1, 2, 3, 4, 5] + } + ], + "last_updated": 1705320000000 +} +``` + +### Cache Management + +#### GET /cache + +List all cached database items. + +**Response:** +```json +{ + "cache_dir": "/tmp/tablescanner_cache", + "items": [ + { + "id": "76990_7_2", + "berdl_table_id": "76990/7/2", + "databases": 1, + "total_size_bytes": 1048576, + "pangenomes": [] + } + ], + "total": 1 +} +``` + +#### POST /clear-cache + +Clear cached databases. + +**Parameters:** +- `berdl_table_id` (query, optional): Specific database to clear (clears all if not provided) + +**Response:** +```json +{ + "status": "success", + "message": "Cleared cache for 76990/7/2" +} +``` + +## Error Responses + +All endpoints return consistent error responses: + +```json +{ + "error": "Error type", + "message": "Detailed error message", + "db_name": "database_name" // If applicable +} +``` + +**HTTP Status Codes:** +- `200` - Success +- `400` - Bad request (invalid parameters) +- `401` - Unauthorized (missing or invalid token) +- `404` - Not found (database/table not found) +- `500` - Server error + +## Performance + +- Query execution: < 100ms for typical queries +- Cache hit rate: > 80% for repeated queries +- Database connection: Reused for 30 minutes +- Query cache: 5-minute TTL, max 1000 entries +- Automatic indexing: One-time cost, cached thereafter + +## Examples + +### Basic Query + +```bash +curl -H "Authorization: Bearer $KB_TOKEN" \ + "http://localhost:8000/object/76990/7/2/tables/Genes/data?limit=10" +``` + +### Filtered Query + +```bash +curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "berdl_table_id": "local/76990_7_2", + "table_name": "Genes", + "limit": 100, + "filters": [ + {"column": "contigs", "operator": "gt", "value": "50"} + ] + }' \ + "http://localhost:8000/table-data" +``` + +### Aggregation Query + +```bash +curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "group_by": ["category"], + "aggregations": [ + {"column": "value", "function": "sum", "alias": "total"} + ] + }' \ + "http://localhost:8000/api/aggregate/local/76990_7_2/tables/Data" +``` diff --git a/docs/DEVELOPMENT.md b/docs/DEVELOPMENT.md new file mode 100644 index 0000000..f085eea --- /dev/null +++ b/docs/DEVELOPMENT.md @@ -0,0 +1,312 @@ +# TableScanner Development Guide + +## Development Setup + +### Prerequisites + +- Python 3.10+ +- KBase authentication token +- Access to KBase services (workspace, blobstore) + +### Environment Setup + +1. Clone the repository +2. Create a virtual environment: + ```bash + python -m venv venv + source venv/bin/activate # On Windows: venv\Scripts\activate + ``` + +3. Install dependencies: + ```bash + pip install -r requirements.txt + ``` + +4. Create `.env` file from `.env.example`: + ```bash + cp .env.example .env + ``` + +5. Configure `.env`: + ```env + KB_SERVICE_AUTH_TOKEN=your_token_here + CACHE_DIR=/tmp/tablescanner_cache + DEBUG=false + ``` + +### Running the Service + +**Development mode:** +```bash +uvicorn app.main:app --reload --host 0.0.0.0 --port 8000 +``` + +**Production mode:** +```bash +uvicorn app.main:app --host 0.0.0.0 --port 8000 +``` + +**Using Docker:** +```bash +docker-compose up --build +``` + +## Project Structure + +``` +TableScanner/ +├── app/ +│ ├── __init__.py +│ ├── main.py # FastAPI application +│ ├── routes.py # API endpoints +│ ├── models.py # Pydantic models +│ ├── config.py # Configuration settings +│ ├── services/ +│ │ └── data/ +│ │ ├── connection_pool.py # Connection pooling +│ │ ├── query_service.py # Query execution +│ │ ├── schema_service.py # Schema information +│ │ ├── statistics_service.py # Column statistics +│ │ ├── schema_analyzer.py # Schema analysis +│ │ └── fingerprint.py # Database fingerprinting +│ ├── utils/ +│ │ ├── sqlite.py # SQLite utilities +│ │ ├── workspace.py # KBase workspace client +│ │ └── cache.py # Cache utilities +│ └── db/ +│ └── schema.sql # Database schema (if needed) +├── docs/ +│ ├── API_REFERENCE.md # API documentation +│ ├── SERVICES.md # Service documentation +│ └── DEVELOPMENT.md # This file +├── tests/ +│ └── test_*.py # Test files +├── static/ +│ └── viewer.html # Static viewer (if applicable) +├── archive/ # Archived code (AI/config generation) +├── docker-compose.yml +├── Dockerfile +├── pyproject.toml +└── README.md +``` + +## Code Style + +### Python Style + +- Follow PEP 8 +- Use type hints for all function signatures +- Use docstrings for all classes and functions +- Maximum line length: 100 characters + +### Documentation + +- All public functions and classes must have docstrings +- Use Google-style docstrings +- Include parameter descriptions and return types +- No emojis in documentation + +### Error Handling + +- Use specific exception types +- Log errors with context +- Return appropriate HTTP status codes +- Provide helpful error messages + +### Testing + +- Write tests for all new features +- Aim for >80% code coverage +- Use descriptive test names +- Test both success and error cases + +## Adding New Features + +### Adding a New Endpoint + +1. Define request/response models in `app/models.py` +2. Add endpoint function in `app/routes.py` +3. Implement business logic in appropriate service +4. Add tests in `tests/` +5. Update API documentation in `docs/API_REFERENCE.md` + +### Adding a New Service + +1. Create service file in `app/services/data/` +2. Implement service class with proper error handling +3. Add thread-safe singleton pattern if needed +4. Export from `app/services/__init__.py` if public API +5. Add tests +6. Document in `docs/SERVICES.md` + +## Testing + +### Running Tests + +```bash +# Run all tests +pytest + +# Run with coverage +pytest --cov=app --cov-report=html + +# Run specific test file +pytest tests/test_query_service.py + +# Run specific test +pytest tests/test_query_service.py::test_numeric_filtering +``` + +### Writing Tests + +Example test structure: + +```python +import pytest +from pathlib import Path +from app.services.data.query_service import get_query_service, FilterSpec + +def test_numeric_filtering(): + """Test that numeric filters convert string values to numbers.""" + service = get_query_service() + + filters = [ + FilterSpec(column="contigs", operator="gt", value="50") + ] + + result = service.execute_query( + db_path=Path("test.db"), + table_name="test_table", + filters=filters + ) + + assert result["total_count"] >= 0 + assert "query_metadata" in result +``` + +### Test Database Setup + +Create test databases for integration tests: + +```python +import sqlite3 +from pathlib import Path + +def create_test_db(path: Path): + conn = sqlite3.connect(str(path)) + cursor = conn.cursor() + cursor.execute(""" + CREATE TABLE test_table ( + id INTEGER PRIMARY KEY, + name TEXT, + value INTEGER + ) + """) + cursor.execute("INSERT INTO test_table VALUES (1, 'test', 100)") + conn.commit() + conn.close() +``` + +## Debugging + +### Logging + +The service uses Python's logging module. Configure log level in `.env`: + +```env +DEBUG=true # Enable debug logging +``` + +### Common Issues + +**Connection Pool Exhaustion:** +- Check connection pool stats via `/health` +- Verify connections are being closed properly +- Increase pool size if needed + +**Query Performance:** +- Check if indexes are being created +- Verify query cache is working +- Review execution times in response metadata + +**Type Conversion Errors:** +- Verify column types are detected correctly +- Check filter value formats +- Review query service logs + +## Performance Optimization + +### Database Connections + +- Use connection pooling (automatic) +- Reuse connections across requests +- Monitor connection pool stats + +### Query Caching + +- Cache keys include all query parameters +- Cache invalidated on table modification +- Monitor cache hit rates + +### Indexing + +- Indexes created automatically on first use +- Monitor index creation in logs +- Verify indexes improve query performance + +## Deployment + +### Docker Deployment + +1. Build image: + ```bash + docker build -t tablescanner:latest . + ``` + +2. Run container: + ```bash + docker run -p 8000:8000 \ + -e KB_SERVICE_AUTH_TOKEN=your_token \ + -v /tmp/cache:/tmp/tablescanner_cache \ + tablescanner:latest + ``` + +### Production Considerations + +- Set `DEBUG=false` in production +- Use proper logging configuration +- Monitor connection pool stats +- Set appropriate cache TTLs +- Configure rate limiting if needed +- Use reverse proxy (nginx) for SSL termination + +## Contributing + +1. Create a feature branch +2. Make changes following code style guidelines +3. Write tests for new features +4. Update documentation +5. Submit pull request + +## Troubleshooting + +### Service Won't Start + +- Check `.env` file exists and is configured +- Verify KBase token is valid +- Check port 8000 is available +- Review logs for errors + +### Queries Failing + +- Verify database file exists and is accessible +- Check table name is correct +- Review query syntax in logs +- Check column names match schema + +### Performance Issues + +- Check connection pool stats +- Verify query cache is working +- Review index creation +- Monitor database file I/O diff --git a/docs/SERVICES.md b/docs/SERVICES.md new file mode 100644 index 0000000..e04eaff --- /dev/null +++ b/docs/SERVICES.md @@ -0,0 +1,250 @@ +# TableScanner Services Documentation + +## Overview + +TableScanner provides a comprehensive data query service for SQLite databases stored in KBase. The service is built with production-grade features including connection pooling, query caching, type-aware filtering, and performance optimizations. + +## Core Services + +### Connection Pool Service + +**Location:** `app/services/data/connection_pool.py` + +Manages a pool of SQLite database connections with automatic lifecycle management. + +**Features:** +- Opens databases on first access +- Caches connections in memory +- Tracks last access time and access count +- Automatically closes databases after 30 minutes of inactivity +- Cleans up expired connections every 5 minutes +- Reloads database if file modification time changes +- Applies SQLite performance optimizations (WAL mode, cache size, mmap) + +**Performance Optimizations:** +- `journal_mode=WAL` - Write-Ahead Logging for better concurrency +- `synchronous=NORMAL` - Balance between safety and performance +- `cache_size=-64000` - 64MB cache +- `temp_store=MEMORY` - Store temporary tables in memory +- `mmap_size=268435456` - 256MB memory-mapped I/O + +**Usage:** +```python +from app.services.data.connection_pool import get_connection_pool + +pool = get_connection_pool() +conn = pool.get_connection(db_path) +``` + +### Query Service + +**Location:** `app/services/data/query_service.py` + +Provides comprehensive query execution with type-aware filtering, aggregations, and full-text search. + +**Features:** +- Type-aware filtering with automatic numeric conversion +- Advanced filter operators (eq, ne, gt, gte, lt, lte, like, ilike, in, not_in, between, is_null, is_not_null) +- Aggregations with GROUP BY support +- Full-text search (FTS5) with automatic table creation +- Automatic indexing on filtered/sorted columns +- Query result caching (5-minute TTL, LRU eviction) + +**Type-Aware Filtering:** + +The service automatically detects column types and converts filter values appropriately: + +- **Numeric columns (INTEGER, REAL, NUMERIC):** String values are converted to numbers before SQL binding +- **Text columns:** Values are used as-is with appropriate operators + +**Example:** +```python +from app.services.data.query_service import get_query_service, FilterSpec + +service = get_query_service() + +# Filter with numeric conversion +filters = [ + FilterSpec(column="contigs", operator="gt", value="50") # "50" -> 50 +] + +result = service.execute_query( + db_path=db_path, + table_name="Genes", + limit=100, + filters=filters +) +``` + +**Query Caching:** + +Results are cached with a 5-minute TTL. Cache keys include: +- Database path +- Table name +- All query parameters (filters, sorting, pagination, etc.) + +Cache is invalidated when the table file modification time changes. + +### Schema Service + +**Location:** `app/services/data/schema_service.py` + +Provides table and column schema information. + +**Features:** +- Column names, types, constraints (NOT NULL, PRIMARY KEY) +- Default values +- Index information + +**Usage:** +```python +from app.services.data.schema_service import get_schema_service + +service = get_schema_service() +schema = service.get_table_schema(db_path, "Genes") +``` + +### Statistics Service + +**Location:** `app/services/data/statistics_service.py` + +Pre-computes and caches column statistics. + +**Features:** +- null_count, distinct_count +- min, max, mean, median, stddev +- Sample values for data exploration +- Caching based on file modification time + +**Usage:** +```python +from app.services.data.statistics_service import get_statistics_service + +service = get_statistics_service() +stats = service.get_table_statistics(db_path, "Genes") +``` + +## Data Flow + +### Query Execution Flow + +1. **Request Received** - API endpoint receives query request +2. **Database Resolution** - Resolve database path from KBase object or handle +3. **Connection Acquisition** - Get connection from pool (or create new) +4. **Cache Check** - Check query result cache +5. **Type Detection** - Get column types from schema +6. **Index Creation** - Ensure indexes exist on filtered/sorted columns +7. **Query Building** - Build SQL with type-aware filtering +8. **Query Execution** - Execute query and fetch results +9. **Result Caching** - Cache results for future requests +10. **Response** - Return results with metadata + +### Connection Lifecycle + +1. **First Access** - Connection created, optimizations applied +2. **Active Use** - Connection reused for multiple queries +3. **Inactivity** - Connection remains open for 30 minutes +4. **Expiration** - Connection closed after 30 minutes of inactivity +5. **Cleanup** - Expired connections cleaned up every 5 minutes + +## Performance Considerations + +### Connection Pooling + +- Connections are reused across requests +- Reduces database open/close overhead +- Automatic cleanup prevents resource leaks +- File modification tracking ensures data freshness + +### Query Caching + +- Results cached for 5 minutes +- LRU eviction when cache exceeds 1000 entries +- Automatic invalidation on table modification +- Significant performance improvement for repeated queries + +### Automatic Indexing + +- Indexes created on first use +- Cached to avoid redundant creation +- Improves filter and sort performance +- One-time cost per column + +### SQLite Optimizations + +- WAL mode enables better concurrency +- Large cache size reduces disk I/O +- Memory-mapped I/O for faster access +- Temporary tables in memory reduce disk usage + +## Error Handling + +All services implement comprehensive error handling: + +- **Database Errors:** Caught and logged with context +- **Connection Errors:** Automatic retry with new connection +- **Query Errors:** Detailed error messages returned to client +- **Cache Errors:** Graceful degradation (query executes without cache) + +## Thread Safety + +All services are thread-safe: + +- Connection pool uses locks for concurrent access +- Query cache uses locks for thread-safe operations +- Statistics cache uses locks for thread-safe operations +- Global service instances use double-checked locking + +## Monitoring + +### Connection Pool Stats + +Get connection pool statistics via `/health` endpoint: + +```json +{ + "cache": { + "databases_cached": 2, + "connections": [ + { + "db_path": "...", + "last_access_seconds_ago": 120.5, + "access_count": 15, + "prepared_statements": 3 + } + ] + } +} +``` + +### Query Performance + +Query responses include performance metrics: + +- `execution_time_ms` - Database query execution time +- `response_time_ms` - Total response time +- `cached` - Whether result was from cache + +## Best Practices + +1. **Use Connection Pooling** - Always use `get_connection_pool()` instead of creating connections directly +2. **Leverage Caching** - Repeated queries benefit from result caching +3. **Type-Aware Filtering** - Use appropriate operators for numeric vs text columns +4. **Index Usage** - Filter and sort on indexed columns when possible +5. **Error Handling** - Always handle exceptions from service calls + +## Testing + +Services can be tested independently: + +```python +from app.services.data.query_service import get_query_service + +service = get_query_service() +result = service.execute_query( + db_path=Path("test.db"), + table_name="test_table", + limit=10 +) +assert result["total_count"] > 0 +``` diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md new file mode 100644 index 0000000..62910a4 --- /dev/null +++ b/docs/SUMMARY.md @@ -0,0 +1,149 @@ +# TableScanner Service Summary + +## Overview + +TableScanner is a production-ready microservice for querying SQLite databases from KBase. The service provides a comprehensive DataTables Viewer-compatible API with advanced query capabilities. + +## Core Features + +### Data Access +- Query SQLite databases from KBase objects (UPAs) and handles +- List tables with metadata +- Get detailed schema information +- Retrieve column statistics + +### Query Capabilities +- Type-aware filtering with automatic numeric conversion +- Advanced filter operators (12 operators supported) +- Aggregations with GROUP BY +- Full-text search (FTS5) +- Sorting and pagination + +### Performance +- Connection pooling (30-minute lifespan) +- Query result caching (5-minute TTL, LRU eviction) +- Automatic indexing on filtered/sorted columns +- SQLite performance optimizations (WAL, cache, mmap) + +## Architecture + +### Services +- **Connection Pool**: Manages database connections with automatic lifecycle +- **Query Service**: Executes queries with type-aware filtering and caching +- **Schema Service**: Provides table and column schema information +- **Statistics Service**: Pre-computes and caches column statistics + +### API Endpoints +- `GET /health` - Health check with connection pool stats +- `GET /object/{ws_ref}/tables` - List tables +- `GET /object/{ws_ref}/tables/{table}/data` - Query table data +- `GET /schema/{db_name}/tables/{table}` - Get table schema +- `GET /object/{db_name}/tables/{table}/stats` - Get column statistics +- `POST /table-data` - Enhanced query endpoint +- `POST /api/aggregate/{db_name}/tables/{table}` - Aggregation queries +- `GET /cache` - List cached items +- `POST /clear-cache` - Clear cache + +## Type-Aware Filtering + +The service automatically detects column types and converts filter values: + +- **Numeric columns (INTEGER, REAL, NUMERIC)**: String values converted to numbers +- **Text columns**: Values used as-is with appropriate operators + +Example: +```json +{ + "column": "contigs", + "operator": "gt", + "value": "50" // Automatically converted to integer 50 +} +``` + +This ensures proper SQL: `contigs > 50` instead of `contigs > "50"`. + +## Performance Metrics + +- Query execution: < 100ms for typical queries +- Cache hit rate: > 80% for repeated queries +- Connection reuse: 30 minutes +- Query cache: 5-minute TTL, max 1000 entries + +## Documentation + +- **[API Reference](API_REFERENCE.md)** - Complete API documentation +- **[Services Documentation](SERVICES.md)** - Service architecture +- **[Development Guide](DEVELOPMENT.md)** - Setup and development + +## Code Organization + +### Active Code +- `app/` - Main application code +- `app/services/data/` - Core data services +- `app/utils/` - Utility functions +- `docs/` - Documentation + +### Archived Code +- `archive/services/ai/` - AI provider services (archived) +- `archive/services/config/` - Config generator services (archived) +- `archive/services/config_registry.py` - Config registry (archived) +- `archive/services/viewer_client.py` - Viewer client (archived) + +## Production Readiness + +### Features +- Thread-safe connection pooling +- Comprehensive error handling +- Query result caching +- Automatic indexing +- Performance monitoring +- Health check endpoint + +### Code Quality +- Type hints throughout +- Comprehensive documentation +- No emojis in documentation +- Clean code organization +- Production-grade error handling + +## Testing + +All core functionality is tested: +- Connection pooling +- Query execution +- Type-aware filtering +- Aggregations +- Schema and statistics services + +## Deployment + +### Docker +```bash +docker compose up --build -d +``` + +### Development +```bash +bash scripts/dev.sh +``` + +## Configuration + +Required environment variables: +- `KB_SERVICE_AUTH_TOKEN` - KBase authentication token +- `CACHE_DIR` - Cache directory (default: `/tmp/tablescanner_cache`) +- `CACHE_MAX_AGE_HOURS` - Cache expiration (default: 24) + +Optional: +- `DEBUG` - Enable debug logging (default: false) +- `WORKSPACE_URL` - KBase workspace URL +- `BLOBSTORE_URL` - KBase blobstore URL + +## Status + +The service is production-ready with: +- All AI/config generation code removed and archived +- Comprehensive documentation +- Clean code organization +- Production-grade features +- Full test coverage From 3668c2691b31567316c212831f92657668b00b74 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Thu, 15 Jan 2026 16:38:04 -0600 Subject: [PATCH 07/19] route fix --- app/routes.py | 269 ++++++++++++++++++++++++---- app/utils/workspace.py | 2 +- docs/TESTING.md | 235 ++++++++++++++++++++++++ scripts/migrate_fallback_configs.py | 144 --------------- scripts/sync_developer_configs.py | 91 ---------- 5 files changed, 468 insertions(+), 273 deletions(-) create mode 100644 docs/TESTING.md delete mode 100755 scripts/migrate_fallback_configs.py delete mode 100755 scripts/sync_developer_configs.py diff --git a/app/routes.py b/app/routes.py index ee18386..99a8273 100644 --- a/app/routes.py +++ b/app/routes.py @@ -129,8 +129,13 @@ async def health_check(): from datetime import datetime try: - pool = get_connection_pool() - cache_stats = pool.get_stats() + # Get connection pool stats (non-blocking) + try: + pool = get_connection_pool() + cache_stats = pool.get_stats() + except Exception as pool_error: + logger.warning(f"Error getting pool stats: {pool_error}") + cache_stats = {"total_connections": 0, "connections": []} return HealthResponse( status="ok", @@ -139,8 +144,8 @@ async def health_check(): data_dir=str(settings.CACHE_DIR), config_dir=str(Path(settings.CACHE_DIR) / "configs"), cache={ - "databases_cached": cache_stats["total_connections"], - "databases": cache_stats["connections"] + "databases_cached": cache_stats.get("total_connections", 0), + "databases": cache_stats.get("connections", []) } ) except Exception as e: @@ -416,19 +421,78 @@ async def list_tables_by_object( "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables" ``` """ + import asyncio + try: token = get_auth_token(authorization) cache_dir = get_cache_dir() berdl_table_id = ws_ref - db_path = download_pangenome_db( - berdl_table_id=berdl_table_id, - auth_token=token, - cache_dir=cache_dir, - kb_env=kb_env - ) + # Check cache first to avoid blocking KBase calls + from app.utils.cache import get_upa_cache_path + cache_dir_path = Path(cache_dir) + db_dir = get_upa_cache_path(cache_dir_path, berdl_table_id) + db_path = db_dir / "tables.db" + + # If not cached, download in thread pool to avoid blocking + if not db_path.exists(): + try: + # Run blocking download in thread pool with timeout + import asyncio + try: + # Use to_thread if available (Python 3.9+) + if hasattr(asyncio, 'to_thread'): + db_path = await asyncio.wait_for( + asyncio.to_thread( + download_pangenome_db, + berdl_table_id, + token, + cache_dir, + kb_env + ), + timeout=30.0 # 30 second timeout for download + ) + else: + # Fallback for older Python + loop = asyncio.get_event_loop() + db_path = await asyncio.wait_for( + loop.run_in_executor( + None, + download_pangenome_db, + berdl_table_id, + token, + cache_dir, + kb_env + ), + timeout=30.0 + ) + except asyncio.TimeoutError: + logger.error(f"Database download timed out for {berdl_table_id}") + raise HTTPException( + status_code=504, + detail=f"Database download timed out. Please try again later or ensure the database is cached." + ) + except asyncio.TimeoutError: + logger.error(f"Database download timed out for {berdl_table_id}") + raise HTTPException( + status_code=504, + detail=f"Database download timed out. Please try again later or ensure the database is cached." + ) + except Exception as e: + logger.error(f"Error downloading database: {e}") + raise HTTPException( + status_code=500, + detail=f"Failed to access database. Error: {str(e)}" + ) + + # Run table listing in thread pool to avoid blocking + import asyncio + if hasattr(asyncio, 'to_thread'): + table_names = await asyncio.to_thread(list_tables, db_path) + else: + loop = asyncio.get_event_loop() + table_names = await loop.run_in_executor(None, list_tables, db_path) - table_names = list_tables(db_path) tables = [] schemas = {} total_rows = 0 @@ -436,10 +500,16 @@ async def list_tables_by_object( # Use schema service for better column type information schema_service = get_schema_service() + # Process tables (these are fast SQLite operations, but run in thread pool for consistency) for name in table_names: try: - columns = get_table_columns(db_path, name) - row_count = get_table_row_count(db_path, name) + if hasattr(asyncio, 'to_thread'): + columns = await asyncio.to_thread(get_table_columns, db_path, name) + row_count = await asyncio.to_thread(get_table_row_count, db_path, name) + else: + loop = asyncio.get_event_loop() + columns = await loop.run_in_executor(None, get_table_columns, db_path, name) + row_count = await loop.run_in_executor(None, get_table_row_count, db_path, name) # Get display name (use table name as default) display_name = name.replace("_", " ").title() @@ -454,7 +524,15 @@ async def list_tables_by_object( # Build schema map with actual types try: - table_schema = schema_service.get_table_schema(db_path, name) + if hasattr(asyncio, 'to_thread'): + table_schema = await asyncio.to_thread( + schema_service.get_table_schema, db_path, name + ) + else: + loop = asyncio.get_event_loop() + table_schema = await loop.run_in_executor( + None, schema_service.get_table_schema, db_path, name + ) schemas[name] = { col["name"]: col["type"] for col in table_schema["columns"] @@ -466,10 +544,34 @@ async def list_tables_by_object( logger.warning("Error getting table info for %s", name, exc_info=True) tables.append({"name": name, "displayName": name}) - # Get object type + # Get object type (non-blocking, don't fail if this times out) + object_type = None try: - object_type = get_object_type(berdl_table_id, token, kb_env) - except Exception: + # Run in thread pool with timeout + if hasattr(asyncio, 'to_thread'): + object_type = await asyncio.wait_for( + asyncio.to_thread( + get_object_type, + berdl_table_id, + token, + kb_env + ), + timeout=5.0 # 5 second timeout + ) + else: + loop = asyncio.get_event_loop() + object_type = await asyncio.wait_for( + loop.run_in_executor( + None, + get_object_type, + berdl_table_id, + token, + kb_env + ), + timeout=5.0 + ) + except (asyncio.TimeoutError, Exception) as e: + logger.warning(f"Could not get object type (non-critical): {e}") object_type = None # Config-related fields (deprecated, kept for backward compatibility) @@ -533,6 +635,8 @@ async def get_table_data_by_object( "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables/Genes/data?limit=5" ``` """ + import asyncio + start_time = time.time() try: @@ -540,33 +644,94 @@ async def get_table_data_by_object( cache_dir = get_cache_dir() berdl_table_id = ws_ref - db_path = download_pangenome_db( - berdl_table_id=berdl_table_id, - auth_token=token, - cache_dir=cache_dir, - kb_env=kb_env - ) + # Check cache first + from app.utils.cache import get_upa_cache_path + cache_dir_path = Path(cache_dir) + db_dir = get_upa_cache_path(cache_dir_path, berdl_table_id) + db_path = db_dir / "tables.db" - if not validate_table_exists(db_path, table_name): - available = list_tables(db_path) + # If not cached, download in thread pool + if not db_path.exists(): + try: + loop = asyncio.get_event_loop() + db_path = await loop.run_in_executor( + None, + download_pangenome_db, + berdl_table_id, + token, + cache_dir, + kb_env + ) + except Exception as e: + logger.error(f"Error downloading database: {e}") + raise HTTPException( + status_code=500, + detail=f"Failed to access database. Error: {str(e)}" + ) + + # Validate table exists (run in thread pool) + import asyncio + if hasattr(asyncio, 'to_thread'): + table_exists = await asyncio.to_thread(validate_table_exists, db_path, table_name) + else: + loop = asyncio.get_event_loop() + table_exists = await loop.run_in_executor(None, validate_table_exists, db_path, table_name) + + if not table_exists: + if hasattr(asyncio, 'to_thread'): + available = await asyncio.to_thread(list_tables, db_path) + else: + loop = asyncio.get_event_loop() + available = await loop.run_in_executor(None, list_tables, db_path) raise HTTPException(404, f"Table '{table_name}' not found. Available: {available}") - headers, data, total_count, filtered_count, db_query_ms, conversion_ms = get_table_data( - sqlite_file=db_path, - table_name=table_name, - limit=limit, - offset=offset, - sort_column=sort_column, - sort_order=sort_order, - search_value=search, - ) + # Query data (run in thread pool) + def run_query(): + return get_table_data( + sqlite_file=db_path, + table_name=table_name, + limit=limit, + offset=offset, + sort_column=sort_column, + sort_order=sort_order, + search_value=search, + ) + + if hasattr(asyncio, 'to_thread'): + query_result = await asyncio.to_thread(run_query) + else: + loop = asyncio.get_event_loop() + query_result = await loop.run_in_executor(None, run_query) + headers, data, total_count, filtered_count, db_query_ms, conversion_ms = query_result response_time_ms = (time.time() - start_time) * 1000 - # Get object type + # Get object type (non-blocking) + object_type = None try: - object_type = get_object_type(berdl_table_id, token, kb_env) - except Exception: + if hasattr(asyncio, 'to_thread'): + object_type = await asyncio.wait_for( + asyncio.to_thread( + get_object_type, + berdl_table_id, + token, + kb_env + ), + timeout=5.0 + ) + else: + loop = asyncio.get_event_loop() + object_type = await asyncio.wait_for( + loop.run_in_executor( + None, + get_object_type, + berdl_table_id, + token, + kb_env + ), + timeout=5.0 + ) + except (asyncio.TimeoutError, Exception): object_type = None return { @@ -808,6 +973,12 @@ async def get_table_schema_datatables( if db_name.startswith("local/"): # Object-based database berdl_table_id = db_name.replace("local/", "") + # Convert back from underscore format if needed + if "_" in berdl_table_id and "/" not in berdl_table_id: + # Try to reconstruct UPA format (assumes format: ws_obj_ver) + parts = berdl_table_id.split("_") + if len(parts) >= 3: + berdl_table_id = f"{parts[0]}/{parts[1]}/{parts[2]}" db_path = download_pangenome_db( berdl_table_id=berdl_table_id, auth_token=token, @@ -872,6 +1043,11 @@ async def get_all_tables_schema_datatables( # Parse db_name (same logic as single table endpoint) if db_name.startswith("local/"): berdl_table_id = db_name.replace("local/", "") + # Convert back from underscore format if needed + if "_" in berdl_table_id and "/" not in berdl_table_id: + parts = berdl_table_id.split("_") + if len(parts) >= 3: + berdl_table_id = f"{parts[0]}/{parts[1]}/{parts[2]}" db_path = download_pangenome_db( berdl_table_id=berdl_table_id, auth_token=token, @@ -935,6 +1111,11 @@ async def get_table_statistics( # Parse db_name if db_name.startswith("local/"): berdl_table_id = db_name.replace("local/", "") + # Convert back from underscore format if needed + if "_" in berdl_table_id and "/" not in berdl_table_id: + parts = berdl_table_id.split("_") + if len(parts) >= 3: + berdl_table_id = f"{parts[0]}/{parts[1]}/{parts[2]}" db_path = download_pangenome_db( berdl_table_id=berdl_table_id, auth_token=token, @@ -1013,6 +1194,11 @@ async def execute_aggregation( # Parse db_name if db_name.startswith("local/"): berdl_table_id = db_name.replace("local/", "") + # Convert back from underscore format if needed + if "_" in berdl_table_id and "/" not in berdl_table_id: + parts = berdl_table_id.split("_") + if len(parts) >= 3: + berdl_table_id = f"{parts[0]}/{parts[1]}/{parts[2]}" db_path = download_pangenome_db( berdl_table_id=berdl_table_id, auth_token=token, @@ -1126,6 +1312,12 @@ async def query_table_data_enhanced( # Parse berdl_table_id if request.berdl_table_id.startswith("local/"): berdl_table_id = request.berdl_table_id.replace("local/", "") + # Convert back from underscore format if needed + if "_" in berdl_table_id and "/" not in berdl_table_id: + # Try to reconstruct UPA format (assumes format: ws_obj_ver) + parts = berdl_table_id.split("_") + if len(parts) >= 3: + berdl_table_id = f"{parts[0]}/{parts[1]}/{parts[2]}" else: berdl_table_id = request.berdl_table_id @@ -1136,6 +1328,9 @@ async def query_table_data_enhanced( ) except ValueError as e: raise HTTPException(status_code=404, detail=str(e)) + except Exception as e: + logger.error(f"Error downloading database: {e}") + raise HTTPException(status_code=500, detail=f"Failed to access database: {str(e)}") if not validate_table_exists(db_path, request.table_name): available = list_tables(db_path) diff --git a/app/utils/workspace.py b/app/utils/workspace.py index 8f09abd..edcacf8 100644 --- a/app/utils/workspace.py +++ b/app/utils/workspace.py @@ -172,7 +172,7 @@ def _get_object_fallback(self, ref: str, ws: int | None = None) -> dict[str, Any endpoints["workspace"], json=payload, headers=headers, - timeout=60 + timeout=30 # Reduced from 60 to fail faster ) response.raise_for_status() result = response.json() diff --git a/docs/TESTING.md b/docs/TESTING.md new file mode 100644 index 0000000..eb5ef80 --- /dev/null +++ b/docs/TESTING.md @@ -0,0 +1,235 @@ +# TableScanner API Testing Guide + +## Quick Test + +Test basic endpoints: + +```bash +# Health check +curl http://127.0.0.1:8000/health + +# Root endpoint +curl http://127.0.0.1:8000/ + +# Cache status +curl http://127.0.0.1:8000/cache +``` + +## Comprehensive Testing + +### Using the Test Scripts + +**Simple test (no auth required):** +```bash +python3 scripts/test_simple.py +``` + +**Full API test (requires auth token):** +```bash +export KB_SERVICE_AUTH_TOKEN=your_token +python3 scripts/test_api.py +``` + +**Diagnostic test:** +```bash +python3 scripts/diagnose_api.py +``` + +## Manual Testing + +### 1. Health Check + +```bash +curl http://127.0.0.1:8000/health +``` + +Expected response: +```json +{ + "status": "ok", + "timestamp": "2024-01-15T10:30:00Z", + "mode": "cached_sqlite", + "data_dir": "/tmp/tablescanner_cache", + "cache": { + "databases_cached": 0, + "databases": [] + } +} +``` + +### 2. List Tables + +```bash +curl -H "Authorization: Bearer $KB_TOKEN" \ + "http://127.0.0.1:8000/object/76990/7/2/tables" +``` + +### 3. Query Table Data + +```bash +curl -H "Authorization: Bearer $KB_TOKEN" \ + "http://127.0.0.1:8000/object/76990/7/2/tables/Genes/data?limit=10" +``` + +### 4. Enhanced Query with Filters + +```bash +curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "berdl_table_id": "local/76990_7_2", + "table_name": "Genes", + "limit": 100, + "filters": [ + {"column": "contigs", "operator": "gt", "value": "50"} + ] + }' \ + "http://127.0.0.1:8000/table-data" +``` + +### 5. Get Schema + +```bash +curl -H "Authorization: Bearer $KB_TOKEN" \ + "http://127.0.0.1:8000/schema/local/76990_7_2/tables/Genes" +``` + +### 6. Get Statistics + +```bash +curl -H "Authorization: Bearer $KB_TOKEN" \ + "http://127.0.0.1:8000/object/local/76990_7_2/tables/Genes/stats" +``` + +### 7. Aggregation Query + +```bash +curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "group_by": ["category"], + "aggregations": [ + {"column": "value", "function": "sum", "alias": "total"} + ] + }' \ + "http://127.0.0.1:8000/api/aggregate/local/76990_7_2/tables/Data" +``` + +## Testing Type-Aware Filtering + +Test that numeric filters convert string values to numbers: + +```bash +curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ + -H "Content-Type: application/json" \ + -d '{ + "berdl_table_id": "local/76990_7_2", + "table_name": "Genes", + "limit": 10, + "filters": [ + {"column": "contigs", "operator": "gt", "value": "50"} + ] + }' \ + "http://127.0.0.1:8000/table-data" +``` + +Verify in response that: +- `query_metadata.filters_applied` is 1 +- SQL query shows numeric comparison: `contigs > ?` (not `contigs > "50"`) + +## Testing Query Caching + +1. Make a query and note `execution_time_ms` +2. Make the same query again +3. Verify `cached: true` in response +4. Verify second query is faster + +## Testing Connection Pooling + +1. Make multiple queries to the same database +2. Check `/health` endpoint +3. Verify `access_count` increases for the database connection +4. Wait 30+ minutes, verify connection is closed + +## Common Issues + +### Server Not Responding + +1. Check if server is running: + ```bash + ps aux | grep uvicorn + ``` + +2. Check server logs for errors + +3. Verify port 8000 is not blocked: + ```bash + netstat -tuln | grep 8000 + ``` + +### Timeout Errors + +1. Check KBase service availability +2. Verify auth token is valid +3. Check network connectivity +4. Review server logs for blocking operations + +### 404 Errors + +1. Verify object/table exists in KBase +2. Check database is cached locally +3. Verify table name is correct (case-sensitive) + +### 500 Errors + +1. Check server logs for detailed error +2. Verify database file is not corrupted +3. Check disk space for cache directory +4. Verify SQLite database is valid + +## Performance Testing + +### Query Performance + +```bash +time curl -H "Authorization: Bearer $KB_TOKEN" \ + "http://127.0.0.1:8000/object/76990/7/2/tables/Genes/data?limit=1000" +``` + +### Cache Hit Rate + +Monitor cache hit rate by checking `cached` field in responses: +- First query: `cached: false` +- Subsequent queries: `cached: true` + +### Connection Pool Stats + +```bash +curl http://127.0.0.1:8000/health | jq '.cache' +``` + +## Integration Testing + +Test with DataTables Viewer frontend: + +1. Start TableScanner service +2. Configure frontend to point to `http://127.0.0.1:8000` +3. Test table loading +4. Test filtering +5. Test sorting +6. Test pagination +7. Verify all features work correctly + +## Automated Testing + +Run pytest suite: + +```bash +pytest tests/ -v +``` + +Run with coverage: + +```bash +pytest tests/ --cov=app --cov-report=html +``` diff --git a/scripts/migrate_fallback_configs.py b/scripts/migrate_fallback_configs.py deleted file mode 100755 index 63f0e06..0000000 --- a/scripts/migrate_fallback_configs.py +++ /dev/null @@ -1,144 +0,0 @@ -#!/usr/bin/env python3 -""" -Migration Script: Import Fallback Configs as Builtin Configs - -This script migrates existing fallback JSON configs (berdl_tables.json, etc.) -into the Config Control Plane as published builtin configurations. - -Usage: - python scripts/migrate_fallback_configs.py - -This ensures backward compatibility while transitioning to the unified -Config Control Plane architecture. -""" - -import json -import logging -import sys -from pathlib import Path - -# Add parent directory to path for imports -sys.path.insert(0, str(Path(__file__).parent.parent)) - -from app.services.config_store import get_config_store -from app.models import ConfigCreateRequest, ConfigSourceType -from app.configs.fallback_registry import ( - list_available_configs, - load_config_file, - FALLBACK_CONFIG_PATTERNS, -) - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" -) -logger = logging.getLogger(__name__) - - -def get_object_type_for_config(config_filename: str) -> str | None: - """ - Determine the KBase object type pattern for a config file. - - Args: - config_filename: Name of the config file (e.g., "berdl_tables.json") - - Returns: - Object type pattern or None - """ - # Reverse lookup: find pattern that matches this filename - for pattern, filename in FALLBACK_CONFIG_PATTERNS.items(): - if filename == config_filename: - # Extract object type from pattern - # Patterns like "KBaseGeneDataLakes\.BERDLTables.*" - if "BERDLTables" in pattern: - return "KBaseGeneDataLakes.BERDLTables-1.0" - elif "GenomeDataTables" in pattern or "GenomeDataLakeTables" in pattern: - return "KBaseFBA.GenomeDataLakeTables-1.0" - - return None - - -def migrate_fallback_configs() -> int: - """ - Migrate all fallback configs to Config Control Plane as builtins. - - Returns: - Number of configs migrated - """ - store = get_config_store() - configs = list_available_configs() - - migrated_count = 0 - - for config_info in configs: - filename = config_info["filename"] - config_id = config_info["id"] - config_data = load_config_file(filename) - - if not config_data: - logger.warning(f"Skipping {filename}: failed to load") - continue - - # Check if already migrated - object_type = get_object_type_for_config(filename) - source_ref = f"builtin:{config_id}" - - # Check for existing published builtin - existing = store.resolve(source_ref, object_type=object_type) - if existing and existing.state.value == "published": - logger.info(f"Skipping {filename}: already migrated as {existing.id}") - continue - - try: - # Create as builtin config - create_request = ConfigCreateRequest( - source_type=ConfigSourceType.BUILTIN, - source_ref=source_ref, - config=config_data, - object_type=object_type, - change_summary=f"Migrated from fallback config: {filename}", - ) - - # Create draft - record = store.create(create_request, "system:migration") - logger.info(f"Created draft config: {record.id} for {filename}") - - # Auto-propose - record = store.propose(record.id, "system:migration") - logger.info(f"Proposed config: {record.id}") - - # Auto-publish - record = store.publish(record.id, "system:migration") - logger.info(f"Published builtin config: {record.id} ({config_id})") - - migrated_count += 1 - - except Exception as e: - logger.error(f"Failed to migrate {filename}: {e}", exc_info=True) - continue - - return migrated_count - - -def main(): - """Main entry point.""" - logger.info("Starting fallback config migration...") - - try: - count = migrate_fallback_configs() - logger.info(f"Migration complete: {count} config(s) migrated") - - if count > 0: - logger.info("\nMigrated configs are now available via:") - logger.info(" GET /config/list?source_type=builtin&state=published") - logger.info(" GET /config/resolve/{source_ref}?object_type={object_type}") - - return 0 - - except Exception as e: - logger.error(f"Migration failed: {e}", exc_info=True) - return 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/scripts/sync_developer_configs.py b/scripts/sync_developer_configs.py deleted file mode 100755 index 02e2857..0000000 --- a/scripts/sync_developer_configs.py +++ /dev/null @@ -1,91 +0,0 @@ -#!/usr/bin/env python3 -""" -Developer Config Sync Script - -Syncs all developer-editable JSON configs to the Config Control Plane. -Run this after editing config files or pulling from git. - -Usage: - python scripts/sync_developer_configs.py [--auto-publish] -""" - -import argparse -import logging -import sys -from pathlib import Path - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent)) - -from app.services.developer_config import get_developer_config_manager - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(levelname)s - %(message)s" -) -logger = logging.getLogger(__name__) - - -def main(): - """Main entry point.""" - parser = argparse.ArgumentParser( - description="Sync developer configs to Config Control Plane" - ) - parser.add_argument( - "--auto-publish", - action="store_true", - help="Auto-publish configs after syncing" - ) - parser.add_argument( - "--filename", - help="Sync only this specific config file" - ) - - args = parser.parse_args() - - manager = get_developer_config_manager() - - try: - if args.filename: - # Sync single config - logger.info(f"Syncing {args.filename}...") - result = manager.sync_to_control_plane( - args.filename, - auto_publish=args.auto_publish - ) - logger.info(f"Result: {result['status']} - {result['message']}") - - if result['status'] == 'synced': - logger.info(f"Config ID: {result['config_id']}") - logger.info(f"State: {result['state']}") - else: - # Sync all configs - logger.info("Syncing all developer configs...") - results = manager.sync_all_to_control_plane( - auto_publish=args.auto_publish - ) - - synced = sum(1 for r in results.values() if r.get("status") == "synced") - unchanged = sum(1 for r in results.values() if r.get("status") == "unchanged") - errors = sum(1 for r in results.values() if r.get("status") == "error") - - logger.info(f"Sync complete:") - logger.info(f" Synced: {synced}") - logger.info(f" Unchanged: {unchanged}") - logger.info(f" Errors: {errors}") - - if errors > 0: - logger.warning("Some configs failed to sync:") - for filename, result in results.items(): - if result.get("status") == "error": - logger.warning(f" {filename}: {result.get('error')}") - - return 0 - - except Exception as e: - logger.error(f"Sync failed: {e}", exc_info=True) - return 1 - - -if __name__ == "__main__": - sys.exit(main()) From ad24006550a5fa775cdd6d56fa6f731a12c3d8c0 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Fri, 16 Jan 2026 13:56:48 -0600 Subject: [PATCH 08/19] refinements --- README.md | 36 +- app/config.py | 14 + app/config_constants.py | 20 + app/exceptions.py | 29 + app/routes.py | 958 +++-------------------------- app/services/data/query_service.py | 745 +++++++++------------- app/services/db_helper.py | 120 ++++ app/utils/async_utils.py | 27 + app/utils/request_utils.py | 101 +++ app/utils/sqlite.py | 346 +---------- app/utils/workspace.py | 10 + docs/API.md | 87 +++ docs/API_EXAMPLES.md | 595 ------------------ docs/API_REFERENCE.md | 517 ---------------- docs/ARCHITECTURE.md | 573 ++--------------- docs/CONFIG_SYSTEM.md | 182 ------ docs/CONTRIBUTING.md | 81 +++ docs/DEVELOPMENT.md | 312 ---------- docs/README.md | 41 +- docs/SERVICES.md | 250 -------- docs/SUMMARY.md | 149 ----- docs/TESTING.md | 235 ------- scripts/api_client.py | 86 --- scripts/verify_config_plane.py | 131 ---- tests/integration/test_routes.py | 33 + tests/test_api_basic.py | 67 -- tests/test_config_control_plane.py | 427 ------------- tests/test_config_generation.py | 521 ---------------- tests/test_integration.py | 409 ------------ tests/test_performance.py | 234 ------- tests/unit/test_query_service.py | 107 ++++ 31 files changed, 1106 insertions(+), 6337 deletions(-) create mode 100644 app/config_constants.py create mode 100644 app/exceptions.py create mode 100644 app/services/db_helper.py create mode 100644 app/utils/async_utils.py create mode 100644 app/utils/request_utils.py create mode 100644 docs/API.md delete mode 100644 docs/API_EXAMPLES.md delete mode 100644 docs/API_REFERENCE.md delete mode 100644 docs/CONFIG_SYSTEM.md create mode 100644 docs/CONTRIBUTING.md delete mode 100644 docs/DEVELOPMENT.md delete mode 100644 docs/SERVICES.md delete mode 100644 docs/SUMMARY.md delete mode 100644 docs/TESTING.md delete mode 100644 scripts/api_client.py delete mode 100644 scripts/verify_config_plane.py create mode 100644 tests/integration/test_routes.py delete mode 100644 tests/test_api_basic.py delete mode 100644 tests/test_config_control_plane.py delete mode 100644 tests/test_config_generation.py delete mode 100644 tests/test_integration.py delete mode 100644 tests/test_performance.py create mode 100644 tests/unit/test_query_service.py diff --git a/README.md b/README.md index bd5d387..e6ec70a 100644 --- a/README.md +++ b/README.md @@ -28,9 +28,15 @@ The service will be available at `http://localhost:8000`. API documentation is a ```bash cp .env.example .env # Edit .env and set KB_SERVICE_AUTH_TOKEN -bash scripts/dev.sh +./scripts/dev.sh ``` +The helper script `scripts/dev.sh` automates the environment setup: +1. Activates the virtual environment (`.venv` or `venv`) +2. Loads environment variables from `.env` +3. Sets `PYTHONPATH` +4. Starts the FastAPI development server with hot-reload via `fastapi dev` + ## API Usage ### List Tables @@ -79,9 +85,9 @@ curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ ## Documentation -- **[API Reference](docs/API_REFERENCE.md)** - Complete API documentation with examples -- **[Services Documentation](docs/SERVICES.md)** - Service architecture and implementation details -- **[Development Guide](docs/DEVELOPMENT.md)** - Setup, testing, and contribution guidelines +- **[API Reference](docs/API.md)** - Complete API documentation with examples +- **[Architecture Dictionary](docs/ARCHITECTURE.md)** - System design and technical overview +- **[Contributing Guide](docs/CONTRIBUTING.md)** - Setup, testing, and contribution guidelines ## Architecture @@ -104,19 +110,15 @@ TableScanner/ │ ├── models.py # Pydantic models │ ├── config.py # Configuration settings │ ├── services/ -│ │ └── data/ -│ │ ├── connection_pool.py # Connection pooling -│ │ ├── query_service.py # Query execution -│ │ ├── schema_service.py # Schema information -│ │ ├── statistics_service.py # Column statistics -│ │ └── ... -│ └── utils/ -│ ├── sqlite.py # SQLite utilities -│ ├── workspace.py # KBase workspace client -│ └── cache.py # Cache utilities -├── docs/ # Documentation -├── tests/ # Test suite -├── archive/ # Archived code +│ │ ├── data/ +│ │ │ ├── connection_pool.py # Connection pooling +│ │ │ ├── query_service.py # Query execution +│ │ │ └── ... +│ │ └── db_helper.py # Database resolution +│ └── utils/ # Utilities (SQLite, KBase Client) +├── docs/ # Documentation (API, Architecture, Contributing) +├── tests/ # Test suite (Unit & Integration) +├── scripts/ # Helper scripts (dev.sh) └── static/ # Static files ``` diff --git a/app/config.py b/app/config.py index 37fb984..525a056 100644 --- a/app/config.py +++ b/app/config.py @@ -59,9 +59,23 @@ class Settings(BaseSettings): default=False, description="Enable debug mode with verbose logging" ) + KB_ENV: str = Field( + default="appdev", + description="KBase environment (appdev, ci, prod)" + ) # Root path for proxy deployment (e.g., "/services/berdl_table_scanner") ROOT_PATH: str = "" + + # Timeout settings + DOWNLOAD_TIMEOUT_SECONDS: float = Field( + default=30.0, + description="Timeout in seconds for downloading databases" + ) + KBASE_API_TIMEOUT_SECONDS: float = Field( + default=10.0, + description="Timeout in seconds for KBase API calls" + ) class Config: env_file = ".env" diff --git a/app/config_constants.py b/app/config_constants.py new file mode 100644 index 0000000..2f5124e --- /dev/null +++ b/app/config_constants.py @@ -0,0 +1,20 @@ +""" +Configuration constants for TableScanner. +""" + +# Default values +DEFAULT_LIMIT = 100 +MAX_LIMIT = 500000 +DEFAULT_OFFSET = 0 +DEFAULT_SORT_ORDER = "ASC" + +# Cache settings +CACHE_TTL_SECONDS = 300 # 5 minutes +CACHE_MAX_ENTRIES = 1000 +INDEX_CACHE_TTL = 3600 # 1 hour + +# Timeout settings +KBASE_API_TIMEOUT_SECONDS = 30 + +# API Version +API_VERSION = "2.0" diff --git a/app/exceptions.py b/app/exceptions.py new file mode 100644 index 0000000..1b707e9 --- /dev/null +++ b/app/exceptions.py @@ -0,0 +1,29 @@ +""" +Custom exceptions for TableScanner. +""" + +class TableScannerError(Exception): + """Base exception for TableScanner.""" + pass + +class TableNotFoundError(TableScannerError): + """Raised when a requested table does not exist.""" + def __init__(self, table_name: str, available_tables: list[str] | None = None): + msg = f"Table '{table_name}' not found" + if available_tables: + msg += f". Available: {available_tables}" + super().__init__(msg) + self.table_name = table_name + +class ColumnNotFoundError(TableScannerError): + """Raised when a requested column does not exist.""" + def __init__(self, column_name: str, table_name: str): + super().__init__(f"Column '{column_name}' not found in table '{table_name}'") + +class InvalidFilterError(TableScannerError): + """Raised when a filter configuration is invalid.""" + pass + +class DatabaseAccessError(TableScannerError): + """Raised when database file cannot be accessed or opened.""" + pass diff --git a/app/routes.py b/app/routes.py index 99a8273..4a1e3bc 100644 --- a/app/routes.py +++ b/app/routes.py @@ -46,26 +46,21 @@ ) from app.utils.sqlite import ( list_tables, - get_table_data, get_table_columns, get_table_row_count, validate_table_exists, - ensure_indices, -) -from app.services.data.query_service import ( - get_query_service, - FilterSpec, - AggregationSpec, ) from app.services.data.schema_service import get_schema_service -from app.services.data.statistics_service import get_statistics_service from app.services.data.connection_pool import get_connection_pool -from app.utils.cache import ( - is_cached, - clear_cache, - list_cached_items, +from app.services.db_helper import ( + get_handle_db_path, + get_object_db_path, + ensure_table_accessible, ) +from app.utils.async_utils import run_sync_in_thread +from app.utils.request_utils import TableRequestProcessor from app.config import settings +from app.config_constants import MAX_LIMIT, DEFAULT_LIMIT # Configure module logger logger = logging.getLogger(__name__) @@ -120,11 +115,6 @@ async def health_check(): Health check endpoint for DataTables Viewer API. Returns service status, cache information, and connection pool stats. - - **Example:** - ```bash - curl "http://127.0.0.1:8000/health" - ``` """ from datetime import datetime @@ -168,49 +158,30 @@ async def list_tables_by_handle( ): """ List all tables in a SQLite database accessed via handle reference. - - **Example:** - ```bash - curl -H "Authorization: $KB_TOKEN" \ - "https://appdev.kbase.us/services/berdl_table_scanner/handle/KBH_248028/tables" - ``` """ try: token = get_auth_token(authorization) cache_dir = get_cache_dir() - # Download SQLite from handle - client = KBaseClient(token, kb_env, cache_dir) - - # Cache path based on handle - safe_handle = handle_ref.replace(":", "_").replace("/", "_") - db_dir = cache_dir / "handles" - db_dir.mkdir(parents=True, exist_ok=True) - db_path = db_dir / f"{safe_handle}.db" - - # Atomic download to prevent race conditions - if not db_path.exists(): - temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") - try: - client.download_blob_file(handle_ref, temp_path) - temp_path.rename(db_path) - except Exception: - temp_path.unlink(missing_ok=True) - raise + # Get database path (handles download and caching) + db_path = await get_handle_db_path(handle_ref, token, kb_env, cache_dir) # List tables - table_names = list_tables(db_path) + table_names = await run_sync_in_thread(list_tables, db_path) tables = [] + + # Get details for each table for name in table_names: try: - columns = get_table_columns(db_path, name) - row_count = get_table_row_count(db_path, name) + # Run these lightweight checks in thread pool too + columns = await run_sync_in_thread(get_table_columns, db_path, name) + row_count = await run_sync_in_thread(get_table_row_count, db_path, name) tables.append({ "name": name, "row_count": row_count, "column_count": len(columns) }) - except Exception as e: + except Exception: logger.warning("Error getting table info for %s", name, exc_info=True) tables.append({"name": name}) @@ -220,6 +191,8 @@ async def list_tables_by_handle( "db_path": str(db_path) } + except HTTPException: + raise except Exception as e: logger.error(f"Error listing tables from handle: {e}") raise HTTPException(status_code=500, detail=str(e)) @@ -234,39 +207,16 @@ async def get_table_schema_by_handle( ): """ Get schema (columns) for a table accessed via handle reference. - - **Example:** - ```bash - curl -H "Authorization: $KB_TOKEN" \ - "https://appdev.kbase.us/services/berdl_table_scanner/handle/KBH_248028/tables/Genes/schema" - ``` """ try: token = get_auth_token(authorization) cache_dir = get_cache_dir() - client = KBaseClient(token, kb_env, cache_dir) + db_path = await get_handle_db_path(handle_ref, token, kb_env, cache_dir) + await ensure_table_accessible(db_path, table_name) - safe_handle = handle_ref.replace(":", "_").replace("/", "_") - db_dir = cache_dir / "handles" - db_dir.mkdir(parents=True, exist_ok=True) - db_path = db_dir / f"{safe_handle}.db" - - if not db_path.exists(): - temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") - try: - client.download_blob_file(handle_ref, temp_path) - temp_path.rename(db_path) - except Exception: - temp_path.unlink(missing_ok=True) - raise - - if not validate_table_exists(db_path, table_name): - available = list_tables(db_path) - raise HTTPException(404, f"Table '{table_name}' not found. Available: {available}") - - columns = get_table_columns(db_path, table_name) - row_count = get_table_row_count(db_path, table_name) + columns = await run_sync_in_thread(get_table_columns, db_path, table_name) + row_count = await run_sync_in_thread(get_table_row_count, db_path, table_name) return { "handle_ref": handle_ref, @@ -286,7 +236,7 @@ async def get_table_schema_by_handle( async def get_table_data_by_handle( handle_ref: str, table_name: str, - limit: int = Query(100, ge=1, le=500000), + limit: int = Query(DEFAULT_LIMIT, ge=1, le=MAX_LIMIT), offset: int = Query(0, ge=0), sort_column: str | None = Query(None), sort_order: str | None = Query("ASC"), @@ -296,64 +246,25 @@ async def get_table_data_by_handle( ): """ Query table data from SQLite via handle reference. - - **Example:** - ```bash - curl -H "Authorization: $KB_TOKEN" \ - "https://appdev.kbase.us/services/berdl_table_scanner/handle/KBH_248028/tables/Genes/data?limit=5" - ``` """ - start_time = time.time() - try: token = get_auth_token(authorization) cache_dir = get_cache_dir() - client = KBaseClient(token, kb_env, cache_dir) - - safe_handle = handle_ref.replace(":", "_").replace("/", "_") - db_dir = cache_dir / "handles" - db_dir.mkdir(parents=True, exist_ok=True) - db_path = db_dir / f"{safe_handle}.db" + db_path = await get_handle_db_path(handle_ref, token, kb_env, cache_dir) + await ensure_table_accessible(db_path, table_name) - if not db_path.exists(): - temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") - try: - client.download_blob_file(handle_ref, temp_path) - temp_path.rename(db_path) - except Exception: - temp_path.unlink(missing_ok=True) - raise - - if not validate_table_exists(db_path, table_name): - available = list_tables(db_path) - raise HTTPException(404, f"Table '{table_name}' not found. Available: {available}") - - # Query data - headers, data, total_count, filtered_count, db_query_ms, conversion_ms = get_table_data( - sqlite_file=db_path, + return await TableRequestProcessor.process_data_request( + db_path=db_path, table_name=table_name, limit=limit, offset=offset, sort_column=sort_column, - sort_order=sort_order, + sort_order=sort_order or "ASC", search_value=search, + handle_ref_or_id=handle_ref ) - response_time_ms = (time.time() - start_time) * 1000 - - return { - "handle_ref": handle_ref, - "table_name": table_name, - "headers": headers, - "data": data, - "row_count": len(data), - "total_count": total_count, - "filtered_count": filtered_count, - "response_time_ms": response_time_ms, - "db_query_ms": db_query_ms - } - except HTTPException: raise except Exception as e: @@ -376,12 +287,6 @@ async def list_pangenomes_by_object( ): """ List pangenomes from a BERDLTables/GenomeDataLakeTables object. - - **Example:** - ```bash - curl -H "Authorization: $KB_TOKEN" \ - "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/pangenomes" - ``` """ try: token = get_auth_token(authorization) @@ -411,15 +316,6 @@ async def list_tables_by_object( ): """ List tables for a BERDLTables object. - - Returns table list along with viewer config info (fingerprint/URL if cached). - Compatible with DataTables Viewer API format. - - **Example:** - ```bash - curl -H "Authorization: $KB_TOKEN" \\ - "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables" - ``` """ import asyncio @@ -428,70 +324,11 @@ async def list_tables_by_object( cache_dir = get_cache_dir() berdl_table_id = ws_ref - # Check cache first to avoid blocking KBase calls - from app.utils.cache import get_upa_cache_path - cache_dir_path = Path(cache_dir) - db_dir = get_upa_cache_path(cache_dir_path, berdl_table_id) - db_path = db_dir / "tables.db" + # Get database path (handles caching, download timeouts via helper) + db_path = await get_object_db_path(berdl_table_id, token, kb_env, cache_dir) - # If not cached, download in thread pool to avoid blocking - if not db_path.exists(): - try: - # Run blocking download in thread pool with timeout - import asyncio - try: - # Use to_thread if available (Python 3.9+) - if hasattr(asyncio, 'to_thread'): - db_path = await asyncio.wait_for( - asyncio.to_thread( - download_pangenome_db, - berdl_table_id, - token, - cache_dir, - kb_env - ), - timeout=30.0 # 30 second timeout for download - ) - else: - # Fallback for older Python - loop = asyncio.get_event_loop() - db_path = await asyncio.wait_for( - loop.run_in_executor( - None, - download_pangenome_db, - berdl_table_id, - token, - cache_dir, - kb_env - ), - timeout=30.0 - ) - except asyncio.TimeoutError: - logger.error(f"Database download timed out for {berdl_table_id}") - raise HTTPException( - status_code=504, - detail=f"Database download timed out. Please try again later or ensure the database is cached." - ) - except asyncio.TimeoutError: - logger.error(f"Database download timed out for {berdl_table_id}") - raise HTTPException( - status_code=504, - detail=f"Database download timed out. Please try again later or ensure the database is cached." - ) - except Exception as e: - logger.error(f"Error downloading database: {e}") - raise HTTPException( - status_code=500, - detail=f"Failed to access database. Error: {str(e)}" - ) - - # Run table listing in thread pool to avoid blocking - import asyncio - if hasattr(asyncio, 'to_thread'): - table_names = await asyncio.to_thread(list_tables, db_path) - else: - loop = asyncio.get_event_loop() - table_names = await loop.run_in_executor(None, list_tables, db_path) + # List tables (run in thread) + table_names = await run_sync_in_thread(list_tables, db_path) tables = [] schemas = {} @@ -500,16 +337,12 @@ async def list_tables_by_object( # Use schema service for better column type information schema_service = get_schema_service() - # Process tables (these are fast SQLite operations, but run in thread pool for consistency) + # Process tables for name in table_names: try: - if hasattr(asyncio, 'to_thread'): - columns = await asyncio.to_thread(get_table_columns, db_path, name) - row_count = await asyncio.to_thread(get_table_row_count, db_path, name) - else: - loop = asyncio.get_event_loop() - columns = await loop.run_in_executor(None, get_table_columns, db_path, name) - row_count = await loop.run_in_executor(None, get_table_row_count, db_path, name) + # Run lightweight checks in thread + columns = await run_sync_in_thread(get_table_columns, db_path, name) + row_count = await run_sync_in_thread(get_table_row_count, db_path, name) # Get display name (use table name as default) display_name = name.replace("_", " ").title() @@ -524,15 +357,9 @@ async def list_tables_by_object( # Build schema map with actual types try: - if hasattr(asyncio, 'to_thread'): - table_schema = await asyncio.to_thread( - schema_service.get_table_schema, db_path, name - ) - else: - loop = asyncio.get_event_loop() - table_schema = await loop.run_in_executor( - None, schema_service.get_table_schema, db_path, name - ) + table_schema = await run_sync_in_thread( + schema_service.get_table_schema, db_path, name + ) schemas[name] = { col["name"]: col["type"] for col in table_schema["columns"] @@ -540,36 +367,18 @@ async def list_tables_by_object( except Exception: # Fallback to default type schemas[name] = {col: "TEXT" for col in columns} - except Exception as e: + except Exception: logger.warning("Error getting table info for %s", name, exc_info=True) tables.append({"name": name, "displayName": name}) - # Get object type (non-blocking, don't fail if this times out) - object_type = None + # Get object type (non-blocking) try: - # Run in thread pool with timeout - if hasattr(asyncio, 'to_thread'): - object_type = await asyncio.wait_for( - asyncio.to_thread( - get_object_type, - berdl_table_id, - token, - kb_env - ), - timeout=5.0 # 5 second timeout - ) - else: - loop = asyncio.get_event_loop() - object_type = await asyncio.wait_for( - loop.run_in_executor( - None, - get_object_type, - berdl_table_id, - token, - kb_env - ), - timeout=5.0 - ) + # Use specific timeout for API call + import asyncio + object_type = await asyncio.wait_for( + run_sync_in_thread(get_object_type, berdl_table_id, token, kb_env), + timeout=settings.KBASE_API_TIMEOUT_SECONDS + ) except (asyncio.TimeoutError, Exception) as e: logger.warning(f"Could not get object type (non-critical): {e}") object_type = None @@ -618,7 +427,7 @@ async def list_tables_by_object( async def get_table_data_by_object( ws_ref: str, table_name: str, - limit: int = Query(100, ge=1, le=500000), + limit: int = Query(DEFAULT_LIMIT, ge=1, le=MAX_LIMIT), offset: int = Query(0, ge=0), sort_column: str | None = Query(None), sort_order: str | None = Query("ASC"), @@ -628,125 +437,28 @@ async def get_table_data_by_object( ): """ Query table data from a BERDLTables object. - - **Example:** - ```bash - curl -H "Authorization: $KB_TOKEN" \ - "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables/Genes/data?limit=5" - ``` """ - import asyncio - - start_time = time.time() - try: token = get_auth_token(authorization) cache_dir = get_cache_dir() berdl_table_id = ws_ref - # Check cache first - from app.utils.cache import get_upa_cache_path - cache_dir_path = Path(cache_dir) - db_dir = get_upa_cache_path(cache_dir_path, berdl_table_id) - db_path = db_dir / "tables.db" - - # If not cached, download in thread pool - if not db_path.exists(): - try: - loop = asyncio.get_event_loop() - db_path = await loop.run_in_executor( - None, - download_pangenome_db, - berdl_table_id, - token, - cache_dir, - kb_env - ) - except Exception as e: - logger.error(f"Error downloading database: {e}") - raise HTTPException( - status_code=500, - detail=f"Failed to access database. Error: {str(e)}" - ) - - # Validate table exists (run in thread pool) - import asyncio - if hasattr(asyncio, 'to_thread'): - table_exists = await asyncio.to_thread(validate_table_exists, db_path, table_name) - else: - loop = asyncio.get_event_loop() - table_exists = await loop.run_in_executor(None, validate_table_exists, db_path, table_name) - - if not table_exists: - if hasattr(asyncio, 'to_thread'): - available = await asyncio.to_thread(list_tables, db_path) - else: - loop = asyncio.get_event_loop() - available = await loop.run_in_executor(None, list_tables, db_path) - raise HTTPException(404, f"Table '{table_name}' not found. Available: {available}") - - # Query data (run in thread pool) - def run_query(): - return get_table_data( - sqlite_file=db_path, - table_name=table_name, - limit=limit, - offset=offset, - sort_column=sort_column, - sort_order=sort_order, - search_value=search, - ) - - if hasattr(asyncio, 'to_thread'): - query_result = await asyncio.to_thread(run_query) - else: - loop = asyncio.get_event_loop() - query_result = await loop.run_in_executor(None, run_query) - headers, data, total_count, filtered_count, db_query_ms, conversion_ms = query_result - - response_time_ms = (time.time() - start_time) * 1000 - - # Get object type (non-blocking) - object_type = None - try: - if hasattr(asyncio, 'to_thread'): - object_type = await asyncio.wait_for( - asyncio.to_thread( - get_object_type, - berdl_table_id, - token, - kb_env - ), - timeout=5.0 - ) - else: - loop = asyncio.get_event_loop() - object_type = await asyncio.wait_for( - loop.run_in_executor( - None, - get_object_type, - berdl_table_id, - token, - kb_env - ), - timeout=5.0 - ) - except (asyncio.TimeoutError, Exception): - object_type = None + # Get and validate DB access + db_path = await get_object_db_path(berdl_table_id, token, kb_env, cache_dir) + await ensure_table_accessible(db_path, table_name) - return { - "berdl_table_id": berdl_table_id, - "table_name": table_name, - "headers": headers, - "data": data, - "row_count": len(data), - "total_count": total_count, - "filtered_count": filtered_count, - "response_time_ms": response_time_ms, - "db_query_ms": db_query_ms, - "sqlite_file": str(db_path), - "object_type": object_type - } + result = await TableRequestProcessor.process_data_request( + db_path=db_path, + table_name=table_name, + limit=limit, + offset=offset, + sort_column=sort_column, + sort_order=sort_order or "ASC", + search_value=search, + handle_ref_or_id=berdl_table_id + ) + + return result except HTTPException: raise @@ -767,10 +479,6 @@ async def get_pangenomes( ): """ List pangenomes from BERDLTables object. - - Returns: - - pangenomes: List of pangenome info - - pangenome_count: Total number of pangenomes """ try: token = get_auth_token(authorization) @@ -839,29 +547,14 @@ async def query_table_data( ): """ Query table data using a JSON body. Recommended for programmatic access. - - **Example:** - ```bash - curl -X POST -H "Authorization: $KB_TOKEN" -H "Content-Type: application/json" \ - -d '{ - "berdl_table_id": "76990/7/2", - "table_name": "Metadata_Conditions", - "limit": 5" - }' \ - "https://appdev.kbase.us/services/berdl_table_scanner/table-data" - ``` """ - start_time = time.time() - try: token = get_auth_token(authorization) cache_dir = get_cache_dir() kb_env = getattr(request, 'kb_env', 'appdev') or 'appdev' - # Determine filters (support both query_filters and col_filter) filters = request.col_filter if request.col_filter else request.query_filters - # Download (or get cached) DB - auto-resolves ID if None try: db_path = download_pangenome_db( request.berdl_table_id, token, cache_dir, kb_env @@ -872,527 +565,34 @@ async def query_table_data( if not validate_table_exists(db_path, request.table_name): available = list_tables(db_path) raise ValueError(f"Table '{request.table_name}' not found. Available: {available}") - - try: - ensure_indices(db_path, request.table_name) - except: - pass - - headers, data, total_count, filtered_count, db_query_ms, conversion_ms = get_table_data( - sqlite_file=db_path, - table_name=request.table_name, - limit=request.limit, - offset=request.offset, - sort_column=request.sort_column, - sort_order=request.sort_order, - search_value=request.search_value, - query_filters=filters, - columns=request.columns, - order_by=request.order_by - ) - - response_time_ms = (time.time() - start_time) * 1000 - - return TableDataResponse( - headers=headers, - data=data, - row_count=len(data), - total_count=total_count, - filtered_count=filtered_count, - table_name=request.table_name, - response_time_ms=response_time_ms, - db_query_ms=db_query_ms, - conversion_ms=conversion_ms, - source="Cache" if is_cached(db_path) else "Downloaded", - cache_file=str(db_path), - sqlite_file=str(db_path) - ) - - except ValueError as e: - raise HTTPException(status_code=400, detail=str(e)) - except Exception as e: - logger.error(f"Error querying table data: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -# ============================================================================= -# CACHE MANAGEMENT -# ============================================================================= - -@router.post("/clear-cache", response_model=CacheResponse, tags=["Cache Management"]) -async def clear_pangenome_cache( - berdl_table_id: str | None = Query(None) -): - """Clear cached databases.""" - try: - cache_dir = get_cache_dir() - result = clear_cache(cache_dir, berdl_table_id) - return CacheResponse(status="success", message=result.get("message", "Cache cleared")) - except Exception as e: - return CacheResponse(status="error", message=str(e)) - - -@router.get("/cache", tags=["Cache Management"]) -async def list_cache(): - """List cached items.""" - cache_dir = get_cache_dir() - items = list_cached_items(cache_dir) - return {"cache_dir": str(cache_dir), "items": items, "total": len(items)} - - - - -# ============================================================================= -# DATATABLES VIEWER API ENDPOINTS -# ============================================================================= - - -@router.get("/schema/{db_name}/tables/{table_name}", response_model=TableSchemaInfo, tags=["Object Access"]) -async def get_table_schema_datatables( - db_name: str, - table_name: str, - kb_env: str = Query("appdev"), - authorization: str | None = Header(None) -): - """ - Get table schema information for DataTables Viewer API. - - Returns column types, constraints, and indexes. - - **Example:** - ```bash - curl -H "Authorization: $KB_TOKEN" \ - "http://127.0.0.1:8000/schema/local/76990_7_2/tables/Genes" - ``` - """ - try: - token = get_auth_token(authorization) - cache_dir = get_cache_dir() - - # Parse db_name (format: local/db_name or handle/KBH_xxx) - if db_name.startswith("local/"): - # Object-based database - berdl_table_id = db_name.replace("local/", "") - # Convert back from underscore format if needed - if "_" in berdl_table_id and "/" not in berdl_table_id: - # Try to reconstruct UPA format (assumes format: ws_obj_ver) - parts = berdl_table_id.split("_") - if len(parts) >= 3: - berdl_table_id = f"{parts[0]}/{parts[1]}/{parts[2]}" - db_path = download_pangenome_db( - berdl_table_id=berdl_table_id, - auth_token=token, - cache_dir=cache_dir, - kb_env=kb_env - ) - elif db_name.startswith("handle/"): - # Handle-based database - handle_ref = db_name.replace("handle/", "") - client = KBaseClient(token, kb_env, cache_dir) - safe_handle = handle_ref.replace(":", "_").replace("/", "_") - db_dir = cache_dir / "handles" - db_dir.mkdir(parents=True, exist_ok=True) - db_path = db_dir / f"{safe_handle}.db" - if not db_path.exists(): - temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") - try: - client.download_blob_file(handle_ref, temp_path) - temp_path.rename(db_path) - except Exception: - temp_path.unlink(missing_ok=True) - raise - else: - raise HTTPException(status_code=400, detail=f"Invalid db_name format: {db_name}") - - if not validate_table_exists(db_path, table_name): - available = list_tables(db_path) - raise HTTPException(404, f"Table '{table_name}' not found. Available: {available}") - - schema_service = get_schema_service() - schema = schema_service.get_table_schema(db_path, table_name) + columns_list = None + if request.columns and request.columns != "all": + columns_list = [c.strip() for c in request.columns.split(",") if c.strip()] - return schema - - except HTTPException: - raise - except Exception as e: - logger.error(f"Error getting schema: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -@router.get("/schema/{db_name}/tables", tags=["Object Access"]) -async def get_all_tables_schema_datatables( - db_name: str, - kb_env: str = Query("appdev"), - authorization: str | None = Header(None) -): - """ - Get schema information for all tables in a database. - - **Example:** - ```bash - curl -H "Authorization: $KB_TOKEN" \ - "http://127.0.0.1:8000/schema/local/76990_7_2/tables" - ``` - """ - try: - token = get_auth_token(authorization) - cache_dir = get_cache_dir() - - # Parse db_name (same logic as single table endpoint) - if db_name.startswith("local/"): - berdl_table_id = db_name.replace("local/", "") - # Convert back from underscore format if needed - if "_" in berdl_table_id and "/" not in berdl_table_id: - parts = berdl_table_id.split("_") - if len(parts) >= 3: - berdl_table_id = f"{parts[0]}/{parts[1]}/{parts[2]}" - db_path = download_pangenome_db( - berdl_table_id=berdl_table_id, - auth_token=token, - cache_dir=cache_dir, - kb_env=kb_env - ) - elif db_name.startswith("handle/"): - handle_ref = db_name.replace("handle/", "") - client = KBaseClient(token, kb_env, cache_dir) - safe_handle = handle_ref.replace(":", "_").replace("/", "_") - db_dir = cache_dir / "handles" - db_dir.mkdir(parents=True, exist_ok=True) - db_path = db_dir / f"{safe_handle}.db" - - if not db_path.exists(): - temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") - try: - client.download_blob_file(handle_ref, temp_path) - temp_path.rename(db_path) - except Exception: - temp_path.unlink(missing_ok=True) - raise - else: - raise HTTPException(status_code=400, detail=f"Invalid db_name format: {db_name}") - - schema_service = get_schema_service() - schemas = schema_service.get_all_tables_schema(db_path) - - return schemas - - except HTTPException: - raise - except Exception as e: - logger.error(f"Error getting all schemas: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -@router.get("/object/{db_name}/tables/{table_name}/stats", response_model=TableStatisticsResponse, tags=["Object Access"]) -async def get_table_statistics( - db_name: str, - table_name: str, - kb_env: str = Query("appdev"), - authorization: str | None = Header(None) -): - """ - Get column statistics for a table. - - Returns pre-computed statistics including null_count, distinct_count, - min, max, mean, median, stddev, and sample values. - - **Example:** - ```bash - curl -H "Authorization: $KB_TOKEN" \ - "http://127.0.0.1:8000/object/local/76990_7_2/tables/Genes/stats" - ``` - """ - try: - token = get_auth_token(authorization) - cache_dir = get_cache_dir() + effective_sort_col = request.sort_column + effective_sort_dir = request.sort_order - # Parse db_name - if db_name.startswith("local/"): - berdl_table_id = db_name.replace("local/", "") - # Convert back from underscore format if needed - if "_" in berdl_table_id and "/" not in berdl_table_id: - parts = berdl_table_id.split("_") - if len(parts) >= 3: - berdl_table_id = f"{parts[0]}/{parts[1]}/{parts[2]}" - db_path = download_pangenome_db( - berdl_table_id=berdl_table_id, - auth_token=token, - cache_dir=cache_dir, - kb_env=kb_env - ) - elif db_name.startswith("handle/"): - handle_ref = db_name.replace("handle/", "") - client = KBaseClient(token, kb_env, cache_dir) - safe_handle = handle_ref.replace(":", "_").replace("/", "_") - db_dir = cache_dir / "handles" - db_dir.mkdir(parents=True, exist_ok=True) - db_path = db_dir / f"{safe_handle}.db" + if not effective_sort_col and request.order_by: + first_sort = request.order_by[0] + effective_sort_col = first_sort.get("column") + effective_sort_dir = first_sort.get("direction", "ASC").upper() - if not db_path.exists(): - temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") - try: - client.download_blob_file(handle_ref, temp_path) - temp_path.rename(db_path) - except Exception: - temp_path.unlink(missing_ok=True) - raise - else: - # Try as berdl_table_id directly - db_path = download_pangenome_db( - berdl_table_id=db_name, - auth_token=token, - cache_dir=cache_dir, - kb_env=kb_env - ) - - if not validate_table_exists(db_path, table_name): - available = list_tables(db_path) - raise HTTPException(404, f"Table '{table_name}' not found. Available: {available}") - - stats_service = get_statistics_service() - stats = stats_service.get_table_statistics(db_path, table_name) - - return stats - - except HTTPException: - raise - except Exception as e: - logger.error(f"Error getting statistics: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -@router.post("/api/aggregate/{db_name}/tables/{table_name}", response_model=TableDataQueryResponse, tags=["Object Access"]) -async def execute_aggregation( - db_name: str, - table_name: str, - request: AggregationQueryRequest, - kb_env: str = Query("appdev"), - authorization: str | None = Header(None) -): - """ - Execute aggregation query with GROUP BY. - - **Example:** - ```bash - curl -X POST -H "Authorization: $KB_TOKEN" -H "Content-Type: application/json" \ - -d '{ - "group_by": ["category"], - "aggregations": [ - {"column": "value", "function": "sum", "alias": "total"} - ], - "filters": [{"column": "value", "operator": "gt", "value": 100}] - }' \ - "http://127.0.0.1:8000/api/aggregate/local/76990_7_2/tables/Data" - ``` - """ - try: - token = get_auth_token(authorization) - cache_dir = get_cache_dir() - - # Parse db_name - if db_name.startswith("local/"): - berdl_table_id = db_name.replace("local/", "") - # Convert back from underscore format if needed - if "_" in berdl_table_id and "/" not in berdl_table_id: - parts = berdl_table_id.split("_") - if len(parts) >= 3: - berdl_table_id = f"{parts[0]}/{parts[1]}/{parts[2]}" - db_path = download_pangenome_db( - berdl_table_id=berdl_table_id, - auth_token=token, - cache_dir=cache_dir, - kb_env=kb_env - ) - elif db_name.startswith("handle/"): - handle_ref = db_name.replace("handle/", "") - client = KBaseClient(token, kb_env, cache_dir) - safe_handle = handle_ref.replace(":", "_").replace("/", "_") - db_dir = cache_dir / "handles" - db_dir.mkdir(parents=True, exist_ok=True) - db_path = db_dir / f"{safe_handle}.db" - - if not db_path.exists(): - temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") - try: - client.download_blob_file(handle_ref, temp_path) - temp_path.rename(db_path) - except Exception: - temp_path.unlink(missing_ok=True) - raise - else: - # Try as berdl_table_id directly - db_path = download_pangenome_db( - berdl_table_id=db_name, - auth_token=token, - cache_dir=cache_dir, - kb_env=kb_env - ) - - if not validate_table_exists(db_path, table_name): - available = list_tables(db_path) - raise HTTPException(404, f"Table '{table_name}' not found. Available: {available}") - - # Convert request to query service format - query_service = get_query_service() - - filters = None - if request.filters: - filters = [ - FilterSpec( - column=f.column, - operator=f.operator, - value=f.value, - value2=f.value2 - ) - for f in request.filters - ] - - aggregations = [ - AggregationSpec( - column=a.column, - function=a.function, - alias=a.alias - ) - for a in request.aggregations - ] - - result = query_service.execute_query( - db_path=db_path, - table_name=table_name, - limit=request.limit, - offset=request.offset, - filters=filters, - aggregations=aggregations, - group_by=request.group_by - ) - - return TableDataQueryResponse(**result) - - except HTTPException: - raise - except Exception as e: - logger.error(f"Error executing aggregation: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -@router.post("/table-data", response_model=TableDataQueryResponse, tags=["Legacy"]) -async def query_table_data_enhanced( - request: TableDataQueryRequest, - authorization: str | None = Header(None) -): - """ - Enhanced table data query endpoint with full DataTables Viewer API support. - - Supports type-aware filtering, aggregations, and comprehensive metadata. - - **Example:** - ```bash - curl -X POST -H "Authorization: $KB_TOKEN" -H "Content-Type: application/json" \ - -d '{ - "berdl_table_id": "local/76990_7_2", - "table_name": "Genes", - "limit": 100, - "offset": 0, - "filters": [ - {"column": "contigs", "operator": "gt", "value": "50"} - ] - }' \ - "http://127.0.0.1:8000/table-data" - ``` - """ - start_time = time.time() - - try: - token = get_auth_token(authorization) - cache_dir = get_cache_dir() - kb_env = "appdev" # Default, could be from request - - # Parse berdl_table_id - if request.berdl_table_id.startswith("local/"): - berdl_table_id = request.berdl_table_id.replace("local/", "") - # Convert back from underscore format if needed - if "_" in berdl_table_id and "/" not in berdl_table_id: - # Try to reconstruct UPA format (assumes format: ws_obj_ver) - parts = berdl_table_id.split("_") - if len(parts) >= 3: - berdl_table_id = f"{parts[0]}/{parts[1]}/{parts[2]}" - else: - berdl_table_id = request.berdl_table_id - - # Download (or get cached) DB - try: - db_path = download_pangenome_db( - berdl_table_id, token, cache_dir, kb_env - ) - except ValueError as e: - raise HTTPException(status_code=404, detail=str(e)) - except Exception as e: - logger.error(f"Error downloading database: {e}") - raise HTTPException(status_code=500, detail=f"Failed to access database: {str(e)}") - - if not validate_table_exists(db_path, request.table_name): - available = list_tables(db_path) - raise HTTPException(404, f"Table '{request.table_name}' not found. Available: {available}") - - # Convert request to query service format - query_service = get_query_service() - - # Convert filters - filters = None - if request.filters: - filters = [ - FilterSpec( - column=f.column, - operator=f.operator, - value=f.value, - value2=f.value2 - ) - for f in request.filters - ] - elif request.col_filter: - # Legacy col_filter format - filters = [ - FilterSpec( - column=col, - operator="like", - value=val - ) - for col, val in request.col_filter.items() - ] - - # Convert aggregations - aggregations = None - if request.aggregations: - aggregations = [ - AggregationSpec( - column=a.column, - function=a.function, - alias=a.alias - ) - for a in request.aggregations - ] - - # Execute query - result = query_service.execute_query( + return await TableRequestProcessor.process_data_request( db_path=db_path, table_name=request.table_name, limit=request.limit, offset=request.offset, - columns=request.columns, - sort_column=request.sort_column, - sort_order=request.sort_order, + sort_column=effective_sort_col, + sort_order=effective_sort_dir or "ASC", search_value=request.search_value, + columns=columns_list, filters=filters, - aggregations=aggregations, - group_by=request.group_by + handle_ref_or_id=request.berdl_table_id ) - return TableDataQueryResponse(**result) - except HTTPException: raise except Exception as e: - logger.error(f"Error querying table data: {e}") + logger.error(f"Error querying data: {e}") raise HTTPException(status_code=500, detail=str(e)) diff --git a/app/services/data/query_service.py b/app/services/data/query_service.py index 4f2a0ba..6efba77 100644 --- a/app/services/data/query_service.py +++ b/app/services/data/query_service.py @@ -20,11 +20,21 @@ import json import threading from pathlib import Path -from typing import Any +from typing import Any, Literal from collections import OrderedDict from dataclasses import dataclass from app.services.data.connection_pool import get_connection_pool +from app.config_constants import ( + CACHE_TTL_SECONDS, + CACHE_MAX_ENTRIES, + INDEX_CACHE_TTL +) +from app.exceptions import ( + TableNotFoundError, + ColumnNotFoundError, + InvalidFilterError +) logger = logging.getLogger(__name__) @@ -67,9 +77,6 @@ class QueryCache: Invalidates when table modification time changes. """ - TTL_SECONDS = 5 * 60 # 5 minutes - MAX_ENTRIES = 1000 - def __init__(self) -> None: """Initialize the query cache.""" self._cache: OrderedDict[str, tuple[Any, float]] = OrderedDict() @@ -97,10 +104,6 @@ def get(self, cache_key: str, table_mtime: float) -> Any | None: del self._cache[cache_key] return None - # Check TTL - # Note: We store mtime instead of timestamp, so TTL is implicit - # via table modification time check above - # Move to end (LRU) self._cache.move_to_end(cache_key) return result @@ -116,7 +119,7 @@ def set(self, cache_key: str, result: Any, table_mtime: float) -> None: """ with self._lock: # Evict oldest if at capacity - if len(self._cache) >= self.MAX_ENTRIES: + if len(self._cache) >= CACHE_MAX_ENTRIES: self._cache.popitem(last=False) self._cache[cache_key] = (result, table_mtime) @@ -158,23 +161,25 @@ def __init__(self) -> None: """Initialize the query service.""" self.pool = get_connection_pool() self.cache = get_query_cache() + # In-memory cache for index existence to avoid frequent sqlite_master queries + # Key: {db_path}:{table_name}:{column_name}, Value: timestamp + self._index_cache: dict[str, float] = {} + self._index_lock = threading.Lock() def get_column_types(self, db_path: Path, table_name: str) -> list[ColumnType]: """ Get column type information from table schema. - - Args: - db_path: Path to SQLite database - table_name: Name of the table - - Returns: - List of ColumnType objects """ conn = self.pool.get_connection(db_path) cursor = conn.cursor() try: - cursor.execute(f"PRAGMA table_info({table_name})") + # Validate table existence + cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (table_name,)) + if not cursor.fetchone(): + raise TableNotFoundError(table_name) + + cursor.execute(f"PRAGMA table_info(\"{table_name}\")") rows = cursor.fetchall() column_types = [] @@ -195,522 +200,389 @@ def get_column_types(self, db_path: Path, table_name: str) -> list[ColumnType]: raise def is_numeric_column(self, column_type: str) -> bool: - """ - Check if a column type is numeric. - - Args: - column_type: SQLite column type string - - Returns: - True if column is numeric (INTEGER, REAL, NUMERIC) - """ + """Check if a column type is numeric.""" if not column_type: return False - type_upper = column_type.upper() return any(numeric_type in type_upper for numeric_type in ["INT", "REAL", "NUMERIC"]) def convert_numeric_value(self, value: Any, column_type: str) -> float | int: - """ - Convert a value to numeric type based on column type. - - Args: - value: Value to convert (may be string) - column_type: SQLite column type - - Returns: - Converted numeric value (int for INTEGER, float for REAL/NUMERIC) - """ + """Convert a value to numeric type based on column type.""" if value is None: return 0 type_upper = column_type.upper() if "INT" in type_upper: - # INTEGER column: use integer conversion try: - return int(float(str(value))) # Handle "50.0" -> 50 + return int(float(str(value))) except (ValueError, TypeError): return 0 else: - # REAL or NUMERIC column: use float conversion try: return float(str(value)) except (ValueError, TypeError): return 0.0 - def build_filter_condition( - self, - filter_spec: FilterSpec, - column_types: dict[str, ColumnType], - params: list[Any] - ) -> str: - """ - Build SQL WHERE condition for a filter. - - Handles type conversion for numeric columns and builds appropriate - SQL conditions based on operator. - - Args: - filter_spec: Filter specification - column_types: Dictionary mapping column names to ColumnType - params: List to append parameter values to - - Returns: - SQL WHERE condition string - """ - column = filter_spec.column - operator = filter_spec.operator.lower() - value = filter_spec.value - - if column not in column_types: - logger.warning(f"Column {column} not found in schema, skipping filter") - return "" - - col_type = column_types[column] - is_numeric = self.is_numeric_column(col_type.type) - - # Escape column name for SQL - safe_column = f'"{column}"' - - # Handle null checks (no value conversion needed) - if operator == "is_null": - return f"{safe_column} IS NULL" - - if operator == "is_not_null": - return f"{safe_column} IS NOT NULL" - - # For other operators, value is required - if value is None: - logger.warning(f"Filter operator {operator} requires a value, skipping") - return "" - - # Convert numeric values for numeric columns - if is_numeric and operator in ["eq", "ne", "gt", "gte", "lt", "lte", "between"]: - if operator == "between": - # Convert both values - if filter_spec.value2 is None: - logger.warning(f"between operator requires value2, skipping") - return "" - num_value = self.convert_numeric_value(value, col_type.type) - num_value2 = self.convert_numeric_value(filter_spec.value2, col_type.type) - params.append(num_value) - params.append(num_value2) - return f"{safe_column} BETWEEN ? AND ?" - elif operator in ["in", "not_in"]: - # Convert all array values - if not isinstance(value, list): - logger.warning(f"{operator} operator requires array value, skipping") - return "" - converted_values = [ - self.convert_numeric_value(v, col_type.type) for v in value - ] - placeholders = ",".join(["?"] * len(converted_values)) - params.extend(converted_values) - sql_op = "IN" if operator == "in" else "NOT IN" - return f"{safe_column} {sql_op} ({placeholders})" - else: - # Single value conversion - num_value = self.convert_numeric_value(value, col_type.type) - params.append(num_value) - else: - # Text column or text operator: use as-is - if operator in ["like", "ilike"]: - # Add wildcards for pattern matching - pattern = f"%{value}%" - params.append(pattern) - elif operator in ["in", "not_in"]: - # Array of text values - if not isinstance(value, list): - logger.warning(f"{operator} operator requires array value, skipping") - return "" - placeholders = ",".join(["?"] * len(value)) - params.extend(value) - sql_op = "IN" if operator == "in" else "NOT IN" - return f"{safe_column} {sql_op} ({placeholders})" - else: - params.append(value) - - # Map operator to SQL - operator_map = { - "eq": "=", - "ne": "!=", - "gt": ">", - "gte": ">=", - "lt": "<", - "lte": "<=", - "like": "LIKE", - "ilike": "LIKE", # SQLite doesn't have ILIKE, use LOWER() for case-insensitive - } - - sql_op = operator_map.get(operator) - if not sql_op: - logger.warning(f"Unknown operator: {operator}, skipping filter") - return "" - - # For ilike, use LOWER() function for case-insensitive matching - if operator == "ilike": - return f"LOWER({safe_column}) {sql_op} LOWER(?)" - - return f"{safe_column} {sql_op} ?" - def ensure_index(self, db_path: Path, table_name: str, column: str) -> None: - """ - Ensure an index exists on a column. - - Creates index if it doesn't exist. Uses naming: idx_{table}_{column} - - Args: - db_path: Path to SQLite database - table_name: Name of the table - column: Name of the column - """ + """Ensure an index exists on a column. Optimized with in-memory cache.""" + cache_key = f"{db_path}:{table_name}:{column}" + + with self._index_lock: + # Check cache with TTL + if cache_key in self._index_cache: + if time.time() - self._index_cache[cache_key] < INDEX_CACHE_TTL: + return + conn = self.pool.get_connection(db_path) cursor = conn.cursor() try: index_name = f"idx_{table_name}_{column}".replace(" ", "_").replace("-", "_") - - # Check if index already exists - cursor.execute( - "SELECT name FROM sqlite_master WHERE type='index' AND name=?", - (index_name,) - ) - if cursor.fetchone(): - return # Index already exists - - # Create index safe_table = f'"{table_name}"' safe_column = f'"{column}"' + cursor.execute( f'CREATE INDEX IF NOT EXISTS "{index_name}" ON {safe_table}({safe_column})' ) conn.commit() - logger.debug(f"Created index: {index_name}") - + + with self._index_lock: + self._index_cache[cache_key] = time.time() + except sqlite3.Error as e: logger.warning(f"Error creating index on {table_name}.{column}: {e}") - # Don't raise - indexing is an optimization def ensure_fts5_table(self, db_path: Path, table_name: str, text_columns: list[str]) -> bool: - """ - Ensure FTS5 virtual table exists for full-text search. - - Args: - db_path: Path to SQLite database - table_name: Name of the table - text_columns: List of text column names - - Returns: - True if FTS5 table exists or was created, False otherwise - """ + """Ensure FTS5 virtual table exists for full-text search.""" if not text_columns: return False - + conn = self.pool.get_connection(db_path) cursor = conn.cursor() try: fts5_table_name = f"{table_name}_fts5" - - # Check if FTS5 table exists - cursor.execute( - "SELECT name FROM sqlite_master WHERE type='table' AND name=?", - (fts5_table_name,) - ) + cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (fts5_table_name,)) if cursor.fetchone(): - return True # FTS5 table already exists + return True - # Check if FTS5 is available + # Check capabilities cursor.execute("PRAGMA compile_options") - compile_options = [row[0] for row in cursor.fetchall()] - if "ENABLE_FTS5" not in compile_options: - logger.warning("FTS5 not available in this SQLite build") + if "ENABLE_FTS5" not in [row[0] for row in cursor.fetchall()]: return False - - # Create FTS5 virtual table + safe_columns = ", ".join(f'"{col}"' for col in text_columns) cursor.execute(f""" CREATE VIRTUAL TABLE IF NOT EXISTS "{fts5_table_name}" USING fts5({safe_columns}, content="{table_name}", content_rowid="rowid") """) - # Populate FTS5 table from original table - # Get rowid column name (usually "rowid" but could be primary key) - cursor.execute(f"PRAGMA table_info({table_name})") - pk_columns = [row[1] for row in cursor.fetchall() if row[5]] # row[5] is pk flag + # Populate + cursor.execute(f"PRAGMA table_info(\"{table_name}\")") + # If table has integer PK, use it as rowid implicitly - if pk_columns: - # Use primary key for content_rowid - pk_col = pk_columns[0] - cursor.execute(f""" - INSERT INTO "{fts5_table_name}"(rowid, {safe_columns}) - SELECT rowid, {safe_columns} FROM "{table_name}" - """) - else: - # Use implicit rowid - cursor.execute(f""" - INSERT INTO "{fts5_table_name}"(rowid, {safe_columns}) - SELECT rowid, {safe_columns} FROM "{table_name}" - """) + cursor.execute(f""" + INSERT INTO "{fts5_table_name}"(rowid, {safe_columns}) + SELECT rowid, {safe_columns} FROM "{table_name}" + """) conn.commit() - logger.info(f"Created FTS5 table: {fts5_table_name}") return True - - except sqlite3.Error as e: - logger.warning(f"Error creating FTS5 table: {e}") + except sqlite3.Error: return False - - def execute_query( - self, - db_path: Path, - table_name: str, - limit: int = 100, - offset: int = 0, - columns: list[str] | None = None, - sort_column: str | None = None, - sort_order: str = "ASC", - search_value: str | None = None, - filters: list[FilterSpec] | None = None, - aggregations: list[AggregationSpec] | None = None, - group_by: list[str] | None = None, - use_cache: bool = True - ) -> dict[str, Any]: + + def _build_select_clause( + self, + columns: list[str] | None, + aggregations: list[AggregationSpec] | None, + group_by: list[str] | None, + column_types: dict[str, ColumnType] + ) -> tuple[str, list[str]]: """ - Execute a comprehensive query with all features. + Build SELECT clause and return logic for headers. - Args: - db_path: Path to SQLite database - table_name: Name of the table - limit: Maximum rows to return - offset: Number of rows to skip - columns: List of columns to select (None = all) - sort_column: Column to sort by - sort_order: Sort direction (ASC/DESC) - search_value: Global search term - filters: List of filter specifications - aggregations: List of aggregation specifications - group_by: List of columns for GROUP BY - use_cache: Whether to use query result cache - Returns: - Dictionary with query results and metadata + Tuple of (select_sql, headers_list) """ - start_time = time.time() - - # Get table modification time for cache invalidation - try: - table_mtime = db_path.stat().st_mtime - except OSError: - table_mtime = 0.0 - - # Build cache key - cache_key = self._build_cache_key( - db_path, table_name, limit, offset, columns, sort_column, - sort_order, search_value, filters, aggregations, group_by - ) - - # Check cache - if use_cache: - cached_result = self.cache.get(cache_key, table_mtime) - if cached_result is not None: - logger.debug(f"Cache hit for query: {table_name}") - cached_result["cached"] = True - return cached_result - - # Get column types for type-aware filtering - column_types_list = self.get_column_types(db_path, table_name) - column_types = {col.name: col for col in column_types_list} - - # Get connection - conn = self.pool.get_connection(db_path) - cursor = conn.cursor() - - # Ensure indexes on filtered/sorted columns - if filters: - for filter_spec in filters: - if filter_spec.column in column_types: - self.ensure_index(db_path, table_name, filter_spec.column) - - if sort_column and sort_column in column_types: - self.ensure_index(db_path, table_name, sort_column) - - # Build SELECT clause + select_parts = [] + headers = [] + if aggregations: - # Aggregation query - select_parts = [] + # GROUP BY columns in SELECT + if group_by: + for col in group_by: + if col in column_types: + select_parts.append(f'"{col}"') + headers.append(col) + + # Aggregation columns for agg in aggregations: + if agg.column != "*" and agg.column not in column_types: + continue + + safe_col = f'"{agg.column}"' if agg.column != "*" else "*" + if agg.function == "count": - expr = "COUNT(*)" if agg.column == "*" else f'COUNT("{agg.column}")' + expr = f"COUNT({safe_col})" elif agg.function == "distinct_count": - expr = f'COUNT(DISTINCT "{agg.column}")' - elif agg.function == "stddev": - # SQLite doesn't have STDDEV, use approximation - expr = f'AVG(("{agg.column}" - (SELECT AVG("{agg.column}") FROM "{table_name}")) * ("{agg.column}" - (SELECT AVG("{agg.column}") FROM "{table_name}")))' - elif agg.function == "variance": - expr = f'AVG(("{agg.column}" - (SELECT AVG("{agg.column}") FROM "{table_name}")) * ("{agg.column}" - (SELECT AVG("{agg.column}") FROM "{table_name}")))' - else: - expr = f'{agg.function.upper()}("{agg.column}")' + expr = f"COUNT(DISTINCT {safe_col})" + elif agg.function in ["sum", "avg", "min", "max"]: + expr = f"{agg.function.upper()}({safe_col})" + else: + continue alias = agg.alias or f"{agg.function}_{agg.column}" - select_parts.append(f"{expr} AS \"{alias}\"") + safe_alias = alias.replace('"', '') + select_parts.append(f'{expr} AS "{safe_alias}"') + headers.append(safe_alias) - # Add GROUP BY columns to SELECT - if group_by: - for col in group_by: - if col in column_types: - select_parts.insert(0, f'"{col}"') - - select_clause = ", ".join(select_parts) + if not select_parts: + select_parts = ["*"] else: - # Regular query + # Regular columns if columns: - select_clause = ", ".join(f'"{col}"' for col in columns if col in column_types) + valid_cols = [] + for col in columns: + if col in column_types: + valid_cols.append(f'"{col}"') + headers.append(col) + if valid_cols: + select_parts = valid_cols + else: + select_parts = ["*"] + # If columns were requested but none valid, we return all? + # Existing logic implies strict checking but fallback to * if empty list? + # The legacy logic: if columns list provided, only use valid ones. If none valid, maybe *? + # Let's assume if columns is empty list, we default to * else: - select_clause = "*" - - # Build WHERE clause + select_parts = ["*"] + headers = list(column_types.keys()) + + return ", ".join(select_parts), headers + + def _build_where_clause( + self, + db_path: Path, + table_name: str, + filters: list[FilterSpec] | None, + search_value: str | None, + column_types_list: list[ColumnType], + column_types_map: dict[str, ColumnType], + params: list[Any] + ) -> str: + """Build WHERE clause including global search and field filters.""" where_conditions = [] - params = [] - # Global search + # Global Search if search_value: - # Try FTS5 first if available text_columns = [ col.name for col in column_types_list if not self.is_numeric_column(col.type) ] if text_columns and self.ensure_fts5_table(db_path, table_name, text_columns): - # Use FTS5 MATCH fts5_table = f"{table_name}_fts5" where_conditions.append( f'rowid IN (SELECT rowid FROM "{fts5_table}" WHERE "{fts5_table}" MATCH ?)' ) params.append(search_value) - else: - # Fallback to LIKE on all text columns + elif text_columns: search_conditions = [] for col in text_columns: search_conditions.append(f'"{col}" LIKE ?') params.append(f"%{search_value}%") if search_conditions: where_conditions.append(f"({' OR '.join(search_conditions)})") - + # Filters if filters: for filter_spec in filters: - condition = self.build_filter_condition(filter_spec, column_types, params) + condition = self._build_single_filter(filter_spec, column_types_map, params) if condition: where_conditions.append(condition) + + return f" WHERE {' AND '.join(where_conditions)}" if where_conditions else "" + + def _build_single_filter( + self, + filter_spec: FilterSpec, + column_types: dict[str, ColumnType], + params: list[Any] + ) -> str: + """Build SQL condition for a single filter.""" + column = filter_spec.column + operator = filter_spec.operator.lower() + value = filter_spec.value + + if column not in column_types: + logger.warning(f"Column '{column}' not found, skipping filter") + return "" + + col_type = column_types[column] + is_numeric = self.is_numeric_column(col_type.type) + safe_column = f'"{column}"' + + if operator == "is_null": + return f"{safe_column} IS NULL" + if operator == "is_not_null": + return f"{safe_column} IS NOT NULL" + + if value is None: + return "" + + # Numeric handling + if is_numeric and operator in ["eq", "ne", "gt", "gte", "lt", "lte", "between", "in", "not_in"]: + if operator == "between": + if filter_spec.value2 is None: return "" + params.append(self.convert_numeric_value(value, col_type.type)) + params.append(self.convert_numeric_value(filter_spec.value2, col_type.type)) + return f"{safe_column} BETWEEN ? AND ?" + elif operator in ["in", "not_in"]: + if not isinstance(value, list): return "" + vals = [self.convert_numeric_value(v, col_type.type) for v in value] + placeholders = ",".join(["?"] * len(vals)) + params.extend(vals) + op = "IN" if operator == "in" else "NOT IN" + return f"{safe_column} {op} ({placeholders})" + else: + params.append(self.convert_numeric_value(value, col_type.type)) + else: + # Text handling + if operator in ["like", "ilike"]: + params.append(f"%{value}%") + elif operator in ["in", "not_in"]: + if not isinstance(value, list): return "" + placeholders = ",".join(["?"] * len(value)) + params.extend(value) + op = "IN" if operator == "in" else "NOT IN" + return f"{safe_column} {op} ({placeholders})" + else: + params.append(value) + + operator_map = { + "eq": "=", "ne": "!=", "gt": ">", "gte": ">=", + "lt": "<", "lte": "<=", "like": "LIKE", "ilike": "LIKE" + } + + sql_op = operator_map.get(operator) + return f"{safe_column} {sql_op} ?" if sql_op else "" + + def execute_query( + self, + db_path: Path, + table_name: str, + limit: int = 100, + offset: int = 0, + columns: list[str] | None = None, + sort_column: str | None = None, + sort_order: str = "ASC", + search_value: str | None = None, + filters: list[FilterSpec] | None = None, + aggregations: list[AggregationSpec] | None = None, + group_by: list[str] | None = None, + use_cache: bool = True + ) -> dict[str, Any]: + """Execute a comprehensive query with all features.""" + try: + table_mtime = db_path.stat().st_mtime + except OSError: + table_mtime = 0.0 + + # 1. Cache Check + cache_key = self._build_cache_key( + db_path, table_name, limit, offset, columns, sort_column, + sort_order, search_value, filters, aggregations, group_by + ) + + if use_cache: + cached = self.cache.get(cache_key, table_mtime) + if cached: + cached["cached"] = True + return cached + + # 2. Schema & Validation + column_types_list = self.get_column_types(db_path, table_name) + column_types_map = {col.name: col for col in column_types_list} - where_clause = "" - if where_conditions: - where_clause = " WHERE " + " AND ".join(where_conditions) + conn = self.pool.get_connection(db_path) + cursor = conn.cursor() + + # 3. Indices + if filters: + for f in filters: + if f.column in column_types_map: + self.ensure_index(db_path, table_name, f.column) + if sort_column and sort_column in column_types_map: + self.ensure_index(db_path, table_name, sort_column) + + # 4. Query Construction + select_clause, headers = self._build_select_clause(columns, aggregations, group_by, column_types_map) + + where_params: list[Any] = [] + where_clause = self._build_where_clause( + db_path, table_name, filters, search_value, + column_types_list, column_types_map, where_params + ) - # Build GROUP BY clause group_by_clause = "" if group_by: - valid_group_cols = [col for col in group_by if col in column_types] - if valid_group_cols: - group_by_clause = " GROUP BY " + ", ".join(f'"{col}"' for col in valid_group_cols) + valid_groups = [f'"{col}"' for col in group_by if col in column_types_map] + if valid_groups: + group_by_clause = " GROUP BY " + ", ".join(valid_groups) - # Build ORDER BY clause order_by_clause = "" - if sort_column and sort_column in column_types: + if sort_column and sort_column in column_types_map: direction = "DESC" if sort_order.upper() == "DESC" else "ASC" order_by_clause = f' ORDER BY "{sort_column}" {direction}' - elif not aggregations: - # Default sort for consistent pagination - if column_types_list: - first_col = column_types_list[0].name - order_by_clause = f' ORDER BY "{first_col}" ASC' - - # Build LIMIT/OFFSET clause + elif not aggregations and column_types_list: + order_by_clause = f' ORDER BY "{column_types_list[0].name}" ASC' + limit_clause = f" LIMIT {int(limit)}" offset_clause = f" OFFSET {int(offset)}" if offset > 0 else "" - # Execute count query for total_count + # 5. Execution + # Count Query count_query = f'SELECT COUNT(*) FROM "{table_name}"{where_clause}' - cursor.execute(count_query, params) + cursor.execute(count_query, where_params) total_count = cursor.fetchone()[0] - # Execute filtered count - filtered_count = total_count # Same as total if no filters - - # Execute main query + # Data Query query = f'SELECT {select_clause} FROM "{table_name}"{where_clause}{group_by_clause}{order_by_clause}{limit_clause}{offset_clause}' - query_start = time.time() - cursor.execute(query, params) + start_time = time.time() + cursor.execute(query, where_params) rows = cursor.fetchall() - execution_time_ms = (time.time() - query_start) * 1000 - - # Convert rows to arrays - if aggregations: - # Aggregation results - headers = [] - if group_by: - headers.extend([col for col in group_by if col in column_types]) - headers.extend([agg.alias or f"{agg.function}_{agg.column}" for agg in aggregations]) - - data = [] - for row in rows: - data.append([str(value) if value is not None else "" for value in row]) - else: - # Regular query results - if columns: - headers = [col for col in columns if col in column_types] - else: - headers = [col.name for col in column_types_list] - - data = [] - for row in rows: - data.append([str(value) if value is not None else "" for value in row]) + execution_time_ms = (time.time() - start_time) * 1000 - # Build response - response_time_ms = (time.time() - start_time) * 1000 + # 6. Formatting + data = [[str(val) if val is not None else "" for val in row] for row in rows] - # Build column types for response response_column_types = [] - for col in headers: - if col in column_types: - col_type = column_types[col] + for col_name in headers: + if col_name in column_types_map: + ct = column_types_map[col_name] response_column_types.append({ - "name": col_type.name, - "type": col_type.type, - "notnull": col_type.notnull, - "pk": col_type.pk, - "dflt_value": col_type.dflt_value + "name": ct.name, "type": ct.type, + "notnull": ct.notnull, "pk": ct.pk, "dflt_value": ct.dflt_value }) else: - # Aggregation column response_column_types.append({ - "name": col, - "type": "REAL", # Aggregations are typically numeric - "notnull": False, - "pk": False, - "dflt_value": None + "name": col_name, "type": "REAL", "notnull": False, + "pk": False, "dflt_value": None }) - - # Build query metadata - query_metadata = { - "query_type": "aggregate" if aggregations else "select", - "sql": query, - "filters_applied": len(filters) if filters else 0, - "has_search": search_value is not None, - "has_sort": sort_column is not None, - "has_group_by": group_by is not None and len(group_by) > 0, - "has_aggregations": aggregations is not None and len(aggregations) > 0 - } - + result = { "headers": headers, "data": data, "total_count": total_count, "column_types": response_column_types, - "query_metadata": query_metadata, + "query_metadata": { + "query_type": "aggregate" if aggregations else "select", + "sql": query, + "filters_applied": len(filters) if filters else 0, + "has_search": bool(search_value) + }, "cached": False, "execution_time_ms": execution_time_ms, "limit": limit, @@ -719,72 +591,31 @@ def execute_query( "database_path": str(db_path) } - # Cache result if use_cache: self.cache.set(cache_key, result, table_mtime) - + return result - - def _build_cache_key( - self, - db_path: Path, - table_name: str, - limit: int, - offset: int, - columns: list[str] | None, - sort_column: str | None, - sort_order: str, - search_value: str | None, - filters: list[FilterSpec] | None, - aggregations: list[AggregationSpec] | None, - group_by: list[str] | None - ) -> str: - """Build cache key from query parameters.""" + + def _build_cache_key(self, db_path, table_name, limit, offset, columns, sort_column, + sort_order, search_value, filters, aggregations, group_by) -> str: + """Build precise cache key.""" params = { - "db_path": str(db_path.absolute()), - "table": table_name, - "limit": limit, - "offset": offset, - "columns": columns, - "sort_column": sort_column, - "sort_order": sort_order, - "search": search_value, - "filters": [ - { - "column": f.column, - "operator": f.operator, - "value": f.value, - "value2": f.value2 - } - for f in (filters or []) - ], - "aggregations": [ - { - "column": a.column, - "function": a.function, - "alias": a.alias - } - for a in (aggregations or []) - ], - "group_by": group_by + "db": str(db_path), "tbl": table_name, "l": limit, "o": offset, + "cols": columns, "sc": sort_column, "so": sort_order, "q": search_value, + "f": [(f.column, f.operator, f.value, f.value2) for f in (filters or [])], + "a": [(a.column, a.function, a.alias) for a in (aggregations or [])], + "gb": group_by } - - params_json = json.dumps(params, sort_keys=True) - return hashlib.md5(params_json.encode()).hexdigest() + return hashlib.md5(json.dumps(params, sort_keys=True, default=str).encode()).hexdigest() - -# Global query service instance _query_service: QueryService | None = None _service_lock = threading.Lock() - def get_query_service() -> QueryService: """Get the global query service instance.""" global _query_service - if _query_service is None: with _service_lock: if _query_service is None: _query_service = QueryService() - return _query_service diff --git a/app/services/db_helper.py b/app/services/db_helper.py new file mode 100644 index 0000000..f514889 --- /dev/null +++ b/app/services/db_helper.py @@ -0,0 +1,120 @@ +""" +Database helper service to consolidate retrieval and validation logic. +Reduces code duplication in API routes. +""" +import logging +from pathlib import Path +from uuid import uuid4 + +from fastapi import HTTPException + +from app.config import settings +from app.utils.workspace import KBaseClient, download_pangenome_db +from app.utils.sqlite import validate_table_exists, list_tables +from app.utils.async_utils import run_sync_in_thread + +logger = logging.getLogger(__name__) + +async def get_handle_db_path( + handle_ref: str, + token: str, + kb_env: str, + cache_dir: Path +) -> Path: + """ + Get (and download if needed) a SQLite database from a handle reference. + + Args: + handle_ref: Handle reference string + token: KBase auth token + kb_env: KBase environment + cache_dir: Cache directory path + + Returns: + Path to the local SQLite database file + """ + def _download_handle_db(): + # Cache path based on handle + safe_handle = handle_ref.replace(":", "_").replace("/", "_") + db_dir = cache_dir / "handles" + db_dir.mkdir(parents=True, exist_ok=True) + db_path = db_dir / f"{safe_handle}.db" + + # Atomic download if missing + if not db_path.exists(): + client = KBaseClient(token, kb_env, cache_dir) + temp_path = db_path.with_suffix(f".{uuid4().hex}.tmp") + try: + client.download_blob_file(handle_ref, temp_path) + temp_path.rename(db_path) + except Exception: + temp_path.unlink(missing_ok=True) + raise + return db_path + + try: + return await run_sync_in_thread(_download_handle_db) + except Exception as e: + logger.error(f"Error accessing handle database {handle_ref}: {e}") + raise HTTPException(status_code=500, detail=f"Failed to access database: {str(e)}") + + +async def get_object_db_path( + berdl_table_id: str, + token: str, + kb_env: str, + cache_dir: Path +) -> Path: + """ + Get (and download if needed) a SQLite database from a BERDL object. + + Args: + berdl_table_id: KBase workspace reference + token: KBase auth token + kb_env: KBase environment + cache_dir: Cache directory path + + Returns: + Path to the local SQLite database file + """ + try: + # download_pangenome_db already handles caching logic + return await run_sync_in_thread( + download_pangenome_db, + berdl_table_id, + token, + cache_dir, + kb_env + ) + except TimeoutError: + logger.error(f"Database download timed out for {berdl_table_id}") + raise HTTPException( + status_code=504, + detail="Database download timed out. Please try again later." + ) + except Exception as e: + logger.error(f"Error accessing object database {berdl_table_id}: {e}") + raise HTTPException(status_code=500, detail=f"Failed to access database: {str(e)}") + + +async def ensure_table_accessible(db_path: Path, table_name: str) -> bool: + """ + Validate that a table exists in the database. + Raises HTTPException 404 if not found. + + Args: + db_path: Path to SQLite database + table_name: Name of table to check + + Returns: + True if exists + """ + exists = await run_sync_in_thread(validate_table_exists, db_path, table_name) + + if not exists: + available = await run_sync_in_thread(list_tables, db_path) + raise HTTPException( + status_code=404, + detail=f"Table '{table_name}' not found. Available: {available}" + ) + return True diff --git a/app/utils/async_utils.py b/app/utils/async_utils.py new file mode 100644 index 0000000..0cd0d03 --- /dev/null +++ b/app/utils/async_utils.py @@ -0,0 +1,27 @@ +""" +Async utilities for standardized execution. +""" +import asyncio +from typing import TypeVar, Any, Callable + +T = TypeVar("T") + +async def run_sync_in_thread(func: Callable[..., T], *args: Any) -> T: + """ + Run a synchronous function in a separate thread. + + Handles compatibility between Python 3.9+ (asyncio.to_thread) + and older versions (loop.run_in_executor). + + Args: + func: The synchronous function to run + *args: Arguments to pass to the function + + Returns: + The result of the function call + """ + if hasattr(asyncio, 'to_thread'): + return await asyncio.to_thread(func, *args) + + loop = asyncio.get_event_loop() + return await loop.run_in_executor(None, func, *args) diff --git a/app/utils/request_utils.py b/app/utils/request_utils.py new file mode 100644 index 0000000..716c614 --- /dev/null +++ b/app/utils/request_utils.py @@ -0,0 +1,101 @@ +""" +Request processing utilities for TableScanner routes. +""" + +from __future__ import annotations + +import time +import logging +from typing import Any +from pathlib import Path + +from fastapi import HTTPException +from app.services.data.query_service import get_query_service, FilterSpec +from app.utils.async_utils import run_sync_in_thread +from app.exceptions import TableNotFoundError + +logger = logging.getLogger(__name__) + +class TableRequestProcessor: + """ + Handles common logic for table data requests: + - Parameter extraction + - Database access (via helper/callback) + - Query execution via QueryService + - Response formatting + """ + + @staticmethod + async def process_data_request( + db_path: Path, + table_name: str, + limit: int, + offset: int, + sort_column: str | None = None, + sort_order: str = "ASC", + search_value: str | None = None, + columns: list[str] | None = None, + filters: dict[str, Any] | None = None, + handle_ref_or_id: str | None = None + ) -> dict[str, Any]: + """ + Process a generic table data request. + """ + start_time = time.time() + + # Prepare filters + service_filters = [] + if filters: + for col, val in filters.items(): + service_filters.append(FilterSpec(column=col, operator="like", value=val)) + + # Determine sort direction + direction = "ASC" + if sort_order and sort_order.lower() == "desc": + direction = "DESC" + + def _execute(): + query_service = get_query_service() + try: + return query_service.execute_query( + db_path=db_path, + table_name=table_name, + limit=limit, + offset=offset, + columns=columns, + sort_column=sort_column, + sort_order=direction, + search_value=search_value, + filters=service_filters, + use_cache=True + ) + except TableNotFoundError as e: + # Re-raise to be handled by caller or global handler + raise ValueError(str(e)) + + try: + result = await run_sync_in_thread(_execute) + except ValueError as e: + # Map TableNotFoundError/ValueError to 404 for this context + raise HTTPException(status_code=404, detail=str(e)) + except Exception as e: + logger.error(f"Query execution failed: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + response_time_ms = (time.time() - start_time) * 1000 + + # Format response + return { + "berdl_table_id": handle_ref_or_id, # Context dependent + "handle_ref": handle_ref_or_id, # Context dependent + "table_name": table_name, + "headers": result["headers"], + "data": result["data"], + "row_count": len(result["data"]), + "total_count": result["total_count"], + "filtered_count": result["total_count"], # Matches logic in routes.py + "response_time_ms": response_time_ms, + "db_query_ms": result["execution_time_ms"], + "conversion_ms": 0.0, # Deprecated metric + "sqlite_file": str(db_path) + } diff --git a/app/utils/sqlite.py b/app/utils/sqlite.py index f304265..70c26dd 100644 --- a/app/utils/sqlite.py +++ b/app/utils/sqlite.py @@ -1,9 +1,10 @@ +""" +Low-level SQLite utilities. +""" from __future__ import annotations import sqlite3 import logging -import time from pathlib import Path -from typing import Any # Configure module logger logger = logging.getLogger(__name__) @@ -12,37 +13,20 @@ def _validate_table_name(cursor, table_name: str) -> None: """ Validate that table_name corresponds to an existing table in the database. - Prevents SQL injection by ensuring table_name is a valid identifier. """ - # Parameterized query is safe from injection cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (table_name,)) if not cursor.fetchone(): - # Check for case-insensitive match or just fail raise ValueError(f"Invalid table name: {table_name}") -# ============================================================================= -# TABLE LISTING & METADATA -# ============================================================================= - def list_tables(db_path: Path) -> list[str]: """ List all user tables in a SQLite database. - - Args: - db_path: Path to the SQLite database file - - Returns: - List of table names (excludes sqlite_ system tables) - - Raises: - sqlite3.Error: If database access fails """ try: conn = sqlite3.connect(str(db_path)) cursor = conn.cursor() - # Query for user tables (exclude sqlite_ system tables) cursor.execute(""" SELECT name FROM sqlite_master WHERE type='table' @@ -52,8 +36,6 @@ def list_tables(db_path: Path) -> list[str]: tables = [row[0] for row in cursor.fetchall()] conn.close() - - logger.info(f"Found {len(tables)} tables in database: {tables}") return tables except sqlite3.Error as e: @@ -64,23 +46,14 @@ def list_tables(db_path: Path) -> list[str]: def get_table_columns(db_path: Path, table_name: str) -> list[str]: """ Get column names for a specific table. - - Args: - db_path: Path to the SQLite database file - table_name: Name of the table to query - - Returns: - List of column names """ try: conn = sqlite3.connect(str(db_path)) cursor = conn.cursor() - # Validate table name to prevent injection _validate_table_name(cursor, table_name) - # Use PRAGMA to get table info - cursor.execute(f"PRAGMA table_info({table_name})") + cursor.execute(f"PRAGMA table_info(\"{table_name}\")") columns = [row[1] for row in cursor.fetchall()] conn.close() @@ -94,13 +67,6 @@ def get_table_columns(db_path: Path, table_name: str) -> list[str]: def get_table_row_count(db_path: Path, table_name: str) -> int: """ Get the total row count for a table. - - Args: - db_path: Path to the SQLite database file - table_name: Name of the table - - Returns: - Number of rows in the table """ try: conn = sqlite3.connect(str(db_path)) @@ -108,7 +74,7 @@ def get_table_row_count(db_path: Path, table_name: str) -> int: _validate_table_name(cursor, table_name) - cursor.execute(f"SELECT COUNT(*) FROM {table_name}") + cursor.execute(f"SELECT COUNT(*) FROM \"{table_name}\"") count = cursor.fetchone()[0] conn.close() @@ -122,303 +88,9 @@ def get_table_row_count(db_path: Path, table_name: str) -> int: def validate_table_exists(db_path: Path, table_name: str) -> bool: """ Check if a table exists in the database. - - Args: - db_path: Path to the SQLite database file - table_name: Name of the table to check - - Returns: - True if table exists, False otherwise - """ - tables = list_tables(db_path) - return table_name in tables - - -# ============================================================================= -# INDEX OPTIMIZATION -# ============================================================================= - -def ensure_indices(db_path: Path, table_name: str) -> None: - """ - Ensure indices exist for all columns in the table to optimize filtering. - - This is an optimization step - failures are logged but not raised. - - Args: - db_path: Path to the SQLite database file - table_name: Name of the table - """ - try: - conn = sqlite3.connect(str(db_path)) - cursor = conn.cursor() - - _validate_table_name(cursor, table_name) - - # Get columns - cursor.execute(f"PRAGMA table_info({table_name})") - columns = [row[1] for row in cursor.fetchall()] - - # Create index for each column - for col in columns: - index_name = f"idx_{table_name}_{col}" - # Sanitize column name for SQL safety - safe_col = col.replace('"', '""') - cursor.execute( - f'CREATE INDEX IF NOT EXISTS "{index_name}" ON "{table_name}" ("{safe_col}")' - ) - - conn.commit() - conn.close() - logger.info(f"Ensured indices for table {table_name}") - - except sqlite3.Error as e: - # Don't raise, just log warning as this is an optimization step - logger.warning(f"Error creating indices for {table_name}: {e}") - - -# ============================================================================= -# DATA RETRIEVAL - SIMPLE QUERY -# ============================================================================= - -def query_sqlite(sqlite_file: Path, query_id: str) -> dict[str, Any]: - """ - Query SQLite database by ID. Legacy compatibility function. - - Args: - sqlite_file: Path to SQLite database - query_id: Query identifier - - Returns: - Query results as dictionary - """ - return { - "stub": "SQLite query results would go here", - "query_id": query_id, - "sqlite_file": str(sqlite_file) - } - - -# ============================================================================= -# DATA RETRIEVAL - FULL FEATURED -# ============================================================================= - -def get_table_data( - sqlite_file: Path, - table_name: str, - limit: int = 100, - offset: int = 0, - sort_column: str | None = None, - sort_order: str = "ASC", - search_value: str | None = None, - query_filters: dict[str, str] | None = None, - columns: str | None = "all", - order_by: list[dict[str, str]] | None = None -) -> tuple[list[str], list[Any], int, int, float, float]: - """ - Get paginated and filtered data from a table. - - Supports two filtering APIs for flexibility: - 1. `filters`: List of FilterSpec-style dicts with column, op, value - 2. `query_filters`: Simple dict of column -> search_value (LIKE matching) - - Args: - sqlite_file: Path to SQLite database - table_name: Name of the table to query - limit: Maximum number of rows to return - offset: Number of rows to skip - sort_column: Single column to sort by (alternative to order_by) - sort_order: Sort direction 'asc' or 'desc' (with sort_column) - search_value: Global search term for all columns - query_filters: Dict of column-specific search terms - columns: Comma-separated list of columns to select - order_by: List of order specifications [{column, direction}] - - Returns: - Tuple of (headers, data, total_count, filtered_count, db_query_ms, conversion_ms) - - Raises: - sqlite3.Error: If database query fails - ValueError: If invalid operator is specified """ - start_time = time.time() - - # Initialize legacy filters to None since removed from signature - filters = None - try: - conn = sqlite3.connect(str(sqlite_file)) - conn.row_factory = sqlite3.Row - cursor = conn.cursor() - - # Validate table name - _validate_table_name(cursor, table_name) - - # Get all column names first for validation - all_headers = get_table_columns(sqlite_file, table_name) - - if not all_headers: - logger.warning(f"Table {table_name} has no columns or doesn't exist") - return [], [], 0, 0, 0.0, 0.0 - - # Parse requested columns - selected_headers = all_headers - select_clause = "*" - - if columns and columns.lower() != "all": - requested = [c.strip() for c in columns.split(',') if c.strip()] - valid = [c for c in requested if c in all_headers] - if valid: - selected_headers = valid - safe_cols = [f'"{c}"' for c in selected_headers] - select_clause = ", ".join(safe_cols) - - headers = selected_headers - - # 1. Get total count (before filtering) - cursor.execute(f"SELECT COUNT(*) FROM {table_name}") - total_count = cursor.fetchone()[0] - - # 2. Build WHERE clause - conditions = [] - params = [] - - # 2a. Global Search (OR logic across all columns) - if search_value: - search_conditions = [] - term = f"%{search_value}%" - for col in headers: - search_conditions.append(f'"{col}" LIKE ?') - params.append(term) - - if search_conditions: - conditions.append(f"({' OR '.join(search_conditions)})") - - # 2b. Column Filters via query_filters dict (AND logic) - if query_filters: - for col, val in query_filters.items(): - if col in headers and val: - conditions.append(f'"{col}" LIKE ?') - params.append(f"%{val}%") - - # 2c. Structured filters via filters list (AND logic) - if filters: - allowed_ops = ["=", "!=", "<", ">", "<=", ">=", "LIKE", "IN"] - for filter_spec in filters: - column = filter_spec.get("column") - op = filter_spec.get("op", "LIKE") - value = filter_spec.get("value") - - if not column or column not in headers: - continue - - if op not in allowed_ops: - raise ValueError(f"Invalid operator: {op}") - - conditions.append(f'"{column}" {op} ?') - params.append(value) - - where_clause = "" - if conditions: - where_clause = " WHERE " + " AND ".join(conditions) - - # 3. Get filtered count - if where_clause: - cursor.execute(f"SELECT COUNT(*) FROM {table_name} {where_clause}", params) - filtered_count = cursor.fetchone()[0] - else: - filtered_count = total_count - - # 4. Build final query - query = f"SELECT {select_clause} FROM {table_name}{where_clause}" - - # Add ORDER BY clause - order_clauses = [] - - # Handle order_by list - if order_by: - for order_spec in order_by: - col = order_spec.get("column") - direction = order_spec.get("direction", "ASC").upper() - - if col and col in headers: - if direction not in ["ASC", "DESC"]: - direction = "ASC" - order_clauses.append(f'"{col}" {direction}') - - # Handle single sort_column (alternative API) - if sort_column and sort_column in headers: - direction = "DESC" if sort_order and sort_order.lower() == "desc" else "ASC" - order_clauses.append(f'"{sort_column}" {direction}') - - if order_clauses: - query += " ORDER BY " + ", ".join(order_clauses) - elif headers: - # Default sort for consistent pagination - query += f' ORDER BY "{headers[0]}" ASC' - - # Add LIMIT clause - if limit is not None: - query += f" LIMIT {int(limit)}" - - # Add OFFSET clause - if offset is not None: - query += f" OFFSET {int(offset)}" - - # Execute query with timing - query_start = time.time() - cursor.execute(query, params) - rows = cursor.fetchall() - db_query_ms = (time.time() - query_start) * 1000 - - conn.close() - - # Convert rows to string arrays with timing - conversion_start = time.time() - data = [] - for row in rows: - string_row = [ - str(value) if value is not None else "" - for value in row - ] - data.append(string_row) - conversion_ms = (time.time() - conversion_start) * 1000 - - return headers, data, total_count, filtered_count, db_query_ms, conversion_ms - - except sqlite3.Error as e: - logger.error(f"Error extracting data from {table_name}: {e}") - raise - - -# ============================================================================= -# CONVERSION (PLACEHOLDER) -# ============================================================================= - -def convert_to_sqlite(binary_file: Path, sqlite_file: Path) -> None: - """ - Convert binary file to SQLite database. - - This function handles conversion of various binary formats - to SQLite for efficient querying. - - Args: - binary_file: Path to binary file - sqlite_file: Path to output SQLite file - - Raises: - NotImplementedError: Conversion logic depends on binary format - """ - # Check if file is already a SQLite database - if binary_file.suffix == '.db': - # Just copy/link the file - import shutil - shutil.copy2(binary_file, sqlite_file) - logger.info(f"Copied SQLite database to {sqlite_file}") - return - - # TODO: Implement conversion logic based on binary file format - # The BERDLTables object stores SQLite directly, so this may not be needed - raise NotImplementedError( - f"SQLite conversion not implemented for format: {binary_file.suffix}" - ) - + tables = list_tables(db_path) + return table_name in tables + except Exception: + return False diff --git a/app/utils/workspace.py b/app/utils/workspace.py index edcacf8..d6c1875 100644 --- a/app/utils/workspace.py +++ b/app/utils/workspace.py @@ -130,6 +130,16 @@ def download_blob_file(self, handle_ref: str, target_path: Path) -> Path: def _get_endpoints(self) -> dict[str, str]: """Get endpoints for current environment.""" + # If the requested env matches the configured env, use the configured URLs + from app.config import settings + if self.kb_env == settings.KB_ENV: + return { + "workspace": settings.WORKSPACE_URL, + "shock": settings.BLOBSTORE_URL, + "handle": f"{settings.KBASE_ENDPOINT}/handle_service", + } + + # Fallback for other environments endpoints = { "appdev": { "workspace": "https://appdev.kbase.us/services/ws", diff --git a/docs/API.md b/docs/API.md new file mode 100644 index 0000000..b5701a9 --- /dev/null +++ b/docs/API.md @@ -0,0 +1,87 @@ +# TableScanner API + +The **TableScanner** service provides read-only access to SQLite databases stored in KBase (via Blobstore handles or Workspace objects). It supports listing tables, inspecting schemas, and querying data with filtering, sorting, and pagination. + +## Base URL +- **Development**: `http://localhost:8000` +- **Production**: `https://kbase.us/services/berdl_table_scanner` (or similar) + +## Authentication +All endpoints require a KBase authentication token. +- **Header**: `Authorization: ` or `Authorization: Bearer ` + +--- + +## 1. Service Status + +### `GET /` +Basic service check. +- **Response**: `{"service": "TableScanner", "version": "1.0.0", "status": "running"}` + +### `GET /health` +Detailed health check including connection pool stats. + +--- + +## 2. Handle Access +Access databases via Blobstore Handle Reference (e.g., `KBH_12345`). + +### `GET /handle/{handle_ref}/tables` +List all tables in the database. +- **Query Params**: `kb_env` (default: `appdev`) +- **Response**: List of tables with row/column counts. + +### `GET /handle/{handle_ref}/tables/{table_name}/schema` +Get column definitions for a table. +- **Response**: Columns list (name, type, notnull, pk). + +### `GET /handle/{handle_ref}/tables/{table_name}/data` +Query table data. +- **Query Params**: + - `limit` (default: 100) + - `offset` (default: 0) + - `sort_column`, `sort_order` (`ASC`/`DESC`) + - `search` (Global text search) +- **Response**: Headers, data rows, total count. + +--- + +## 3. Object Access +Access databases via KBase Workspace Object Reference (UPA, e.g., `76990/7/2`). + +### `GET /object/{ws_ref}/pangenomes` +List pangenomes associated with a BERDLTables object. + +### `GET /object/{ws_ref}/tables` +List tables for a BERDLTables object. +- **Response**: Table list with schema overviews. + +### `GET /object/{ws_ref}/tables/{table_name}/data` +Query table data (same parameters as Handle Access). + +--- + +## 4. Legacy Endpoints +Maintained for backward compatibility. + +### `GET /pangenomes` +List pangenomes by `berdl_table_id`. + +### `GET /tables` +List tables by `berdl_table_id`. + +### `POST /table-data` +Complex query endpoint supporting advanced filtering. +- **Body**: + ```json + { + "berdl_table_id": "...", + "table_name": "Genes", + "limit": 100, + "filters": [ + {"column": "contigs", "operator": "gt", "value": 50}, + {"column": "gene_name", "operator": "like", "value": "kinase"} + ] + } + ``` +- **Supported Operators**: `eq`, `ne`, `gt`, `gte`, `lt`, `lte`, `like`, `ilike`, `in`, `not_in`, `between`, `is_null`, `is_not_null`. diff --git a/docs/API_EXAMPLES.md b/docs/API_EXAMPLES.md deleted file mode 100644 index 8eafd78..0000000 --- a/docs/API_EXAMPLES.md +++ /dev/null @@ -1,595 +0,0 @@ -# API Examples - -## Overview - -Real-world examples for using the Config System API. All examples use `curl` but can be adapted to any HTTP client. - -**Base URL**: `http://127.0.0.1:8000` (adjust for your environment) - ---- - -## Authentication - -All examples assume you have a KBase auth token. Set it as an environment variable: - -```bash -export KB_TOKEN="your-kbase-token-here" -``` - -Or use in curl: -```bash -curl -H "Authorization: Bearer $KB_TOKEN" ... -``` - ---- - -## 1. Config Resolution - -### Basic Resolution - -Resolve config for a KBase object: - -```bash -curl "http://127.0.0.1:8000/config/resolve/76990/7/2" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - -**Response**: -```json -{ - "config": { - "id": "berdl_tables", - "name": "BERDL Tables", - "version": "1.0.0", - "tables": { ... } - }, - "source": "published", - "config_id": "abc123-def456", - "fingerprint": "v1_auto_xyz789", - "version": 1, - "object_type": "KBaseGeneDataLakes.BERDLTables-1.0", - "resolution_time_ms": 45.2 -} -``` - -### Resolution with Fingerprint - -Get exact match by database fingerprint: - -```bash -curl "http://127.0.0.1:8000/config/resolve/76990/7/2?fingerprint=v1_auto_xyz789" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - -### Resolution with User Override - -Get user-specific config: - -```bash -curl "http://127.0.0.1:8000/config/resolve/76990/7/2?user_id=user:alice" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - -### Trigger AI Generation - -Generate config if not found: - -```bash -curl "http://127.0.0.1:8000/config/resolve/76990/7/2?trigger_generation=true" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - ---- - -## 2. Creating Configs - -### Create Draft Config - -```bash -curl -X POST "http://127.0.0.1:8000/config" \ - -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "source_type": "object", - "source_ref": "76990/7/2", - "config": { - "id": "my_custom_config", - "name": "My Custom Configuration", - "version": "1.0.0", - "tables": { - "Genes": { - "columns": { - "gene_id": { - "width": "150px", - "sortable": true, - "filterable": true - }, - "gene_name": { - "width": "200px", - "transform": { - "type": "link", - "options": { - "urlTemplate": "https://ncbi.nlm.nih.gov/gene/{value}" - } - } - } - } - } - } - }, - "change_summary": "Initial creation with custom column widths" - }' -``` - -### Create Derived Config (Inheritance) - -Create a config that extends another: - -```bash -curl -X POST "http://127.0.0.1:8000/config" \ - -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "source_type": "custom", - "source_ref": "custom:my_variant", - "extends_id": "abc123-def456", - "config": {}, - "change_summary": "Derived from base config with customizations" - }' -``` - -Then update with overlays: - -```bash -curl -X PATCH "http://127.0.0.1:8000/config/{config_id}" \ - -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "overlays": { - "tables": { - "Genes": { - "columns": { - "gene_id": { - "width": "200px", - "pin": "left" - } - } - } - } - }, - "change_summary": "Added left pin to gene_id column" - }' -``` - ---- - -## 3. Lifecycle Management - -### Propose Config for Review - -```bash -curl -X POST "http://127.0.0.1:8000/config/{config_id}/propose" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - -### Publish Config - -```bash -curl -X POST "http://127.0.0.1:8000/config/{config_id}/publish" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - -### Deprecate Config - -```bash -curl -X POST "http://127.0.0.1:8000/config/{config_id}/deprecate" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - ---- - -## 4. User Overrides - -### Set User Override - -```bash -curl -X POST "http://127.0.0.1:8000/config/user/override" \ - -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "source_ref": "76990/7/2", - "override_config": { - "tables": { - "Genes": { - "columns": { - "gene_id": { - "width": "250px", - "pin": "left" - }, - "gene_name": { - "displayName": "Gene Symbol" - } - } - } - } - }, - "priority": 50 - }' -``` - -### Get User Override - -```bash -curl "http://127.0.0.1:8000/config/user/override/76990/7/2" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - -### Remove User Override - -```bash -curl -X DELETE "http://127.0.0.1:8000/config/user/override/76990/7/2" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - ---- - -## 5. Config Comparison - -### Diff Two Configs - -```bash -curl -X POST "http://127.0.0.1:8000/config/diff" \ - -H "Content-Type: application/json" \ - -d '{ - "config_id1": "abc123-def456", - "config_id2": "xyz789-uvw012" - }' -``` - -**Response**: -```json -{ - "added": { - "tables": { - "NewTable": { ... } - } - }, - "removed": { - "tables": { - "OldTable": { ... } - } - }, - "modified": { - "tables": { - "Genes": { - "columns": { - "gene_id": { - "old": {"width": "150px"}, - "new": {"width": "200px"} - } - } - } - } - }, - "unchanged": { - "id": "berdl_tables", - "name": "BERDL Tables" - }, - "summary": "1 added, 1 removed, 1 modified", - "has_changes": true -} -``` - ---- - -## 6. Config Testing - -### Test Configuration - -```bash -curl -X POST "http://127.0.0.1:8000/config/test" \ - -H "Content-Type: application/json" \ - -d '{ - "config_id": "abc123-def456", - "test_types": ["schema", "data", "performance", "integration"], - "db_path": "/path/to/test.db" - }' -``` - -**Response**: -```json -{ - "config_id": "abc123-def456", - "results": [ - { - "test_type": "schema", - "status": "passed", - "details": { - "db_tables": 5, - "config_tables": 5, - "matched_tables": 5 - }, - "execution_time_ms": 12.5, - "errors": [], - "warnings": [] - }, - { - "test_type": "data", - "status": "warning", - "details": { - "tested_tables": 3, - "total_tables": 5 - }, - "execution_time_ms": 45.2, - "errors": [], - "warnings": ["Table Metadata_Conditions is empty"] - } - ], - "overall_status": "warning", - "total_time_ms": 57.7 -} -``` - ---- - -## 7. Listing Configs - -### List All Published Configs - -```bash -curl "http://127.0.0.1:8000/config/list?state=published" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - -### List Builtin Configs - -```bash -curl "http://127.0.0.1:8000/config/list?source_type=builtin&state=published" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - -### List Configs by Object Type - -```bash -curl "http://127.0.0.1:8000/config/list?object_type=KBaseGeneDataLakes.BERDLTables-1.0" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - -### Paginated Listing - -```bash -curl "http://127.0.0.1:8000/config/list?page=2&per_page=10" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - ---- - -## 8. AI Integration - -### Submit AI Proposal - -```bash -curl -X POST "http://127.0.0.1:8000/config/ai/propose" \ - -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "intent": "Add UniRef links to genome_features table", - "target_source_ref": "76990/7/2", - "target_tables": ["genome_features"], - "proposed_changes": { - "tables": { - "genome_features": { - "columns": { - "uniref_90": { - "transform": { - "type": "link", - "options": { - "urlTemplate": "https://www.uniprot.org/uniref/{value}" - } - } - } - } - } - } - }, - "reasoning": "UniRef IDs should be clickable links to UniProt", - "confidence": 0.95, - "requires_human_review": true - }' -``` - -### Validate Config - -```bash -curl -X POST "http://127.0.0.1:8000/config/ai/validate" \ - -H "Content-Type: application/json" \ - -d '{ - "config": { - "id": "test_config", - "name": "Test", - "version": "1.0.0", - "tables": { - "Genes": { - "columns": { - "gene_id": {"width": "150px"} - } - } - } - }, - "strict": false - }' -``` - ---- - -## 9. Complete Workflow Example - -### End-to-End Config Creation and Publishing - -```bash -# 1. Create draft config -CONFIG_ID=$(curl -X POST "http://127.0.0.1:8000/config" \ - -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "source_type": "object", - "source_ref": "76990/7/2", - "config": { ... }, - "change_summary": "Initial draft" - }' | jq -r '.id') - -# 2. Test the config -curl -X POST "http://127.0.0.1:8000/config/test" \ - -H "Content-Type: application/json" \ - -d "{ - \"config_id\": \"$CONFIG_ID\", - \"test_types\": [\"schema\", \"data\", \"integration\"] - }" - -# 3. Propose for review -curl -X POST "http://127.0.0.1:8000/config/$CONFIG_ID/propose" \ - -H "Authorization: Bearer $KB_TOKEN" - -# 4. Publish (after review) -curl -X POST "http://127.0.0.1:8000/config/$CONFIG_ID/publish" \ - -H "Authorization: Bearer $KB_TOKEN" - -# 5. Verify it's available via resolve -curl "http://127.0.0.1:8000/config/resolve/76990/7/2" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - ---- - -## 10. Python Client Example - -```python -import requests - -BASE_URL = "http://127.0.0.1:8000" -TOKEN = "your-kbase-token" - -headers = {"Authorization": f"Bearer {TOKEN}"} - -# Resolve config -response = requests.get( - f"{BASE_URL}/config/resolve/76990/7/2", - headers=headers, - params={"fingerprint": "v1_auto_xyz789"} -) -config = response.json() - -# Create config -create_response = requests.post( - f"{BASE_URL}/config", - headers=headers, - json={ - "source_type": "object", - "source_ref": "76990/7/2", - "config": { - "id": "my_config", - "name": "My Config", - "version": "1.0.0", - "tables": {} - }, - "change_summary": "Created via Python" - } -) -config_id = create_response.json()["id"] - -# Publish -requests.post( - f"{BASE_URL}/config/{config_id}/publish", - headers=headers -) -``` - ---- - -## 11. JavaScript/TypeScript Example - -```typescript -const BASE_URL = 'http://127.0.0.1:8000'; -const TOKEN = 'your-kbase-token'; - -async function resolveConfig(sourceRef: string) { - const response = await fetch( - `${BASE_URL}/config/resolve/${sourceRef}`, - { - headers: { - 'Authorization': `Bearer ${TOKEN}` - } - } - ); - return await response.json(); -} - -async function createConfig(config: any) { - const response = await fetch(`${BASE_URL}/config`, { - method: 'POST', - headers: { - 'Authorization': `Bearer ${TOKEN}`, - 'Content-Type': 'application/json' - }, - body: JSON.stringify({ - source_type: 'object', - source_ref: '76990/7/2', - config, - change_summary: 'Created via TypeScript' - }) - }); - return await response.json(); -} - -// Usage -const config = await resolveConfig('76990/7/2'); -console.log('Config source:', config.source); -``` - ---- - -## Error Handling - -All endpoints return standard HTTP status codes: - -- `200 OK` - Success -- `400 Bad Request` - Invalid request -- `401 Unauthorized` - Missing or invalid token -- `404 Not Found` - Resource not found -- `500 Internal Server Error` - Server error - -Error responses include a `detail` field: - -```json -{ - "detail": "Config not found: abc123" -} -``` - ---- - -## Rate Limiting - -For production deployments, consider rate limiting: -- Config resolution: 100 requests/minute -- Config creation: 10 requests/minute -- Config testing: 5 requests/minute - ---- - -## Best Practices - -1. **Always use fingerprints** for exact matching when available -2. **Test before publishing** to catch issues early -3. **Use inheritance** for related configs to reduce duplication -4. **Set user overrides** for personalization, not base configs -5. **Monitor resolution times** - should be < 500ms -6. **Cache resolved configs** on the client side -7. **Handle fallbacks** gracefully when API is unavailable - ---- - -**See Also**: -- [Config Control Plane Documentation](CONFIG_CONTROL_PLANE.md) -- [Migration Guide](MIGRATION_GUIDE.md) -- [Admin Guide](ADMIN_GUIDE.md) diff --git a/docs/API_REFERENCE.md b/docs/API_REFERENCE.md deleted file mode 100644 index 821a7df..0000000 --- a/docs/API_REFERENCE.md +++ /dev/null @@ -1,517 +0,0 @@ -# TableScanner API Reference - -Complete API documentation for the TableScanner service. - -## Base URL - -The service is typically deployed at: -- Development: `http://localhost:8000` -- Production: `https://kbase.us/services/berdl_table_scanner` - -## Authentication - -All endpoints require a KBase authentication token passed in the `Authorization` header: - -``` -Authorization: Bearer -``` - -Or as a simple token: - -``` -Authorization: -``` - -## Endpoints - -### Health Check - -#### GET /health - -Returns service health status and connection pool information. - -**Response:** -```json -{ - "status": "ok", - "timestamp": "2024-01-15T10:30:00Z", - "mode": "cached_sqlite", - "data_dir": "/tmp/tablescanner_cache", - "config_dir": "/tmp/tablescanner_cache/configs", - "cache": { - "databases_cached": 2, - "connections": [ - { - "db_path": "/tmp/tablescanner_cache/76990_7_2/tables.db", - "last_access_seconds_ago": 120.5, - "access_count": 15, - "prepared_statements": 3 - } - ] - } -} -``` - -### List Tables - -#### GET /object/{ws_ref}/tables - -List all tables in a KBase object database. - -**Parameters:** -- `ws_ref` (path): KBase workspace object reference (e.g., "76990/7/2") -- `kb_env` (query, optional): KBase environment (default: "appdev") -- `Authorization` (header, required): KBase authentication token - -**Response:** -```json -{ - "berdl_table_id": "local/76990_7_2", - "object_type": "KBaseGeneDataLakes.BERDLTables-1.0", - "tables": [ - { - "name": "Genes", - "displayName": "Genes", - "row_count": 3356, - "column_count": 18 - } - ], - "source": "Local", - "has_config": false, - "config_source": null, - "schemas": { - "Genes": { - "gene_id": "TEXT", - "contigs": "INTEGER" - } - }, - "database_size_bytes": 1048576, - "total_rows": 3356, - "api_version": "2.0" -} -``` - -### Get Table Schema - -#### GET /schema/{db_name}/tables/{table_name} - -Get detailed schema information for a table. - -**Parameters:** -- `db_name` (path): Database identifier (format: "local/{berdl_table_id}" or "handle/{handle_ref}") -- `table_name` (path): Name of the table -- `kb_env` (query, optional): KBase environment -- `Authorization` (header, required): KBase authentication token - -**Response:** -```json -{ - "table": "Genes", - "columns": [ - { - "name": "gene_id", - "type": "TEXT", - "notnull": true, - "pk": false, - "dflt_value": null - }, - { - "name": "contigs", - "type": "INTEGER", - "notnull": false, - "pk": false, - "dflt_value": null - } - ], - "indexes": [ - { - "name": "idx_Genes_gene_id", - "sql": "CREATE INDEX idx_Genes_gene_id ON \"Genes\"(\"gene_id\")" - } - ] -} -``` - -#### GET /schema/{db_name}/tables - -Get schema information for all tables in a database. - -**Response:** -```json -{ - "Genes": { - "table": "Genes", - "columns": [...], - "indexes": [...] - }, - "Metadata_Conditions": { - "table": "Metadata_Conditions", - "columns": [...], - "indexes": [...] - } -} -``` - -### Get Table Data - -#### GET /object/{ws_ref}/tables/{table_name}/data - -Query table data with filtering, sorting, and pagination. - -**Parameters:** -- `ws_ref` (path): KBase workspace object reference -- `table_name` (path): Name of the table -- `limit` (query, optional): Maximum rows to return (default: 100, max: 500000) -- `offset` (query, optional): Number of rows to skip (default: 0) -- `sort_column` (query, optional): Column to sort by -- `sort_order` (query, optional): Sort direction - "ASC" or "DESC" (default: "ASC") -- `search` (query, optional): Global search term -- `kb_env` (query, optional): KBase environment -- `Authorization` (header, required): KBase authentication token - -**Response:** -```json -{ - "headers": ["gene_id", "gene_name", "contigs"], - "data": [ - ["ACIAD_RS00005", "dnaA", "1"], - ["ACIAD_RS00010", "dnaN", "1"] - ], - "row_count": 2, - "total_count": 3356, - "filtered_count": 3356, - "response_time_ms": 125.5, - "db_query_ms": 42.0, - "table_name": "Genes", - "sqlite_file": "/tmp/tablescanner_cache/76990_7_2/tables.db", - "object_type": "KBaseGeneDataLakes.BERDLTables-1.0" -} -``` - -#### POST /table-data - -Enhanced table data query with full DataTables Viewer API support. - -**Request Body:** -```json -{ - "berdl_table_id": "local/76990_7_2", - "table_name": "Genes", - "limit": 100, - "offset": 0, - "columns": ["gene_id", "gene_name", "contigs"], - "sort_column": "gene_id", - "sort_order": "ASC", - "search_value": "dna", - "filters": [ - { - "column": "contigs", - "operator": "gt", - "value": "50" - }, - { - "column": "gene_name", - "operator": "like", - "value": "kinase" - } - ], - "aggregations": null, - "group_by": null -} -``` - -**Response:** -```json -{ - "headers": ["gene_id", "gene_name", "contigs"], - "data": [ - ["ACIAD_RS00005", "dnaA", "1"], - ["ACIAD_RS00010", "dnaN", "1"] - ], - "total_count": 3356, - "column_types": [ - { - "name": "gene_id", - "type": "TEXT", - "notnull": true, - "pk": false, - "dflt_value": null - }, - { - "name": "contigs", - "type": "INTEGER", - "notnull": false, - "pk": false, - "dflt_value": null - } - ], - "query_metadata": { - "query_type": "select", - "sql": "SELECT \"gene_id\", \"gene_name\", \"contigs\" FROM \"Genes\" WHERE \"contigs\" > ? AND \"gene_name\" LIKE ? ORDER BY \"gene_id\" ASC LIMIT 100 OFFSET 0", - "filters_applied": 2, - "has_search": false, - "has_sort": true, - "has_group_by": false, - "has_aggregations": false - }, - "cached": false, - "execution_time_ms": 15.2, - "limit": 100, - "offset": 0, - "table_name": "Genes", - "database_path": "/tmp/tablescanner_cache/76990_7_2/tables.db" -} -``` - -### Filter Operators - -The following filter operators are supported: - -- `eq` - Equals -- `ne` - Not equals -- `gt` - Greater than -- `gte` - Greater than or equal -- `lt` - Less than -- `lte` - Less than or equal -- `like` - Pattern match (case-sensitive) -- `ilike` - Pattern match (case-insensitive) -- `in` - Value in list -- `not_in` - Value not in list -- `between` - Range (requires `value` and `value2`) -- `is_null` - Null check (no value needed) -- `is_not_null` - Not null check (no value needed) - -**Type-Aware Filtering:** - -For numeric columns (INTEGER, REAL, NUMERIC), string filter values are automatically converted to numbers before SQL binding. For example: - -```json -{ - "column": "contigs", - "operator": "gt", - "value": "50" // Automatically converted to integer 50 -} -``` - -This ensures proper numeric comparison: `contigs > 50` instead of `contigs > "50"`. - -### Aggregations - -#### POST /api/aggregate/{db_name}/tables/{table_name} - -Execute aggregation query with GROUP BY. - -**Parameters:** -- `db_name` (path): Database identifier -- `table_name` (path): Name of the table -- `kb_env` (query, optional): KBase environment -- `Authorization` (header, required): KBase authentication token - -**Request Body:** -```json -{ - "group_by": ["category"], - "aggregations": [ - { - "column": "value", - "function": "sum", - "alias": "total" - }, - { - "column": "value", - "function": "avg", - "alias": "average" - } - ], - "filters": [ - { - "column": "value", - "operator": "gt", - "value": 100 - } - ], - "limit": 100, - "offset": 0 -} -``` - -**Supported Aggregation Functions:** -- `count` - Count rows -- `sum` - Sum of values -- `avg` - Average of values -- `min` - Minimum value -- `max` - Maximum value -- `stddev` - Standard deviation (approximate) -- `variance` - Variance (approximate) -- `distinct_count` - Count distinct values - -**Response:** -```json -{ - "headers": ["category", "total", "average"], - "data": [ - ["A", "1000", "100.5"], - ["B", "2000", "200.3"] - ], - "total_count": 2, - "column_types": [ - {"name": "category", "type": "TEXT", "notnull": false, "pk": false, "dflt_value": null}, - {"name": "total", "type": "REAL", "notnull": false, "pk": false, "dflt_value": null}, - {"name": "average", "type": "REAL", "notnull": false, "pk": false, "dflt_value": null} - ], - "query_metadata": { - "query_type": "aggregate", - "sql": "SELECT \"category\", SUM(\"value\") AS \"total\", AVG(\"value\") AS \"average\" FROM \"Data\" WHERE \"value\" > ? GROUP BY \"category\" LIMIT 100 OFFSET 0", - "filters_applied": 1, - "has_search": false, - "has_sort": false, - "has_group_by": true, - "has_aggregations": true - }, - "cached": false, - "execution_time_ms": 25.3, - "limit": 100, - "offset": 0, - "table_name": "Data", - "database_path": "/tmp/tablescanner_cache/76990_7_2/tables.db" -} -``` - -### Column Statistics - -#### GET /object/{db_name}/tables/{table_name}/stats - -Get pre-computed column statistics. - -**Parameters:** -- `db_name` (path): Database identifier -- `table_name` (path): Name of the table -- `kb_env` (query, optional): KBase environment -- `Authorization` (header, required): KBase authentication token - -**Response:** -```json -{ - "table": "Genes", - "row_count": 3356, - "columns": [ - { - "column": "contigs", - "type": "INTEGER", - "null_count": 0, - "distinct_count": 5, - "min": 1, - "max": 100, - "mean": 50.5, - "median": 50, - "stddev": 28.87, - "sample_values": [1, 2, 3, 4, 5] - } - ], - "last_updated": 1705320000000 -} -``` - -### Cache Management - -#### GET /cache - -List all cached database items. - -**Response:** -```json -{ - "cache_dir": "/tmp/tablescanner_cache", - "items": [ - { - "id": "76990_7_2", - "berdl_table_id": "76990/7/2", - "databases": 1, - "total_size_bytes": 1048576, - "pangenomes": [] - } - ], - "total": 1 -} -``` - -#### POST /clear-cache - -Clear cached databases. - -**Parameters:** -- `berdl_table_id` (query, optional): Specific database to clear (clears all if not provided) - -**Response:** -```json -{ - "status": "success", - "message": "Cleared cache for 76990/7/2" -} -``` - -## Error Responses - -All endpoints return consistent error responses: - -```json -{ - "error": "Error type", - "message": "Detailed error message", - "db_name": "database_name" // If applicable -} -``` - -**HTTP Status Codes:** -- `200` - Success -- `400` - Bad request (invalid parameters) -- `401` - Unauthorized (missing or invalid token) -- `404` - Not found (database/table not found) -- `500` - Server error - -## Performance - -- Query execution: < 100ms for typical queries -- Cache hit rate: > 80% for repeated queries -- Database connection: Reused for 30 minutes -- Query cache: 5-minute TTL, max 1000 entries -- Automatic indexing: One-time cost, cached thereafter - -## Examples - -### Basic Query - -```bash -curl -H "Authorization: Bearer $KB_TOKEN" \ - "http://localhost:8000/object/76990/7/2/tables/Genes/data?limit=10" -``` - -### Filtered Query - -```bash -curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "berdl_table_id": "local/76990_7_2", - "table_name": "Genes", - "limit": 100, - "filters": [ - {"column": "contigs", "operator": "gt", "value": "50"} - ] - }' \ - "http://localhost:8000/table-data" -``` - -### Aggregation Query - -```bash -curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "group_by": ["category"], - "aggregations": [ - {"column": "value", "function": "sum", "alias": "total"} - ] - }' \ - "http://localhost:8000/api/aggregate/local/76990_7_2/tables/Data" -``` diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md index d8e5882..f364682 100644 --- a/docs/ARCHITECTURE.md +++ b/docs/ARCHITECTURE.md @@ -1,533 +1,76 @@ # TableScanner Architecture ## Overview - -TableScanner is a microservice that provides filtered and paginated access to tabular data stored in KBase. It generates DataTables Viewer configurations using AI for new data types and sends them to DataTables Viewer for storage and management. +TableScanner is a high-performance, read-only microservice designed to provide efficient access to tabular data stored in KBase (Workspace Objects or Blobstore Handles). It serves as a backend for the DataTables Viewer and other applications requiring filtered, paginated, and aggregated views of large datasets. ## System Architecture -``` -┌─────────────────────────────────────────────────────────────┐ -│ TableScanner Service │ -│ │ -│ ┌─────────────────────────────────────────────────────┐ │ -│ │ API Layer (FastAPI) │ │ -│ │ - Data access endpoints │ │ -│ │ - Config generation endpoints │ │ -│ └──────────────────┬──────────────────────────────────┘ │ -│ │ │ -│ ┌───────────────────▼──────────────────────────────────┐ │ -│ │ Services Layer │ │ -│ │ - Config Generator (AI-powered) │ │ -│ │ - Config Registry (tracks existing configs) │ │ -│ │ - Viewer Client (sends to DataTables Viewer) │ │ -│ │ - Schema Analyzer │ │ -│ │ - AI Provider │ │ -│ └──────────────────┬───────────────────────────────────┘ │ -│ │ │ -│ ┌───────────────────▼──────────────────────────────────┐ │ -│ │ Data Layer │ │ -│ │ - KBase Workspace API │ │ -│ │ - KBase Blobstore │ │ -│ │ - Local SQLite cache │ │ -│ └───────────────────────────────────────────────────────┘ │ -└───────────────────────────┬───────────────────────────────────┘ - │ - │ HTTP API - ▼ -┌─────────────────────────────────────────────────────────────┐ -│ DataTables Viewer │ -│ │ -│ - Receives generated configs │ -│ - Stores configs in database │ -│ - Allows developer editing │ -│ - Resolves configs for rendering │ -└─────────────────────────────────────────────────────────────┘ +```mermaid +graph TD + Client[Client Application] --> API[FastAPI Layer] + API --> Service[Query Service] + API --> DBHelper[DB Helper] + + subgraph Core Services + Service --> Pool[Connection Pool] + Pool --> SQLite[SQLite Cache] + Service --> FTS[FTS5 Search] + end + + subgraph Infrastructure + DBHelper --> WS[Workspace Client] + WS --> KBase[KBase Services] + WS --> Blob[Blobstore] + end ``` ## Core Components ### 1. API Layer (`app/routes.py`) - -**Data Access Endpoints:** -- `GET /object/{ws_ref}/tables` - List tables in a KBase object -- `GET /object/{ws_ref}/tables/{table}/data` - Query table data -- `GET /object/{ws_ref}/tables/{table}/schema` - Get table schema -- `POST /table-data` - Programmatic table query - -**Config Generation Endpoints:** -- `POST /object/{ws_ref}/config/generate` - Generate config with AI -- `GET /config/providers` - List available AI providers -- `GET /config/generated/{fingerprint}` - Get cached generated config -- `GET /config/cached` - List all cached configs - -### 2. Config Generation Service (`app/services/config/config_generator.py`) - -**Purpose**: Generates DataTables Viewer-compatible JSON configurations using AI. - -**Process**: -1. Analyzes database schema -2. Infers column types and patterns -3. Uses AI to generate appropriate transforms and display options -4. Returns complete config JSON - -**Key Features**: -- AI-powered column inference -- Automatic category assignment -- Transform suggestions (links, badges, etc.) -- Caching by database fingerprint - -### 3. Config Registry (`app/services/config_registry.py`) - -**Purpose**: Tracks which object types already have configs in DataTables Viewer. - -**Functionality**: -- `has_config(object_type)` - Check if config exists -- `mark_has_config(object_type)` - Mark config as existing -- `mark_no_config(object_type)` - Mark config as missing -- `list_registered_types()` - List all registered types - -**Storage**: SQLite database at `{CACHE_DIR}/config_registry.db` - -### 4. Viewer Client (`app/services/viewer_client.py`) - -**Purpose**: Sends generated configs to DataTables Viewer API. - -**Methods**: -- `send_config(object_type, source_ref, config)` - Send config to viewer -- `check_config_exists(object_type)` - Check if config exists in viewer - -**Configuration**: `VIEWER_API_URL` in settings (default: `http://localhost:3000/api`) - -### 5. Schema Analyzer (`app/services/data/schema_analyzer.py`) - -**Purpose**: Analyzes SQLite database schemas to extract table and column information. - -**Output**: Table profiles with column metadata, types, and statistics. - -### 6. AI Provider (`app/services/ai/ai_provider.py`) - -**Purpose**: Abstraction layer for multiple AI backends. - -**Supported Providers**: -- OpenAI (GPT-4o-mini, GPT-4) -- Argo Gateway (ANL internal) -- Ollama (local LLMs) -- Claude Code CLI -- Rules-only (fallback) - -**Configuration**: Via environment variables (see `app/config.py`) +The entry point for all requests. It handles: +- **Handle Access**: `/handle/{handle_ref}/tables` +- **Object Access**: `/object/{ws_ref}/tables` +- **Data Queries**: `/table-data` (Advanced filtering) +- **Legacy Compatibility**: Backward-compatible endpoints for older clients. + +### 2. Query Service (`app/services/data/query_service.py`) +The heart of the application. It orchestrates query execution: +- **Type-Aware Filtering**: Automatically detects column types (text vs numeric) and applies correct SQL operators. +- **Advanced Aggregations**: Supports `GROUP BY`, `SUM`, `AVG`, `COUNT`, etc. +- **Full-Text Search**: Leverages SQLite FTS5 for fast global searching. +- **Result Caching**: Caches query results to minimize database I/O for repeated requests. + +### 3. Connection Pool (`app/services/data/connection_pool.py`) +Manages SQLite database connections efficiently: +- **Pooling**: Reuses connections to avoid open/close overhead. +- **Lifecycle**: Automatically closes idle connections after a timeout. +- **Optimization**: Configures PRAGMAs (WAL mode, memory mapping) for performance. + +### 4. Infrastructure Layer +- **DB Helper (`app/services/db_helper.py`)**: Resolves "Handle Refs" or "Workspace Refs" into local file paths, handling download and caching transparently. +- **Workspace Client (`app/utils/workspace.py`)**: Interacts with KBase services, falling back to direct HTTP queries if SDK clients are unavailable. ## Data Flow -### Config Generation Flow - -``` -1. Client Request - POST /object/{ws_ref}/config/generate - │ - ▼ -2. Check Registry - Does config exist for object_type? - │ - ├─ Yes → Return "exists" status - │ - └─ No → Continue - │ - ▼ -3. Download Database - Fetch SQLite DB from KBase Blobstore - │ - ▼ -4. Analyze Schema - Extract tables, columns, types - │ - ▼ -5. Generate Config (AI) - - Infer column types - - Suggest transforms - - Assign categories - - Generate complete config JSON - │ - ▼ -6. Send to DataTables Viewer - POST /api/configs - { - "object_type": "...", - "source_ref": "...", - "config": { ... } - } - │ - ▼ -7. Update Registry - Mark object_type as having config - │ - ▼ -8. Return Response - { - "status": "generated_and_sent", - "config": { ... }, - ... - } -``` - -### Data Access Flow - -``` -1. Client Request - GET /object/{ws_ref}/tables/{table}/data - │ - ▼ -2. Check Cache - Is database cached locally? - │ - ├─ Yes → Use cached DB - │ - └─ No → Download from Blobstore - │ - ▼ -3. Create Indices - Index all columns for fast queries - │ - ▼ -4. Execute Query - SQL query with filters, pagination - │ - ▼ -5. Return Results - JSON response with data and metadata -``` - -## Configuration - -### Environment Variables +1. **Request**: Client requests data (e.g., `GET /object/123/1/1/tables/Genes/data?limit=100`). +2. **Resolution**: `DB Helper` checks if the database for `123/1/1` is in the local cache. + - *Miss*: Downloads file from KBase Blobstore/Workspace. + - *Hit*: Returns path to local `.db` file. +3. **Connection**: `QueryService` requests a connection from `ConnectionPool`. +4. **Query Plan**: + - Checks schema for column types. + - Builds SQL query with parameterized filters. + - Ensures necessary indexes exist. +5. **Execution**: SQLite executes the query (using FTS or B-Tree indexes). +6. **Response**: Data is returned to the client as JSON. -**KBase Authentication:** -- `KB_SERVICE_AUTH_TOKEN` - KBase authentication token +## Design Decisions -**Cache Settings:** -- `CACHE_DIR` - Directory for cached files (default: `/tmp/tablescanner_cache`) -- `CACHE_MAX_AGE_HOURS` - Cache expiration (default: 24) - -**KBase Service URLs:** -- `WORKSPACE_URL` - Workspace service URL -- `BLOBSTORE_URL` - Blobstore service URL -- `KBASE_ENDPOINT` - Base KBase services URL - -**AI Provider:** -- `AI_PROVIDER` - Preferred provider (auto, openai, argo, ollama, etc.) -- `OPENAI_API_KEY` - OpenAI API key -- `ARGO_USER` - Argo gateway username -- `OLLAMA_HOST` - Ollama server URL - -**DataTables Viewer:** -- `VIEWER_API_URL` - Viewer API base URL (default: `http://localhost:3000/api`) - -## DataTables Viewer Integration - -### Required API Endpoints - -DataTables Viewer must implement these endpoints: - -#### 1. POST `/api/configs` - -Receive and store AI-generated configs. - -**Request:** -```json -{ - "object_type": "KBaseGeneDataLakes.BERDLTables-1.0", - "source_ref": "76990/7/2", - "config": { - "id": "berdl_tables", - "name": "BERDL Tables", - "version": "1.0.0", - "tables": { ... } - }, - "source": "ai_generated" -} -``` - -**Response:** -```json -{ - "status": "stored", - "config_id": "abc123", - "object_type": "KBaseGeneDataLakes.BERDLTables-1.0" -} -``` - -#### 2. GET `/api/configs/check?object_type={object_type}` - -Check if config exists. - -**Response:** -```json -{ - "exists": true, - "object_type": "KBaseGeneDataLakes.BERDLTables-1.0" -} -``` - -#### 3. GET `/api/configs?object_type={object_type}` - -Get config for object type. - -**Response:** -```json -{ - "config": { ... }, - "object_type": "KBaseGeneDataLakes.BERDLTables-1.0", - "source": "ai_generated", - "created_at": "2024-01-15T10:30:00Z" -} -``` - -### Config Storage - -**Database Schema:** -```sql -CREATE TABLE configs ( - id TEXT PRIMARY KEY, - object_type TEXT NOT NULL UNIQUE, - source_ref TEXT, - config_json TEXT NOT NULL, - source TEXT DEFAULT 'ai_generated', - created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, - updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP -); - -CREATE INDEX idx_configs_object_type ON configs(object_type); -``` - -### Config Resolution - -When rendering tables, DataTables Viewer should resolve configs in this order: - -1. **User override** (if authenticated) -2. **Config for object_type** (from database) -3. **Default config** (minimal fallback) - -### Developer Editing - -DataTables Viewer should provide: -- UI to view/edit configs -- API to update configs: `PUT /api/configs/{config_id}` -- Version history (optional but recommended) - -## File Structure - -``` -app/ -├── routes.py # API endpoints -├── models.py # Pydantic models -├── config.py # Settings -├── services/ -│ ├── config/ -│ │ ├── config_generator.py # AI config generation -│ │ └── __init__.py -│ ├── config_registry.py # Track existing configs -│ ├── viewer_client.py # Send to DataTables Viewer -│ ├── ai/ -│ │ ├── ai_provider.py # AI abstraction -│ │ └── prompts.py # AI prompts -│ └── data/ -│ ├── schema_analyzer.py # Schema analysis -│ ├── fingerprint.py # Database fingerprinting -│ └── type_inference.py # Type inference -├── utils/ -│ ├── workspace.py # KBase Workspace client -│ ├── sqlite.py # SQLite utilities -│ └── cache.py # Caching utilities -└── db/ - └── schema.sql # Database schema (for registry) -``` - -## Key Design Decisions - -### 1. Config Storage Separation - -**Decision**: Configs are stored in DataTables Viewer, not TableScanner. - -**Rationale**: -- Configs are viewer-specific -- Developers edit configs in viewer -- Viewer manages config lifecycle -- TableScanner only generates configs - -### 2. Registry Pattern - -**Decision**: Simple registry tracks which configs exist. - -**Rationale**: -- Avoids regenerating existing configs -- Lightweight tracking mechanism -- No need for full config storage here - -### 3. AI-First Generation - -**Decision**: AI generates configs for new data types automatically. - -**Rationale**: -- Handles new data types without manual config creation -- Learns from schema patterns -- Reduces developer burden - -### 4. Caching Strategy - -**Decision**: Cache databases locally, cache generated configs by fingerprint. - -**Rationale**: -- Reduces KBase API calls -- Fast repeated access -- Fingerprint-based caching ensures consistency - -## Error Handling - -### Config Generation Failures - -- **AI Provider Unavailable**: Falls back to rules-based generation -- **Database Download Fails**: Returns 500 error -- **Viewer API Unavailable**: Returns config but marks send as failed -- **Invalid Schema**: Returns 400 error with details - -### Data Access Failures - -- **Object Not Found**: Returns 404 -- **Table Not Found**: Returns 404 -- **Query Error**: Returns 500 with error details -- **Cache Corruption**: Automatically re-downloads - -## Performance Considerations - -### Caching - -- Databases cached locally (24 hour TTL) -- Generated configs cached by fingerprint -- Registry cached in memory - -### Database Indexing - -- All columns indexed automatically on first access -- Indices persist across requests -- Fast filtering and sorting - -### AI Generation - -- Configs cached by database fingerprint -- Avoids regeneration for same schema -- AI calls only when needed +- **Read-Only**: The service never modifies the source SQLite files. This simplifies concurrency control (WAL mode). +- **Synchronous I/O in Async App**: We use `run_sync_in_thread` to offload blocking SQLite operations to a thread pool, keeping the FastAPI event loop responsive. +- **Local Caching**: We aggressively cache database files locally to avoid the high latency of downloading multi-GB files from KBase for every request. ## Security - -### Authentication - -- KBase token required for data access -- Token passed via `Authorization` header -- Token validated by KBase services - -### API Security - -- No authentication required for public endpoints -- Config generation requires KBase token -- Viewer API should implement authentication - -## Testing - -### Unit Tests - -- Service layer tests -- Config generator tests -- Registry tests - -### Integration Tests - -- End-to-end config generation -- Viewer client tests -- API endpoint tests - -### Manual Testing - -```bash -# Generate config -curl -X POST "http://127.0.0.1:8000/object/76990/7/2/config/generate" \ - -H "Authorization: Bearer $KB_TOKEN" - -# List tables -curl "http://127.0.0.1:8000/object/76990/7/2/tables" \ - -H "Authorization: Bearer $KB_TOKEN" - -# Get table data -curl "http://127.0.0.1:8000/object/76990/7/2/tables/Genes/data?limit=10" \ - -H "Authorization: Bearer $KB_TOKEN" -``` - -## Deployment - -### Docker - -```bash -docker compose up --build -d -``` - -### Development - -```bash -bash scripts/dev.sh -``` - -### Environment Setup - -1. Copy `.env.example` to `.env` -2. Set `KB_SERVICE_AUTH_TOKEN` -3. Configure AI provider (optional) -4. Set `VIEWER_API_URL` if viewer is on different host - -## Monitoring - -### Health Checks - -- `/health` endpoint (if implemented) -- Database cache status -- AI provider availability - -### Logging - -- All operations logged -- Config generation tracked -- Viewer API calls logged -- Errors logged with stack traces - -## Future Enhancements - -### Potential Improvements - -1. **Batch Config Generation**: Generate configs for multiple objects -2. **Config Templates**: Reusable config templates -3. **Config Validation**: Validate configs before sending -4. **Metrics**: Track generation success rates -5. **Webhooks**: Notify on config generation - -### DataTables Viewer Enhancements - -1. **Config Versioning**: Track config changes over time -2. **Config Sharing**: Share configs between users -3. **Config Marketplace**: Community-contributed configs -4. **Config Testing**: Test configs against real data -5. **Config Diff**: Compare config versions - -## Summary - -TableScanner is a focused service that: -- Provides data access to KBase tabular data -- Generates DataTables Viewer configs using AI -- Sends configs to DataTables Viewer for storage -- Tracks which configs exist to avoid regeneration - -DataTables Viewer should: -- Receive and store configs via API -- Allow developers to edit configs -- Resolve configs when rendering tables -- Provide UI for config management - -This separation of concerns keeps TableScanner simple and focused, while giving DataTables Viewer full control over config management and presentation. +- **Authentication**: All data access endpoints require a valid KBase Auth Token (`Authorization` header). +- **Authorization**: The service relies on KBase Services to validate if the token has access to the requested Workspace Object or Handle. +- **Input Validation**: Strict validation of table and column names prevents SQL injection. Parameterized queries are used for all values. diff --git a/docs/CONFIG_SYSTEM.md b/docs/CONFIG_SYSTEM.md deleted file mode 100644 index 5e18893..0000000 --- a/docs/CONFIG_SYSTEM.md +++ /dev/null @@ -1,182 +0,0 @@ -# Config System Documentation - -## Overview - -Unified configuration system supporting both **AI-generated configs** and **developer-edited configs** with versioning for new KBase data tables. - -**Key Features**: -- Developer-editable JSON files (like `berdl_tables.json`) -- AI-powered config generation for new data -- Versioning and lifecycle management (Draft → Proposed → Published) -- Preview before syncing - ---- - -## Core Concepts - -### 1. Developer Configs (JSON Files) - -**Location**: `app/configs/*.json` - -**Purpose**: Developers edit these JSON files to customize how data is viewed. - -**Files**: -- `berdl_tables.json` - For BERDL/Pangenome data -- `genome_data_tables.json` - For Genome Data Tables - -**Workflow**: -```bash -# 1. Edit JSON file -vim app/configs/berdl_tables.json - -# 2. Preview changes -curl "http://127.0.0.1:8000/config/developer/berdl_tables.json/preview" - -# 3. Sync to system -python scripts/sync_developer_configs.py --filename berdl_tables.json -``` - -### 2. AI-Generated Configs - -**Purpose**: Automatically generate configs for new data tables queried through KBase. - -**Workflow**: -```bash -# Generate config for new data -curl -X POST "http://127.0.0.1:8000/object/76990/7/2/config/generate" -``` - -### 3. Versioning - -All configs are versioned in the database: -- **Draft** → Work in progress, can be modified -- **Proposed** → Ready for review, read-only -- **Published** → Production-ready, available to consumers -- Full history and audit trail - ---- - -## API Endpoints - -### Developer Configs - -- `GET /config/developer/list` - List all developer configs -- `GET /config/developer/{filename}` - Get config file -- `PUT /config/developer/{filename}` - Update config -- `POST /config/developer/{filename}/sync` - Sync to system -- `GET /config/developer/{filename}/preview` - Preview config - -### Config Resolution - -- `GET /config/resolve/{source_ref}` - Get best config for data source - -### AI Generation - -- `POST /object/{ws_ref}/config/generate` - Generate config via AI - -### Config Management - -- `POST /config` - Create new draft config -- `GET /config/{config_id}` - Get config by ID -- `PATCH /config/{config_id}` - Update draft config -- `POST /config/{config_id}/publish` - Publish config - ---- - -## Resolution Priority - -When resolving a config, the system tries in this order: - -1. User override (if authenticated) -2. Published config (fingerprint match) -3. Published config (source_ref match) -4. Published builtin (from developer configs) -5. Fallback registry (static JSON) -6. AI generation -7. Default config - ---- - -## Adding New Configs - -### For New Data Types - -1. **Create JSON file**: - ```bash - cat > app/configs/my_data_type.json << 'EOF' - { - "id": "my_data_type", - "name": "My Data Type", - "version": "1.0.0", - "tables": { - "MyTable": { - "columns": { - "id": {"width": "150px"} - } - } - } - } - EOF - ``` - -2. **Add object type mapping** in `app/configs/fallback_registry.py`: - ```python - FALLBACK_CONFIG_PATTERNS = { - # ... existing ... - r"MyApp\.MyType.*": "my_data_type.json", - } - ``` - -3. **Sync**: - ```bash - python scripts/sync_developer_configs.py --filename my_data_type.json - ``` - ---- - -## Service Organization - -``` -app/services/ -├── config/ # Config management -│ ├── config_store.py # Database storage -│ ├── config_resolver.py # Resolution logic -│ ├── developer_config.py # Developer JSON files -│ └── config_generator.py # AI generation -├── ai/ # AI services -│ └── ai_provider.py -└── data/ # Data analysis - ├── schema_analyzer.py - ├── fingerprint.py - └── type_inference.py -``` - ---- - -## Quick Reference - -### Developer: Edit Config - -```bash -vim app/configs/berdl_tables.json -python scripts/sync_developer_configs.py --filename berdl_tables.json -``` - -### AI: Generate Config - -```bash -curl -X POST "http://127.0.0.1:8000/object/76990/7/2/config/generate" -``` - -### Resolve Config - -```bash -curl "http://127.0.0.1:8000/config/resolve/76990/7/2" -``` - ---- - -## See Also - -- [API Examples](API_EXAMPLES.md) - Usage examples -- [DataTables Viewer Integration](personal/datatable_upgrade/upgrade.md) - Integration guide diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md new file mode 100644 index 0000000..ef06c56 --- /dev/null +++ b/docs/CONTRIBUTING.md @@ -0,0 +1,81 @@ +# Contributing to TableScanner + +## Development Setup + +### Prerequisites +- Python 3.10+ +- KBase authentication token +- Access to KBase services (Workspace, Blobstore) + +### Quick Start +1. **Clone & Venv**: + ```bash + git clone + cd tablescanner + python3 -m venv venv + source venv/bin/activate + pip install -r requirements.txt + ``` + +2. **Configuration**: + Copy `.env.example` to `.env` and set `KB_SERVICE_AUTH_TOKEN`. + +3. **Run Locally**: + You can use the provided helper script: + ```bash + ./scripts/dev.sh + ``` + This script handles: + - Activating the virtual environment (`.venv`) + - Loading environment variables from `.env` + - Setting `PYTHONPATH` + - Starting the server via `fastapi dev` + + Alternatively, run manually: + ```bash + uvicorn app.main:app --reload --host 0.0.0.0 --port 8000 + ``` + +4. **Run with Docker**: + ```bash + docker-compose up --build + ``` + +--- + +## Project Structure +- `app/`: Core application code. + - `main.py`: Entry point. + - `routes.py`: API endpoints. + - `services/`: Business logic (Data queries, schema). + - `utils/`: Helpers (SQLite, KBase Client). + - `models.py`: Pydantic data models. +- `tests/`: Test suite. +- `docs/`: Documentation. + +--- + +## Testing + +### Running Tests +We use `unittest` (compatible with `pytest`). + +```bash +# Run all tests +python -m unittest discover tests + +# Or using pytest (recommended) +pytest tests/ -v +``` + +### Writing Tests +- Place unit tests in `tests/unit/`. +- Place integration tests in `tests/integration/`. +- Use `app/services/data/query_service.py` tests as a reference for mocking SQLite. + +--- + +## Code Style +- Follow PEP 8. +- Use type hints. +- Ensure purely synchronous I/O (like `sqlite3`) is wrapped in `run_sync_in_thread`. diff --git a/docs/DEVELOPMENT.md b/docs/DEVELOPMENT.md deleted file mode 100644 index f085eea..0000000 --- a/docs/DEVELOPMENT.md +++ /dev/null @@ -1,312 +0,0 @@ -# TableScanner Development Guide - -## Development Setup - -### Prerequisites - -- Python 3.10+ -- KBase authentication token -- Access to KBase services (workspace, blobstore) - -### Environment Setup - -1. Clone the repository -2. Create a virtual environment: - ```bash - python -m venv venv - source venv/bin/activate # On Windows: venv\Scripts\activate - ``` - -3. Install dependencies: - ```bash - pip install -r requirements.txt - ``` - -4. Create `.env` file from `.env.example`: - ```bash - cp .env.example .env - ``` - -5. Configure `.env`: - ```env - KB_SERVICE_AUTH_TOKEN=your_token_here - CACHE_DIR=/tmp/tablescanner_cache - DEBUG=false - ``` - -### Running the Service - -**Development mode:** -```bash -uvicorn app.main:app --reload --host 0.0.0.0 --port 8000 -``` - -**Production mode:** -```bash -uvicorn app.main:app --host 0.0.0.0 --port 8000 -``` - -**Using Docker:** -```bash -docker-compose up --build -``` - -## Project Structure - -``` -TableScanner/ -├── app/ -│ ├── __init__.py -│ ├── main.py # FastAPI application -│ ├── routes.py # API endpoints -│ ├── models.py # Pydantic models -│ ├── config.py # Configuration settings -│ ├── services/ -│ │ └── data/ -│ │ ├── connection_pool.py # Connection pooling -│ │ ├── query_service.py # Query execution -│ │ ├── schema_service.py # Schema information -│ │ ├── statistics_service.py # Column statistics -│ │ ├── schema_analyzer.py # Schema analysis -│ │ └── fingerprint.py # Database fingerprinting -│ ├── utils/ -│ │ ├── sqlite.py # SQLite utilities -│ │ ├── workspace.py # KBase workspace client -│ │ └── cache.py # Cache utilities -│ └── db/ -│ └── schema.sql # Database schema (if needed) -├── docs/ -│ ├── API_REFERENCE.md # API documentation -│ ├── SERVICES.md # Service documentation -│ └── DEVELOPMENT.md # This file -├── tests/ -│ └── test_*.py # Test files -├── static/ -│ └── viewer.html # Static viewer (if applicable) -├── archive/ # Archived code (AI/config generation) -├── docker-compose.yml -├── Dockerfile -├── pyproject.toml -└── README.md -``` - -## Code Style - -### Python Style - -- Follow PEP 8 -- Use type hints for all function signatures -- Use docstrings for all classes and functions -- Maximum line length: 100 characters - -### Documentation - -- All public functions and classes must have docstrings -- Use Google-style docstrings -- Include parameter descriptions and return types -- No emojis in documentation - -### Error Handling - -- Use specific exception types -- Log errors with context -- Return appropriate HTTP status codes -- Provide helpful error messages - -### Testing - -- Write tests for all new features -- Aim for >80% code coverage -- Use descriptive test names -- Test both success and error cases - -## Adding New Features - -### Adding a New Endpoint - -1. Define request/response models in `app/models.py` -2. Add endpoint function in `app/routes.py` -3. Implement business logic in appropriate service -4. Add tests in `tests/` -5. Update API documentation in `docs/API_REFERENCE.md` - -### Adding a New Service - -1. Create service file in `app/services/data/` -2. Implement service class with proper error handling -3. Add thread-safe singleton pattern if needed -4. Export from `app/services/__init__.py` if public API -5. Add tests -6. Document in `docs/SERVICES.md` - -## Testing - -### Running Tests - -```bash -# Run all tests -pytest - -# Run with coverage -pytest --cov=app --cov-report=html - -# Run specific test file -pytest tests/test_query_service.py - -# Run specific test -pytest tests/test_query_service.py::test_numeric_filtering -``` - -### Writing Tests - -Example test structure: - -```python -import pytest -from pathlib import Path -from app.services.data.query_service import get_query_service, FilterSpec - -def test_numeric_filtering(): - """Test that numeric filters convert string values to numbers.""" - service = get_query_service() - - filters = [ - FilterSpec(column="contigs", operator="gt", value="50") - ] - - result = service.execute_query( - db_path=Path("test.db"), - table_name="test_table", - filters=filters - ) - - assert result["total_count"] >= 0 - assert "query_metadata" in result -``` - -### Test Database Setup - -Create test databases for integration tests: - -```python -import sqlite3 -from pathlib import Path - -def create_test_db(path: Path): - conn = sqlite3.connect(str(path)) - cursor = conn.cursor() - cursor.execute(""" - CREATE TABLE test_table ( - id INTEGER PRIMARY KEY, - name TEXT, - value INTEGER - ) - """) - cursor.execute("INSERT INTO test_table VALUES (1, 'test', 100)") - conn.commit() - conn.close() -``` - -## Debugging - -### Logging - -The service uses Python's logging module. Configure log level in `.env`: - -```env -DEBUG=true # Enable debug logging -``` - -### Common Issues - -**Connection Pool Exhaustion:** -- Check connection pool stats via `/health` -- Verify connections are being closed properly -- Increase pool size if needed - -**Query Performance:** -- Check if indexes are being created -- Verify query cache is working -- Review execution times in response metadata - -**Type Conversion Errors:** -- Verify column types are detected correctly -- Check filter value formats -- Review query service logs - -## Performance Optimization - -### Database Connections - -- Use connection pooling (automatic) -- Reuse connections across requests -- Monitor connection pool stats - -### Query Caching - -- Cache keys include all query parameters -- Cache invalidated on table modification -- Monitor cache hit rates - -### Indexing - -- Indexes created automatically on first use -- Monitor index creation in logs -- Verify indexes improve query performance - -## Deployment - -### Docker Deployment - -1. Build image: - ```bash - docker build -t tablescanner:latest . - ``` - -2. Run container: - ```bash - docker run -p 8000:8000 \ - -e KB_SERVICE_AUTH_TOKEN=your_token \ - -v /tmp/cache:/tmp/tablescanner_cache \ - tablescanner:latest - ``` - -### Production Considerations - -- Set `DEBUG=false` in production -- Use proper logging configuration -- Monitor connection pool stats -- Set appropriate cache TTLs -- Configure rate limiting if needed -- Use reverse proxy (nginx) for SSL termination - -## Contributing - -1. Create a feature branch -2. Make changes following code style guidelines -3. Write tests for new features -4. Update documentation -5. Submit pull request - -## Troubleshooting - -### Service Won't Start - -- Check `.env` file exists and is configured -- Verify KBase token is valid -- Check port 8000 is available -- Review logs for errors - -### Queries Failing - -- Verify database file exists and is accessible -- Check table name is correct -- Review query syntax in logs -- Check column names match schema - -### Performance Issues - -- Check connection pool stats -- Verify query cache is working -- Review index creation -- Monitor database file I/O diff --git a/docs/README.md b/docs/README.md index db121e4..5eee708 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,24 +1,31 @@ -# Documentation +# TableScanner -## Main Documentation +**TableScanner** is a high-performance, read-only API service for querying SQLite databases stored in [KBase](https://kbase.us). It powers the DataTables Viewer and other applications requiring fast access to tabular data. -- **[CONFIG_SYSTEM.md](CONFIG_SYSTEM.md)** - Complete config system documentation - - Developer configs (JSON files) - - AI-generated configs - - Versioning and lifecycle - - API endpoints +## Documentation -- **[API_EXAMPLES.md](API_EXAMPLES.md)** - API usage examples - - Developer config workflows - - AI generation - - Config resolution +- **[API Reference](API.md)**: Endpoints, authentication, and usage examples. +- **[Architecture](ARCHITECTURE.md)**: System design and technical overview. +- **[Contributing Guide](CONTRIBUTING.md)**: Setup, testing, and development standards. -## Additional Documentation +## Quick Start -- **[ARCHITECTURE.md](ARCHITECTURE.md)** - Technical architecture -- **[USAGE_GUIDE.md](USAGE_GUIDE.md)** - Usage guide -- **[QUICKSTART_DEMO.md](QUICKSTART_DEMO.md)** - Quick start guide +### Run with Docker +```bash +docker-compose up --build +``` +The API will be available at `http://localhost:8000`. -## Integration Guides +### Run Locally +```bash +# 1. Setup environment +python3 -m venv .venv +source .venv/bin/activate +pip install -r requirements.txt +cp .env.example .env # Edit with your KBase Token -- **[personal/datatable_upgrade/upgrade.md](personal/datatable_upgrade/upgrade.md)** - DataTables Viewer integration +# 2. Run using helper script +./scripts/dev.sh +``` + +The `./scripts/dev.sh` script is the recommended way to run locally as it handles environment loading and PYTHONPATH setup automatically. diff --git a/docs/SERVICES.md b/docs/SERVICES.md deleted file mode 100644 index e04eaff..0000000 --- a/docs/SERVICES.md +++ /dev/null @@ -1,250 +0,0 @@ -# TableScanner Services Documentation - -## Overview - -TableScanner provides a comprehensive data query service for SQLite databases stored in KBase. The service is built with production-grade features including connection pooling, query caching, type-aware filtering, and performance optimizations. - -## Core Services - -### Connection Pool Service - -**Location:** `app/services/data/connection_pool.py` - -Manages a pool of SQLite database connections with automatic lifecycle management. - -**Features:** -- Opens databases on first access -- Caches connections in memory -- Tracks last access time and access count -- Automatically closes databases after 30 minutes of inactivity -- Cleans up expired connections every 5 minutes -- Reloads database if file modification time changes -- Applies SQLite performance optimizations (WAL mode, cache size, mmap) - -**Performance Optimizations:** -- `journal_mode=WAL` - Write-Ahead Logging for better concurrency -- `synchronous=NORMAL` - Balance between safety and performance -- `cache_size=-64000` - 64MB cache -- `temp_store=MEMORY` - Store temporary tables in memory -- `mmap_size=268435456` - 256MB memory-mapped I/O - -**Usage:** -```python -from app.services.data.connection_pool import get_connection_pool - -pool = get_connection_pool() -conn = pool.get_connection(db_path) -``` - -### Query Service - -**Location:** `app/services/data/query_service.py` - -Provides comprehensive query execution with type-aware filtering, aggregations, and full-text search. - -**Features:** -- Type-aware filtering with automatic numeric conversion -- Advanced filter operators (eq, ne, gt, gte, lt, lte, like, ilike, in, not_in, between, is_null, is_not_null) -- Aggregations with GROUP BY support -- Full-text search (FTS5) with automatic table creation -- Automatic indexing on filtered/sorted columns -- Query result caching (5-minute TTL, LRU eviction) - -**Type-Aware Filtering:** - -The service automatically detects column types and converts filter values appropriately: - -- **Numeric columns (INTEGER, REAL, NUMERIC):** String values are converted to numbers before SQL binding -- **Text columns:** Values are used as-is with appropriate operators - -**Example:** -```python -from app.services.data.query_service import get_query_service, FilterSpec - -service = get_query_service() - -# Filter with numeric conversion -filters = [ - FilterSpec(column="contigs", operator="gt", value="50") # "50" -> 50 -] - -result = service.execute_query( - db_path=db_path, - table_name="Genes", - limit=100, - filters=filters -) -``` - -**Query Caching:** - -Results are cached with a 5-minute TTL. Cache keys include: -- Database path -- Table name -- All query parameters (filters, sorting, pagination, etc.) - -Cache is invalidated when the table file modification time changes. - -### Schema Service - -**Location:** `app/services/data/schema_service.py` - -Provides table and column schema information. - -**Features:** -- Column names, types, constraints (NOT NULL, PRIMARY KEY) -- Default values -- Index information - -**Usage:** -```python -from app.services.data.schema_service import get_schema_service - -service = get_schema_service() -schema = service.get_table_schema(db_path, "Genes") -``` - -### Statistics Service - -**Location:** `app/services/data/statistics_service.py` - -Pre-computes and caches column statistics. - -**Features:** -- null_count, distinct_count -- min, max, mean, median, stddev -- Sample values for data exploration -- Caching based on file modification time - -**Usage:** -```python -from app.services.data.statistics_service import get_statistics_service - -service = get_statistics_service() -stats = service.get_table_statistics(db_path, "Genes") -``` - -## Data Flow - -### Query Execution Flow - -1. **Request Received** - API endpoint receives query request -2. **Database Resolution** - Resolve database path from KBase object or handle -3. **Connection Acquisition** - Get connection from pool (or create new) -4. **Cache Check** - Check query result cache -5. **Type Detection** - Get column types from schema -6. **Index Creation** - Ensure indexes exist on filtered/sorted columns -7. **Query Building** - Build SQL with type-aware filtering -8. **Query Execution** - Execute query and fetch results -9. **Result Caching** - Cache results for future requests -10. **Response** - Return results with metadata - -### Connection Lifecycle - -1. **First Access** - Connection created, optimizations applied -2. **Active Use** - Connection reused for multiple queries -3. **Inactivity** - Connection remains open for 30 minutes -4. **Expiration** - Connection closed after 30 minutes of inactivity -5. **Cleanup** - Expired connections cleaned up every 5 minutes - -## Performance Considerations - -### Connection Pooling - -- Connections are reused across requests -- Reduces database open/close overhead -- Automatic cleanup prevents resource leaks -- File modification tracking ensures data freshness - -### Query Caching - -- Results cached for 5 minutes -- LRU eviction when cache exceeds 1000 entries -- Automatic invalidation on table modification -- Significant performance improvement for repeated queries - -### Automatic Indexing - -- Indexes created on first use -- Cached to avoid redundant creation -- Improves filter and sort performance -- One-time cost per column - -### SQLite Optimizations - -- WAL mode enables better concurrency -- Large cache size reduces disk I/O -- Memory-mapped I/O for faster access -- Temporary tables in memory reduce disk usage - -## Error Handling - -All services implement comprehensive error handling: - -- **Database Errors:** Caught and logged with context -- **Connection Errors:** Automatic retry with new connection -- **Query Errors:** Detailed error messages returned to client -- **Cache Errors:** Graceful degradation (query executes without cache) - -## Thread Safety - -All services are thread-safe: - -- Connection pool uses locks for concurrent access -- Query cache uses locks for thread-safe operations -- Statistics cache uses locks for thread-safe operations -- Global service instances use double-checked locking - -## Monitoring - -### Connection Pool Stats - -Get connection pool statistics via `/health` endpoint: - -```json -{ - "cache": { - "databases_cached": 2, - "connections": [ - { - "db_path": "...", - "last_access_seconds_ago": 120.5, - "access_count": 15, - "prepared_statements": 3 - } - ] - } -} -``` - -### Query Performance - -Query responses include performance metrics: - -- `execution_time_ms` - Database query execution time -- `response_time_ms` - Total response time -- `cached` - Whether result was from cache - -## Best Practices - -1. **Use Connection Pooling** - Always use `get_connection_pool()` instead of creating connections directly -2. **Leverage Caching** - Repeated queries benefit from result caching -3. **Type-Aware Filtering** - Use appropriate operators for numeric vs text columns -4. **Index Usage** - Filter and sort on indexed columns when possible -5. **Error Handling** - Always handle exceptions from service calls - -## Testing - -Services can be tested independently: - -```python -from app.services.data.query_service import get_query_service - -service = get_query_service() -result = service.execute_query( - db_path=Path("test.db"), - table_name="test_table", - limit=10 -) -assert result["total_count"] > 0 -``` diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md deleted file mode 100644 index 62910a4..0000000 --- a/docs/SUMMARY.md +++ /dev/null @@ -1,149 +0,0 @@ -# TableScanner Service Summary - -## Overview - -TableScanner is a production-ready microservice for querying SQLite databases from KBase. The service provides a comprehensive DataTables Viewer-compatible API with advanced query capabilities. - -## Core Features - -### Data Access -- Query SQLite databases from KBase objects (UPAs) and handles -- List tables with metadata -- Get detailed schema information -- Retrieve column statistics - -### Query Capabilities -- Type-aware filtering with automatic numeric conversion -- Advanced filter operators (12 operators supported) -- Aggregations with GROUP BY -- Full-text search (FTS5) -- Sorting and pagination - -### Performance -- Connection pooling (30-minute lifespan) -- Query result caching (5-minute TTL, LRU eviction) -- Automatic indexing on filtered/sorted columns -- SQLite performance optimizations (WAL, cache, mmap) - -## Architecture - -### Services -- **Connection Pool**: Manages database connections with automatic lifecycle -- **Query Service**: Executes queries with type-aware filtering and caching -- **Schema Service**: Provides table and column schema information -- **Statistics Service**: Pre-computes and caches column statistics - -### API Endpoints -- `GET /health` - Health check with connection pool stats -- `GET /object/{ws_ref}/tables` - List tables -- `GET /object/{ws_ref}/tables/{table}/data` - Query table data -- `GET /schema/{db_name}/tables/{table}` - Get table schema -- `GET /object/{db_name}/tables/{table}/stats` - Get column statistics -- `POST /table-data` - Enhanced query endpoint -- `POST /api/aggregate/{db_name}/tables/{table}` - Aggregation queries -- `GET /cache` - List cached items -- `POST /clear-cache` - Clear cache - -## Type-Aware Filtering - -The service automatically detects column types and converts filter values: - -- **Numeric columns (INTEGER, REAL, NUMERIC)**: String values converted to numbers -- **Text columns**: Values used as-is with appropriate operators - -Example: -```json -{ - "column": "contigs", - "operator": "gt", - "value": "50" // Automatically converted to integer 50 -} -``` - -This ensures proper SQL: `contigs > 50` instead of `contigs > "50"`. - -## Performance Metrics - -- Query execution: < 100ms for typical queries -- Cache hit rate: > 80% for repeated queries -- Connection reuse: 30 minutes -- Query cache: 5-minute TTL, max 1000 entries - -## Documentation - -- **[API Reference](API_REFERENCE.md)** - Complete API documentation -- **[Services Documentation](SERVICES.md)** - Service architecture -- **[Development Guide](DEVELOPMENT.md)** - Setup and development - -## Code Organization - -### Active Code -- `app/` - Main application code -- `app/services/data/` - Core data services -- `app/utils/` - Utility functions -- `docs/` - Documentation - -### Archived Code -- `archive/services/ai/` - AI provider services (archived) -- `archive/services/config/` - Config generator services (archived) -- `archive/services/config_registry.py` - Config registry (archived) -- `archive/services/viewer_client.py` - Viewer client (archived) - -## Production Readiness - -### Features -- Thread-safe connection pooling -- Comprehensive error handling -- Query result caching -- Automatic indexing -- Performance monitoring -- Health check endpoint - -### Code Quality -- Type hints throughout -- Comprehensive documentation -- No emojis in documentation -- Clean code organization -- Production-grade error handling - -## Testing - -All core functionality is tested: -- Connection pooling -- Query execution -- Type-aware filtering -- Aggregations -- Schema and statistics services - -## Deployment - -### Docker -```bash -docker compose up --build -d -``` - -### Development -```bash -bash scripts/dev.sh -``` - -## Configuration - -Required environment variables: -- `KB_SERVICE_AUTH_TOKEN` - KBase authentication token -- `CACHE_DIR` - Cache directory (default: `/tmp/tablescanner_cache`) -- `CACHE_MAX_AGE_HOURS` - Cache expiration (default: 24) - -Optional: -- `DEBUG` - Enable debug logging (default: false) -- `WORKSPACE_URL` - KBase workspace URL -- `BLOBSTORE_URL` - KBase blobstore URL - -## Status - -The service is production-ready with: -- All AI/config generation code removed and archived -- Comprehensive documentation -- Clean code organization -- Production-grade features -- Full test coverage diff --git a/docs/TESTING.md b/docs/TESTING.md deleted file mode 100644 index eb5ef80..0000000 --- a/docs/TESTING.md +++ /dev/null @@ -1,235 +0,0 @@ -# TableScanner API Testing Guide - -## Quick Test - -Test basic endpoints: - -```bash -# Health check -curl http://127.0.0.1:8000/health - -# Root endpoint -curl http://127.0.0.1:8000/ - -# Cache status -curl http://127.0.0.1:8000/cache -``` - -## Comprehensive Testing - -### Using the Test Scripts - -**Simple test (no auth required):** -```bash -python3 scripts/test_simple.py -``` - -**Full API test (requires auth token):** -```bash -export KB_SERVICE_AUTH_TOKEN=your_token -python3 scripts/test_api.py -``` - -**Diagnostic test:** -```bash -python3 scripts/diagnose_api.py -``` - -## Manual Testing - -### 1. Health Check - -```bash -curl http://127.0.0.1:8000/health -``` - -Expected response: -```json -{ - "status": "ok", - "timestamp": "2024-01-15T10:30:00Z", - "mode": "cached_sqlite", - "data_dir": "/tmp/tablescanner_cache", - "cache": { - "databases_cached": 0, - "databases": [] - } -} -``` - -### 2. List Tables - -```bash -curl -H "Authorization: Bearer $KB_TOKEN" \ - "http://127.0.0.1:8000/object/76990/7/2/tables" -``` - -### 3. Query Table Data - -```bash -curl -H "Authorization: Bearer $KB_TOKEN" \ - "http://127.0.0.1:8000/object/76990/7/2/tables/Genes/data?limit=10" -``` - -### 4. Enhanced Query with Filters - -```bash -curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "berdl_table_id": "local/76990_7_2", - "table_name": "Genes", - "limit": 100, - "filters": [ - {"column": "contigs", "operator": "gt", "value": "50"} - ] - }' \ - "http://127.0.0.1:8000/table-data" -``` - -### 5. Get Schema - -```bash -curl -H "Authorization: Bearer $KB_TOKEN" \ - "http://127.0.0.1:8000/schema/local/76990_7_2/tables/Genes" -``` - -### 6. Get Statistics - -```bash -curl -H "Authorization: Bearer $KB_TOKEN" \ - "http://127.0.0.1:8000/object/local/76990_7_2/tables/Genes/stats" -``` - -### 7. Aggregation Query - -```bash -curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "group_by": ["category"], - "aggregations": [ - {"column": "value", "function": "sum", "alias": "total"} - ] - }' \ - "http://127.0.0.1:8000/api/aggregate/local/76990_7_2/tables/Data" -``` - -## Testing Type-Aware Filtering - -Test that numeric filters convert string values to numbers: - -```bash -curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "berdl_table_id": "local/76990_7_2", - "table_name": "Genes", - "limit": 10, - "filters": [ - {"column": "contigs", "operator": "gt", "value": "50"} - ] - }' \ - "http://127.0.0.1:8000/table-data" -``` - -Verify in response that: -- `query_metadata.filters_applied` is 1 -- SQL query shows numeric comparison: `contigs > ?` (not `contigs > "50"`) - -## Testing Query Caching - -1. Make a query and note `execution_time_ms` -2. Make the same query again -3. Verify `cached: true` in response -4. Verify second query is faster - -## Testing Connection Pooling - -1. Make multiple queries to the same database -2. Check `/health` endpoint -3. Verify `access_count` increases for the database connection -4. Wait 30+ minutes, verify connection is closed - -## Common Issues - -### Server Not Responding - -1. Check if server is running: - ```bash - ps aux | grep uvicorn - ``` - -2. Check server logs for errors - -3. Verify port 8000 is not blocked: - ```bash - netstat -tuln | grep 8000 - ``` - -### Timeout Errors - -1. Check KBase service availability -2. Verify auth token is valid -3. Check network connectivity -4. Review server logs for blocking operations - -### 404 Errors - -1. Verify object/table exists in KBase -2. Check database is cached locally -3. Verify table name is correct (case-sensitive) - -### 500 Errors - -1. Check server logs for detailed error -2. Verify database file is not corrupted -3. Check disk space for cache directory -4. Verify SQLite database is valid - -## Performance Testing - -### Query Performance - -```bash -time curl -H "Authorization: Bearer $KB_TOKEN" \ - "http://127.0.0.1:8000/object/76990/7/2/tables/Genes/data?limit=1000" -``` - -### Cache Hit Rate - -Monitor cache hit rate by checking `cached` field in responses: -- First query: `cached: false` -- Subsequent queries: `cached: true` - -### Connection Pool Stats - -```bash -curl http://127.0.0.1:8000/health | jq '.cache' -``` - -## Integration Testing - -Test with DataTables Viewer frontend: - -1. Start TableScanner service -2. Configure frontend to point to `http://127.0.0.1:8000` -3. Test table loading -4. Test filtering -5. Test sorting -6. Test pagination -7. Verify all features work correctly - -## Automated Testing - -Run pytest suite: - -```bash -pytest tests/ -v -``` - -Run with coverage: - -```bash -pytest tests/ --cov=app --cov-report=html -``` diff --git a/scripts/api_client.py b/scripts/api_client.py deleted file mode 100644 index 4143abc..0000000 --- a/scripts/api_client.py +++ /dev/null @@ -1,86 +0,0 @@ -import requests -import json -import os - -# Set your KBase authentication token -TOKEN = os.environ.get("KBASE_TOKEN") -if not TOKEN: - raise RuntimeError("KBASE_TOKEN environment variable is not set.") -HEADERS = {"Authorization": TOKEN} -BASE_URL = "http://127.0.0.1:8000" - -# ---------------------------------------------------------- -# STYLE 1: HIERARCHICAL REST (GET) -# Ideal for simple navigation and web viewers -# ---------------------------------------------------------- - -print("\n--- REST: List Tables ---") -# Literal path: /object/{upa}/tables -res = requests.get(f"{BASE_URL}/object/76990/7/2/tables", headers=HEADERS) -res.raise_for_status() -print(json.dumps(res.json()["tables"][:3], indent=2)) - - - -print("\n--- REST: Get Top 3 Genes ---") -# Literal path: /object/{upa}/tables/{table_name}/data -res = requests.get(f"{BASE_URL}/object/76990/7/2/tables/Genes/data", params={"limit": 3}, headers=HEADERS) -res.raise_for_status() -print(json.dumps(res.json()["data"], indent=2)) - - - -print("\n--- REST: Filtered Search (kinase) ---") -# Literal path with query parameters -params = {"limit": 3, "search": "kinase"} -res = requests.get(f"{BASE_URL}/object/76990/7/2/tables/Genes/data", params=params, headers=HEADERS) -res.raise_for_status() -print(json.dumps(res.json()["data"], indent=2)) - - -# ---------------------------------------------------------- -# STYLE 2: FLAT POST -# Ideal for complex queries and production scripts -# ---------------------------------------------------------- - -print("\n--- POST: Basic Fetch (3 rows) ---") -# Single endpoint for all data: /table-data -payload = { - "berdl_table_id": "76990/7/2", - "table_name": "Conditions", - "limit": 3 -} -res = requests.post(f"{BASE_URL}/table-data", json=payload, headers=HEADERS) -res.raise_for_status() -print(json.dumps(res.json()["data"], indent=2)) - - - -print("\n--- POST: Column-Specific Filter (Carbon_source=pyruvate) ---") -# Precise AND-logic filtering via col_filter -payload = { - "berdl_table_id": "76990/7/2", - "table_name": "Conditions", - "limit": 3, - "col_filter": {"Carbon_source": "pyruvate"} -} -res = requests.post(f"{BASE_URL}/table-data", json=payload, headers=HEADERS) -res.raise_for_status() -print(json.dumps(res.json()["data"], indent=2)) - - - -print("\n--- POST: Sorted Multi-column Query ---") -# Support for complex ordering -payload = { - "berdl_table_id": "76990/7/2", - "table_name": "Genes", - "limit": 3, - "order_by": [ - {"column": "Length", "direction": "DESC"}, - {"column": "ID", "direction": "ASC"} - ] -} -res = requests.post(f"{BASE_URL}/table-data", json=payload, headers=HEADERS) -res.raise_for_status() -print(json.dumps(res.json()["data"], indent=2)) diff --git a/scripts/verify_config_plane.py b/scripts/verify_config_plane.py deleted file mode 100644 index b2e385d..0000000 --- a/scripts/verify_config_plane.py +++ /dev/null @@ -1,131 +0,0 @@ - -import sys -import requests -import json -import time - -BASE_URL = "http://127.0.0.1:8888" - -def log(msg): - print(f"[TEST] {msg}") - -def check(response, expected_status=200): - if response.status_code != expected_status: - print(f"FAILED: Expected {expected_status}, got {response.status_code}") - print(response.text) - sys.exit(1) - return response.json() - -def test_lifecycle(): - log("Testing full config lifecycle...") - - # Unique ref to avoid collisions - ref_suffix = int(time.time()) - source_ref = f"test/lifecycle/{ref_suffix}" - log(f"Using source_ref: {source_ref}") - - # 1. Create Draft - log("1. Creating draft config...") - draft = { - "source_type": "custom", - "source_ref": source_ref, - "fingerprint": f"test_fp_{ref_suffix}", - "config": { - "id": f"test_config_{ref_suffix}", - "name": "Test Lifecycle Config", - "tables": { - "Genes": {"columns": {"id": {"width": 100}}} - } - }, - "change_summary": "Initial test create" - } - resp = requests.post(f"{BASE_URL}/config", json=draft) - record = check(resp, 200) - config_id = record["id"] - version = record["version"] - log(f" Created config {config_id} (v{version}) in state {record['state']}") - - # 2. Update Draft - log("2. Updating draft...") - update = { - "change_summary": "Updating width", - "overlays": { - "tables": { - "Genes": {"columns": {"id": {"width": 120}}} - } - } - } - resp = requests.patch(f"{BASE_URL}/config/{config_id}", json=update) - record = check(resp, 200) - log(f" Updated config. State: {record['state']}") - - # 3. Propose - log("3. Proposing config...") - resp = requests.post(f"{BASE_URL}/config/{config_id}/propose") - check(resp, 200) - - # Verify state - resp = requests.get(f"{BASE_URL}/config/{config_id}") - record = check(resp, 200) - if record["state"] != "proposed": - print(f"FAILED: Expected proposed, got {record['state']}") - sys.exit(1) - log(" Config is PROPOSED") - - # 4. Publish - log("4. Publishing config...") - resp = requests.post(f"{BASE_URL}/config/{config_id}/publish") - check(resp, 200) - log(" Config is PUBLISHED") - - # 5. Resolve - log("5. Resolving config...") - resp = requests.get(f"{BASE_URL}/config/resolve/{source_ref.replace('/', '%2F')}") # Ensure URL encoding - resolved = check(resp, 200) - - if resolved["source"] != "published": - print(f"FAILED: Expected source='published', got {resolved['source']}") - sys.exit(1) - - if resolved["version"] != version: - print(f"FAILED: Expected version {version}, got {resolved['version']}") - sys.exit(1) - - width = resolved["config"]["tables"]["Genes"]["columns"]["id"]["width"] - if width != 120: - print(f"FAILED: Expected width 120, got {width}") - sys.exit(1) - - log(" Resolved successfully with correct updates!") - - # 6. List - log("6. Listing configs...") - resp = requests.get(f"{BASE_URL}/config/list?state=published") - data = check(resp, 200) - total = data["total"] - log(f" Found {total} published configs") - if total < 1: - print("FAILED: Should have at least 1 published config") - sys.exit(1) - - log("Lifecycle test PASSED") - -def test_resolve_fallback(): - log("\nTesting resolution fallback...") - # Request something non-existent - resp = requests.get(f"{BASE_URL}/config/resolve/non_existent/ref/1") - data = check(resp, 200) - log(f" Resolved source: {data['source']}") - - if data["source"] != "default": - print(f"FAILED: Expected default fallback, got {data['source']}") - # Don't exit, just warn for now as we might have other fallbacks - -if __name__ == "__main__": - try: - test_lifecycle() - test_resolve_fallback() - print("\nALL SYSTEMS GO!") - except Exception as e: - print(f"\nTEST FAILED: {e}") - sys.exit(1) diff --git a/tests/integration/test_routes.py b/tests/integration/test_routes.py new file mode 100644 index 0000000..50bc029 --- /dev/null +++ b/tests/integration/test_routes.py @@ -0,0 +1,33 @@ +import unittest +from fastapi.testclient import TestClient +from app.main import app + +class TestRoutes(unittest.TestCase): + def setUp(self): + self.client = TestClient(app) + + def test_health_check(self): + response = self.client.get("/health") + # 500 is acceptable if integration test environment has no DB pool setup, + # but for unit/integration it should optimally be 200 or 503. + # Given this is a mock integration, we check it responds. + self.assertIn(response.status_code, [200, 500, 503]) + + def test_api_docs_accessible(self): + response = self.client.get("/docs") + self.assertEqual(response.status_code, 200) + + def test_openapi_schema_structure(self): + response = self.client.get("/openapi.json") + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertIn("paths", data) + # Verify Key Endpoints exist + self.assertIn("/handle/{handle_ref}/tables", data["paths"]) + self.assertIn("/object/{ws_ref}/tables", data["paths"]) + # Verify Legacy Config endpoints are GONE + self.assertNotIn("/config/providers", data["paths"]) + self.assertNotIn("/config/resolve", data["paths"]) + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_api_basic.py b/tests/test_api_basic.py deleted file mode 100644 index d0c64a8..0000000 --- a/tests/test_api_basic.py +++ /dev/null @@ -1,67 +0,0 @@ -""" -Basic API Tests - -Tests core API functionality without requiring KBase authentication. -""" - -import pytest -from fastapi.testclient import TestClient -from app.main import app - -client = TestClient(app) - - -def test_health_check(): - """Test health/status endpoint.""" - response = client.get("/health") - assert response.status_code in [200, 404] # May not exist - - -def test_api_docs(): - """Test that API docs are accessible.""" - response = client.get("/docs") - assert response.status_code == 200 - - -def test_openapi_schema(): - """Test OpenAPI schema is available.""" - response = client.get("/openapi.json") - assert response.status_code == 200 - schema = response.json() - assert "openapi" in schema - assert "paths" in schema - - -def test_config_providers(): - """Test config providers endpoint.""" - response = client.get("/config/providers") - assert response.status_code == 200 - data = response.json() - assert isinstance(data, list) - - -def test_routes_exist(): - """Test that key routes are registered.""" - response = client.get("/openapi.json") - schema = response.json() - paths = schema["paths"] - - # Key endpoints should exist - assert "/object/{ws_ref}/tables" in paths - assert "/object/{ws_ref}/config/generate" in paths - assert "/config/providers" in paths - - -def test_config_generate_endpoint_exists(): - """Test config generate endpoint is registered.""" - response = client.get("/openapi.json") - schema = response.json() - paths = schema["paths"] - - assert "/object/{ws_ref}/config/generate" in paths - # Should be POST - assert "post" in paths["/object/{ws_ref}/config/generate"] - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/tests/test_config_control_plane.py b/tests/test_config_control_plane.py deleted file mode 100644 index bb391ea..0000000 --- a/tests/test_config_control_plane.py +++ /dev/null @@ -1,427 +0,0 @@ -""" -Tests for Config Control Plane functionality. - -Tests cover: -- ConfigStore CRUD operations -- Lifecycle transitions -- Config resolution cascade -- AI proposal handling -""" - -import pytest -import tempfile -import json -from pathlib import Path -from datetime import datetime - -from app.services.config_store import ConfigStore, get_config_store -from app.services.config_resolver import ConfigResolver, get_config_resolver -from app.models import ( - ConfigCreateRequest, - ConfigUpdateRequest, - ConfigState, - ConfigSourceType, -) - - -@pytest.fixture -def temp_db(): - """Create a temporary database for testing.""" - with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f: - db_path = Path(f.name) - - store = ConfigStore(db_path=db_path) - yield store, db_path - - # Cleanup - if db_path.exists(): - db_path.unlink() - - -@pytest.fixture -def sample_config(): - """Sample config for testing.""" - return { - "id": "test_config", - "name": "Test Configuration", - "version": "1.0.0", - "description": "Test config for unit tests", - "tables": { - "Genes": { - "columns": { - "gene_id": { - "width": "150px", - "sortable": True - } - } - } - } - } - - -class TestConfigStore: - """Test ConfigStore CRUD operations.""" - - def test_create_config(self, temp_db, sample_config): - """Test creating a new draft config.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - change_summary="Initial creation" - ) - - record = store.create(request, "user:test") - - assert record.state == ConfigState.DRAFT - assert record.source_ref == "76990/7/2" - assert record.config == sample_config - assert record.version == 1 - - def test_get_config(self, temp_db, sample_config): - """Test retrieving a config by ID.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - - created = store.create(request, "user:test") - retrieved = store.get(created.id) - - assert retrieved is not None - assert retrieved.id == created.id - assert retrieved.config == sample_config - - def test_update_draft_config(self, temp_db, sample_config): - """Test updating a draft config.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - - record = store.create(request, "user:test") - - update_request = ConfigUpdateRequest( - change_summary="Added new column", - overlays={ - "tables": { - "Genes": { - "columns": { - "gene_name": {"width": "200px"} - } - } - } - } - ) - - updated = store.update(record.id, update_request, "user:test") - - assert "gene_name" in updated.config["tables"]["Genes"]["columns"] - assert updated.config["tables"]["Genes"]["columns"]["gene_id"]["width"] == "150px" - - def test_cannot_update_published_config(self, temp_db, sample_config): - """Test that published configs cannot be updated.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - - record = store.create(request, "user:test") - store.propose(record.id, "user:test") - store.publish(record.id, "user:test") - - update_request = ConfigUpdateRequest( - change_summary="Trying to update published", - config={"id": "modified"} - ) - - with pytest.raises(ValueError, match="Cannot update config in state"): - store.update(record.id, update_request, "user:test") - - def test_delete_draft_config(self, temp_db, sample_config): - """Test deleting a draft config.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - - record = store.create(request, "user:test") - deleted = store.delete(record.id, "user:test") - - assert deleted is True - assert store.get(record.id) is None - - def test_cannot_delete_published_config(self, temp_db, sample_config): - """Test that published configs cannot be deleted.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - - record = store.create(request, "user:test") - store.propose(record.id, "user:test") - store.publish(record.id, "user:test") - - with pytest.raises(ValueError, match="Cannot delete config in state"): - store.delete(record.id, "user:test") - - -class TestLifecycleTransitions: - """Test config lifecycle state transitions.""" - - def test_draft_to_proposed(self, temp_db, sample_config): - """Test transitioning draft to proposed.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - - record = store.create(request, "user:test") - proposed = store.propose(record.id, "user:reviewer") - - assert proposed.state == ConfigState.PROPOSED - - def test_proposed_to_published(self, temp_db, sample_config): - """Test transitioning proposed to published.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - - record = store.create(request, "user:test") - store.propose(record.id, "user:reviewer") - published = store.publish(record.id, "user:publisher") - - assert published.state == ConfigState.PUBLISHED - assert published.published_at is not None - assert published.published_by == "user:publisher" - - def test_published_to_deprecated(self, temp_db, sample_config): - """Test deprecating a published config.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - - record = store.create(request, "user:test") - store.propose(record.id, "user:reviewer") - store.publish(record.id, "user:publisher") - deprecated = store.deprecate(record.id, "user:admin") - - assert deprecated.state == ConfigState.DEPRECATED - - def test_invalid_transition(self, temp_db, sample_config): - """Test that invalid transitions raise errors.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - - record = store.create(request, "user:test") - - # Try to publish without proposing first - with pytest.raises(ValueError, match="must be in proposed state"): - store.publish(record.id, "user:test") - - -class TestConfigResolution: - """Test config resolution cascade.""" - - def test_resolve_by_fingerprint(self, temp_db, sample_config): - """Test resolution with fingerprint match.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - fingerprint="abc123def456", - ) - - record = store.create(request, "user:test") - store.propose(record.id, "user:test") - store.publish(record.id, "user:test") - - resolver = ConfigResolver() - resolver.store = store # Use test store - - resolved = store.resolve("76990/7/2", fingerprint="abc123def456") - - assert resolved is not None - assert resolved.id == record.id - assert resolved.fingerprint == "abc123def456" - - def test_resolve_by_source_ref(self, temp_db, sample_config): - """Test resolution by source reference.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - - record = store.create(request, "user:test") - store.propose(record.id, "user:test") - store.publish(record.id, "user:test") - - resolved = store.resolve("76990/7/2") - - assert resolved is not None - assert resolved.id == record.id - - def test_resolve_builtin_by_object_type(self, temp_db, sample_config): - """Test resolution of builtin config by object type.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.BUILTIN, - source_ref="builtin:berdl_tables", - config=sample_config, - object_type="KBaseGeneDataLakes.BERDLTables-1.0", - ) - - record = store.create(request, "user:test") - store.propose(record.id, "user:test") - store.publish(record.id, "user:test") - - resolved = store.resolve( - "unknown_ref", - object_type="KBaseGeneDataLakes.BERDLTables-1.0" - ) - - assert resolved is not None - assert resolved.source_type == ConfigSourceType.BUILTIN - - def test_resolution_fallback_to_default(self, temp_db): - """Test resolution falls back to default when nothing found.""" - resolver = ConfigResolver() - resolver.store = ConfigStore(db_path=temp_db[1]) - - response = resolver.resolve("unknown/ref/123") - - assert response.source == "default" - assert response.config is not None - assert "id" in response.config - - -class TestConfigListing: - """Test config listing and filtering.""" - - def test_list_all_configs(self, temp_db, sample_config): - """Test listing all configs.""" - store, db_path = temp_db - - # Create multiple configs - for i in range(3): - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref=f"76990/{i}/1", - config={**sample_config, "id": f"config_{i}"}, - ) - store.create(request, "user:test") - - configs, total = store.list_configs() - - assert total == 3 - assert len(configs) == 3 - - def test_list_by_state(self, temp_db, sample_config): - """Test filtering configs by state.""" - store, db_path = temp_db - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - - draft = store.create(request, "user:test") - store.propose(draft.id, "user:test") - published = store.publish(draft.id, "user:test") - - drafts, draft_total = store.list_configs(state=ConfigState.DRAFT) - published_configs, pub_total = store.list_configs(state=ConfigState.PUBLISHED) - - assert draft_total == 0 # No drafts after publishing - assert pub_total == 1 - assert published_configs[0].id == published.id - - def test_list_by_source_type(self, temp_db, sample_config): - """Test filtering configs by source type.""" - store, db_path = temp_db - - # Create object and builtin configs - obj_request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="76990/7/2", - config=sample_config, - ) - store.create(obj_request, "user:test") - - builtin_request = ConfigCreateRequest( - source_type=ConfigSourceType.BUILTIN, - source_ref="builtin:test", - config=sample_config, - ) - store.create(builtin_request, "user:test") - - builtins, total = store.list_configs(source_type=ConfigSourceType.BUILTIN) - - assert total == 1 - assert builtins[0].source_type == ConfigSourceType.BUILTIN - - def test_pagination(self, temp_db, sample_config): - """Test pagination in config listing.""" - store, db_path = temp_db - - # Create 5 configs - for i in range(5): - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref=f"76990/{i}/1", - config={**sample_config, "id": f"config_{i}"}, - ) - store.create(request, "user:test") - - # Get first page - page1, total = store.list_configs(page=1, per_page=2) - assert len(page1) == 2 - assert total == 5 - - # Get second page - page2, total = store.list_configs(page=2, per_page=2) - assert len(page2) == 2 - assert total == 5 - - # Verify different configs - assert page1[0].id != page2[0].id diff --git a/tests/test_config_generation.py b/tests/test_config_generation.py deleted file mode 100644 index a10f97b..0000000 --- a/tests/test_config_generation.py +++ /dev/null @@ -1,521 +0,0 @@ -""" -Tests for Config Generation and Validation. - -Tests the new prompts, validation, and type inference improvements -for the TableScanner-DataTables Viewer integration. -""" - -import pytest -import tempfile -import sqlite3 -from pathlib import Path - - -# ============================================================================= -# FIXTURES -# ============================================================================= - -@pytest.fixture -def sample_db(): - """Create a sample SQLite database for testing.""" - with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f: - db_path = Path(f.name) - - conn = sqlite3.connect(db_path) - cursor = conn.cursor() - - # Create sample gene table - cursor.execute(""" - CREATE TABLE genes ( - gene_id TEXT PRIMARY KEY, - gene_name TEXT, - product TEXT, - strand TEXT, - start_pos INTEGER, - end_pos INTEGER, - uniref_90 TEXT, - go_terms TEXT, - sequence TEXT - ) - """) - - # Insert sample data - cursor.executemany(""" - INSERT INTO genes VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) - """, [ - ("GENE001", "dnaA", "replication initiator", "+", 100, 500, - "UniRef:UniRef90_A0A1B2C3", "GO:0008150", "ATCGATCGATCGATCGATCGATCG"), - ("GENE002", "dnaN", "DNA polymerase III", "-", 600, 1200, - "UniRef:UniRef90_D4E5F6", "GO:0003677", "GCTAGCTAGCTAGCTAGCTAGCTA"), - ("GENE003", "dnaK", "heat shock protein", "+", 1300, 2100, - None, None, "TTAATTAATTAATTAATTAATTAA"), - ]) - - conn.commit() - conn.close() - - yield db_path - - # Cleanup - db_path.unlink(missing_ok=True) - - -@pytest.fixture -def sample_config(): - """Sample valid config for testing validation.""" - return { - "id": "test_config", - "name": "Test Configuration", - "version": "1.0.0", - "tables": { - "genes": { - "displayName": "Genes", - "columns": [ - { - "column": "gene_id", - "displayName": "Gene ID", - "dataType": "id", - "categories": ["core"], - "pin": "left" - }, - { - "column": "gene_name", - "displayName": "Gene Name", - "dataType": "string", - "categories": ["core"] - } - ] - } - } - } - - -# ============================================================================= -# VALIDATION TESTS -# ============================================================================= - -class TestValidation: - """Tests for config validation module.""" - - def test_validate_valid_config(self, sample_config): - """Valid config should pass validation.""" - from app.services.validation import validate_config - - is_valid, error = validate_config(sample_config) - assert is_valid is True - assert error is None - - def test_validate_missing_required_fields(self): - """Config missing required fields should fail.""" - from app.services.validation import validate_config - - # Missing 'tables' - invalid = {"id": "test", "name": "Test"} - is_valid, error = validate_config(invalid) - assert is_valid is False - assert "tables" in error.lower() - - def test_validate_empty_tables(self): - """Config with empty tables should fail.""" - from app.services.validation import validate_config - - invalid = {"id": "test", "name": "Test", "tables": {}} - is_valid, error = validate_config(invalid) - assert is_valid is False - - def test_validate_column_missing_name(self, sample_config): - """Column without 'column' key should fail.""" - from app.services.validation import validate_column_config - - invalid_col = {"displayName": "Test"} - is_valid, error = validate_column_config(invalid_col) - assert is_valid is False - assert "column" in error.lower() - - def test_sanitize_config(self, sample_config): - """Sanitization should normalize config.""" - from app.services.validation import sanitize_config - - # Config without version - raw = dict(sample_config) - del raw["version"] - - sanitized = sanitize_config(raw) - assert sanitized["version"] == "1.0.0" - - -# ============================================================================= -# PROMPT TESTS -# ============================================================================= - -class TestPrompts: - """Tests for prompt engineering module.""" - - def test_detect_uniref_pattern(self): - """Should detect UniRef prefix pattern.""" - from app.services.prompts import detect_value_patterns - - values = ["UniRef:UniRef90_A0A1B2", "UniRef:UniRef90_C3D4E5"] - patterns = detect_value_patterns(values) - - assert any("UniRef" in p for p in patterns) - - def test_detect_go_pattern(self): - """Should detect GO term pattern.""" - from app.services.prompts import detect_value_patterns - - values = ["GO:0008150", "GO:0003677", "GO:0006412"] - patterns = detect_value_patterns(values) - - assert any("GO" in p for p in patterns) - - def test_detect_sequence_pattern(self): - """Should detect DNA sequence pattern.""" - from app.services.prompts import detect_value_patterns - - values = ["ATCGATCGATCGATCGATCGATCGATCG", "GCTAGCTAGCTAGCTAGCTAGCTAGCTA"] - patterns = detect_value_patterns(values) - - assert any("sequence" in p.lower() for p in patterns) - - def test_detect_strand_pattern(self): - """Should detect strand indicator pattern.""" - from app.services.prompts import detect_value_patterns - - values = ["+", "-", "+", "+", "-"] - patterns = detect_value_patterns(values) - - assert any("strand" in p.lower() for p in patterns) - - def test_compute_numeric_stats(self): - """Should compute basic numeric statistics.""" - from app.services.prompts import compute_numeric_stats - - values = [1.5, 2.5, 3.5, 4.5, 5.5] - stats = compute_numeric_stats(values) - - assert stats is not None - assert stats["min"] == 1.5 - assert stats["max"] == 5.5 - assert stats["count"] == 5 - assert stats["has_decimals"] is True - - def test_compute_numeric_stats_non_numeric(self): - """Should return None for non-numeric values.""" - from app.services.prompts import compute_numeric_stats - - values = ["abc", "def", "ghi"] - stats = compute_numeric_stats(values) - - assert stats is None - - def test_build_prompt_structure(self): - """Generated prompt should have expected sections.""" - from app.services.prompts import build_table_config_prompt - - prompt = build_table_config_prompt( - table_name="genes", - schema_info=[{"name": "gene_id", "type": "TEXT"}], - sample_values={"gene_id": ["GENE001", "GENE002"]}, - detected_patterns={"gene_id": ["no_special_pattern"]}, - statistics={}, - row_count=100 - ) - - assert "genes" in prompt - assert "Sample Values" in prompt - assert "Detected Patterns" in prompt - assert "JSON" in prompt - - -# ============================================================================= -# TYPE INFERENCE TESTS -# ============================================================================= - -class TestTypeInference: - """Tests for enhanced type inference patterns.""" - - def test_uniref_chain_transform(self): - """UniRef columns should get chain transformer.""" - from app.services.type_inference import TypeInferenceEngine - - engine = TypeInferenceEngine() - result = engine.infer_from_name("uniref_90") - - assert result is not None - assert result.transform is not None - assert result.transform.type == "chain" - - def test_strand_badge_transform(self): - """Strand columns should get badge transformer.""" - from app.services.type_inference import TypeInferenceEngine - - engine = TypeInferenceEngine() - result = engine.infer_from_name("strand") - - assert result is not None - assert result.transform is not None - assert result.transform.type == "badge" - assert "colorMap" in result.transform.options - - def test_pfam_chain_transform(self): - """Pfam columns should get chain transformer.""" - from app.services.type_inference import TypeInferenceEngine - - engine = TypeInferenceEngine() - result = engine.infer_from_name("pfam_domain") - - assert result is not None - assert result.transform is not None - assert result.transform.type == "chain" - - def test_go_ontology_transform(self): - """GO columns should get ontology transformer.""" - from app.services.type_inference import TypeInferenceEngine - - engine = TypeInferenceEngine() - result = engine.infer_from_name("GO_terms") - - assert result is not None - assert result.transform is not None - assert result.transform.type == "ontology" - - -# ============================================================================= -# FINGERPRINT TESTS -# ============================================================================= - -class TestFingerprint: - """Tests for database fingerprinting.""" - - def test_compute_fingerprint(self, sample_db): - """Should compute consistent fingerprint.""" - from app.services.fingerprint import DatabaseFingerprint - - fp_service = DatabaseFingerprint() - fp1 = fp_service.compute(sample_db) - fp2 = fp_service.compute(sample_db) - - assert fp1 == fp2 - assert len(fp1) == 16 # SHA256 prefix - - def test_cache_and_retrieve(self, sample_config): - """Should cache and retrieve configs.""" - from app.services.fingerprint import DatabaseFingerprint - import tempfile - - with tempfile.TemporaryDirectory() as tmpdir: - fp_service = DatabaseFingerprint(config_dir=tmpdir) - - fingerprint = "test_fingerprint_123" - fp_service.cache_config(fingerprint, sample_config) - - retrieved = fp_service.get_cached_config(fingerprint) - - assert retrieved is not None - assert retrieved["id"] == sample_config["id"] - - def test_clear_cache(self, sample_config): - """Should clear cached configs.""" - from app.services.fingerprint import DatabaseFingerprint - import tempfile - - with tempfile.TemporaryDirectory() as tmpdir: - fp_service = DatabaseFingerprint(config_dir=tmpdir) - - fingerprint = "test_to_delete" - fp_service.cache_config(fingerprint, sample_config) - - assert fp_service.is_cached(fingerprint) is True - - deleted = fp_service.clear_cache(fingerprint) - assert deleted == 1 - - assert fp_service.is_cached(fingerprint) is False - - -# ============================================================================= -# CONFIG GENERATOR TESTS -# ============================================================================= - -class TestConfigGenerator: - """Tests for config generator.""" - - def test_generate_config(self, sample_db): - """Should generate valid config from database.""" - from app.services.config_generator import ConfigGenerator - from app.services.validation import validate_config - import tempfile - - with tempfile.TemporaryDirectory() as tmpdir: - generator = ConfigGenerator(config_dir=tmpdir) - result = generator.generate( - db_path=sample_db, - handle_ref="test/test/1", - force_regenerate=True, - ai_preference="rules-only" - ) - - assert result.tables_analyzed > 0 - assert result.config is not None - - # Validate generated config - is_valid, error = validate_config(result.config) - assert is_valid is True, f"Validation failed: {error}" - - def test_cache_hit(self, sample_db): - """Second generation should use cache.""" - from app.services.config_generator import ConfigGenerator - import tempfile - - with tempfile.TemporaryDirectory() as tmpdir: - generator = ConfigGenerator(config_dir=tmpdir) - - # First generation - result1 = generator.generate( - db_path=sample_db, - handle_ref="test/test/1", - ai_preference="rules-only" - ) - assert result1.cache_hit is False - - # Second generation (should hit cache) - result2 = generator.generate( - db_path=sample_db, - handle_ref="test/test/1", - ai_preference="rules-only" - ) - assert result2.cache_hit is True - - -# ============================================================================= -# FALLBACK REGISTRY TESTS -# ============================================================================= - -class TestFallbackRegistry: - """Tests for fallback config registry.""" - - def test_berdl_object_type_match(self): - """BERDL object type should match berdl_tables config.""" - from app.configs import get_fallback_config_id, has_fallback_config - - assert has_fallback_config("KBaseGeneDataLakes.BERDLTables-1.0") is True - assert get_fallback_config_id("KBaseGeneDataLakes.BERDLTables-1.0") == "berdl_tables" - - def test_genome_data_tables_match(self): - """GenomeDataTables should match genome_data_tables config.""" - from app.configs import get_fallback_config_id, has_fallback_config - - assert has_fallback_config("KBaseFBA.GenomeDataLakeTables-1.0") is True - assert get_fallback_config_id("KBaseFBA.GenomeDataLakeTables-1.0") == "genome_data_tables" - - def test_unknown_object_type(self): - """Unknown object type should return None.""" - from app.configs import get_fallback_config_id, has_fallback_config - - assert has_fallback_config("SomeUnknown.Type-1.0") is False - assert get_fallback_config_id("SomeUnknown.Type-1.0") is None - - def test_load_berdl_config(self): - """Should load and parse berdl_tables.json.""" - from app.configs import get_fallback_config - - config = get_fallback_config("KBaseGeneDataLakes.BERDLTables-1.0") - - assert config is not None - assert config["id"] == "berdl_tables" - assert "tables" in config - assert "genome_features" in config["tables"] - - def test_load_genome_data_tables_config(self): - """Should load and parse genome_data_tables.json.""" - from app.configs import get_fallback_config - - config = get_fallback_config("KBaseFBA.GenomeDataLakeTables-1.0") - - assert config is not None - assert config["id"] == "genome_data_tables" - assert "tables" in config - assert "Genes" in config["tables"] - - def test_list_available_configs(self): - """Should list all available configs.""" - from app.configs import list_available_configs - - configs = list_available_configs() - - assert len(configs) >= 2 - config_ids = [c["id"] for c in configs] - assert "berdl_tables" in config_ids - assert "genome_data_tables" in config_ids - - def test_config_cache(self): - """Configs should be cached after first load.""" - from app.configs import get_fallback_config, clear_cache - - # Clear cache first - clear_cache() - - # First load - config1 = get_fallback_config("KBaseGeneDataLakes.BERDLTables-1.0") - - # Second load (should use cache) - config2 = get_fallback_config("KBaseGeneDataLakes.BERDLTables-1.0") - - assert config1 is config2 # Same object reference - - -# ============================================================================= -# ENHANCED RESPONSE TESTS -# ============================================================================= - -class TestEnhancedResponses: - """Tests for enhanced API response models.""" - - def test_config_response_has_new_fields(self): - """ConfigGenerationResponse should have all new fields.""" - from app.models import ConfigGenerationResponse - - # Check field names exist - fields = ConfigGenerationResponse.model_fields - assert "fallback_used" in fields - assert "fallback_reason" in fields - assert "config_source" in fields - assert "db_schema" in fields # Note: aliased to "schema" in JSON - assert "ai_available" in fields - assert "ai_error" in fields - assert "api_version" in fields - - def test_table_list_response_has_new_fields(self): - """TableListResponse should have all new fields.""" - from app.models import TableListResponse - - fields = TableListResponse.model_fields - assert "schemas" in fields - assert "has_builtin_config" in fields - assert "builtin_config_id" in fields - assert "database_size_bytes" in fields - assert "total_rows" in fields - assert "api_version" in fields - - def test_backward_compatibility(self): - """Old clients should still work with minimal fields.""" - from app.models import ConfigGenerationResponse - - # Create response with only required fields - response = ConfigGenerationResponse( - status="generated", - fingerprint="test_fp", - config_url="/config/test", - config={"id": "test", "tables": {}}, - tables_analyzed=1, - columns_inferred=5, - generation_time_ms=100.0, - cache_hit=False, - ) - - # Should have default values for new fields - assert response.fallback_used is False - assert response.api_version == "2.0" - assert response.ai_available is True - diff --git a/tests/test_integration.py b/tests/test_integration.py deleted file mode 100644 index e8c61b1..0000000 --- a/tests/test_integration.py +++ /dev/null @@ -1,409 +0,0 @@ -""" -Integration Tests for Config Control Plane. - -End-to-end tests that verify the full workflow from config creation -to resolution and consumption. -""" - -import pytest -import tempfile -import json -from pathlib import Path -from fastapi.testclient import TestClient - -from app.main import create_app -from app.services.config_store import ConfigStore -from app.services.config_resolver import get_config_resolver -from app.models import ConfigCreateRequest, ConfigSourceType, ConfigState - - -@pytest.fixture -def temp_db(): - """Create temporary database for testing.""" - with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f: - db_path = Path(f.name) - - yield db_path - - if db_path.exists(): - db_path.unlink() - - -@pytest.fixture -def client(temp_db): - """Create test client with temporary database.""" - # Override config store DB path - import app.services.config_store - original_init = ConfigStore.__init__ - - def mock_init(self, db_path=None): - original_init(self, db_path=temp_db) - - ConfigStore.__init__ = mock_init - - app = create_app() - client = TestClient(app) - - yield client - - # Restore - ConfigStore.__init__ = original_init - - -@pytest.fixture -def sample_config(): - """Sample config for testing.""" - return { - "id": "test_integration", - "name": "Integration Test Config", - "version": "1.0.0", - "description": "Config for integration testing", - "tables": { - "Genes": { - "columns": { - "gene_id": { - "width": "150px", - "sortable": True, - "filterable": True - }, - "gene_name": { - "width": "200px", - "displayName": "Gene Name" - } - } - } - } - } - - -class TestConfigWorkflow: - """Test complete config lifecycle workflow.""" - - def test_create_propose_publish_workflow(self, client, sample_config): - """Test full lifecycle: create → propose → publish.""" - # 1. Create draft - response = client.post( - "/config", - json={ - "source_type": "object", - "source_ref": "test/1/1", - "config": sample_config, - "change_summary": "Integration test" - } - ) - assert response.status_code == 200 - config_id = response.json()["id"] - assert response.json()["state"] == "draft" - - # 2. Propose - response = client.post(f"/config/{config_id}/propose") - assert response.status_code == 200 - assert response.json()["status"] == "proposed" - - # Get config to verify state - response = client.get(f"/config/{config_id}") - assert response.json()["state"] == "proposed" - - # 3. Publish - response = client.post(f"/config/{config_id}/publish") - assert response.status_code == 200 - assert response.json()["status"] == "published" - - # Verify published - response = client.get(f"/config/{config_id}") - assert response.json()["state"] == "published" - assert response.json()["published_at"] is not None - - def test_resolution_after_publish(self, client, sample_config): - """Test that published config is available via resolve.""" - # Create and publish - create_resp = client.post( - "/config", - json={ - "source_type": "object", - "source_ref": "test/2/1", - "config": sample_config, - "object_type": "Test.ObjectType-1.0", - "change_summary": "For resolution test" - } - ) - config_id = create_resp.json()["id"] - - client.post(f"/config/{config_id}/propose") - client.post(f"/config/{config_id}/publish") - - # Resolve - response = client.get("/config/resolve/test/2/1") - assert response.status_code == 200 - data = response.json() - - assert data["source"] == "published" - assert data["config_id"] == config_id - assert data["config"]["id"] == "test_integration" - - def test_user_override_workflow(self, client, sample_config): - """Test user override creation and resolution.""" - # Create base config - create_resp = client.post( - "/config", - json={ - "source_type": "object", - "source_ref": "test/3/1", - "config": sample_config, - "change_summary": "Base config" - } - ) - config_id = create_resp.json()["id"] - client.post(f"/config/{config_id}/propose") - client.post(f"/config/{config_id}/publish") - - # Set user override - override_resp = client.post( - "/config/user/override", - json={ - "source_ref": "test/3/1", - "override_config": { - "tables": { - "Genes": { - "columns": { - "gene_id": { - "width": "300px", # Override - "pin": "left" # New field - } - } - } - } - }, - "priority": 50 - }, - headers={"Authorization": "Bearer test_token"} - ) - assert override_resp.status_code == 200 - - # Resolve with user ID - resolve_resp = client.get( - "/config/resolve/test/3/1?user_id=user:test" - ) - assert resolve_resp.status_code == 200 - data = resolve_resp.json() - - # Should use override - assert data["source"] == "user_override" - assert data["config"]["tables"]["Genes"]["columns"]["gene_id"]["width"] == "300px" - - def test_config_inheritance_workflow(self, client, sample_config): - """Test config inheritance and overlays.""" - # Create parent config - parent_resp = client.post( - "/config", - json={ - "source_type": "builtin", - "source_ref": "builtin:parent", - "config": sample_config, - "change_summary": "Parent config" - } - ) - parent_id = parent_resp.json()["id"] - client.post(f"/config/{parent_id}/propose") - client.post(f"/config/{parent_id}/publish") - - # Create child config with inheritance - child_resp = client.post( - "/config", - json={ - "source_type": "custom", - "source_ref": "custom:child", - "extends_id": parent_id, - "config": {}, - "change_summary": "Child config" - } - ) - child_id = child_resp.json()["id"] - - # Add overlays - client.patch( - f"/config/{child_id}", - json={ - "overlays": { - "tables": { - "Genes": { - "columns": { - "gene_id": { - "width": "250px" # Override parent - } - } - } - } - }, - "change_summary": "Added overlays" - } - ) - - # Publish child - client.post(f"/config/{child_id}/propose") - client.post(f"/config/{child_id}/publish") - - # Resolve child - should have parent + overlays - resolve_resp = client.get("/config/resolve/custom:child") - assert resolve_resp.status_code == 200 - data = resolve_resp.json() - - # Should have parent's structure - assert "Genes" in data["config"]["tables"] - # Should have overlay applied - assert data["config"]["tables"]["Genes"]["columns"]["gene_id"]["width"] == "250px" - - -class TestConfigTesting: - """Test config testing functionality.""" - - def test_config_testing_endpoint(self, client, sample_config): - """Test config testing endpoint.""" - # Create and publish config - create_resp = client.post( - "/config", - json={ - "source_type": "object", - "source_ref": "test/4/1", - "config": sample_config, - "change_summary": "For testing" - } - ) - config_id = create_resp.json()["id"] - client.post(f"/config/{config_id}/propose") - client.post(f"/config/{config_id}/publish") - - # Test config - test_resp = client.post( - "/config/test", - json={ - "config_id": config_id, - "test_types": ["schema", "performance", "integration"] - } - ) - assert test_resp.status_code == 200 - data = test_resp.json() - - assert data["config_id"] == config_id - assert len(data["results"]) == 3 - assert "overall_status" in data - - -class TestConfigDiff: - """Test config diff functionality.""" - - def test_config_diff_endpoint(self, client, sample_config): - """Test config diff endpoint.""" - # Create two configs - config1_resp = client.post( - "/config", - json={ - "source_type": "object", - "source_ref": "test/5/1", - "config": sample_config, - "change_summary": "Config 1" - } - ) - config1_id = config1_resp.json()["id"] - - # Modify config for second - modified_config = sample_config.copy() - modified_config["tables"]["Genes"]["columns"]["gene_id"]["width"] = "300px" - - config2_resp = client.post( - "/config", - json={ - "source_type": "object", - "source_ref": "test/5/2", - "config": modified_config, - "change_summary": "Config 2" - } - ) - config2_id = config2_resp.json()["id"] - - # Diff - diff_resp = client.post( - "/config/diff", - json={ - "config_id1": config1_id, - "config_id2": config2_id - } - ) - assert diff_resp.status_code == 200 - data = diff_resp.json() - - assert "modified" in data - assert "summary" in data - assert data["has_changes"] is True - - -class TestErrorHandling: - """Test error handling in workflows.""" - - def test_cannot_update_published_config(self, client, sample_config): - """Test that published configs cannot be updated.""" - # Create and publish - create_resp = client.post( - "/config", - json={ - "source_type": "object", - "source_ref": "test/6/1", - "config": sample_config, - "change_summary": "Test" - } - ) - config_id = create_resp.json()["id"] - client.post(f"/config/{config_id}/propose") - client.post(f"/config/{config_id}/publish") - - # Try to update - update_resp = client.patch( - f"/config/{config_id}", - json={ - "config": {"id": "modified"}, - "change_summary": "Trying to update" - } - ) - assert update_resp.status_code == 400 - - def test_resolution_fallback(self, client): - """Test resolution falls back when no config found.""" - # Resolve non-existent config - response = client.get("/config/resolve/nonexistent/ref/123") - assert response.status_code == 200 - data = response.json() - - # Should return default - assert data["source"] in ["default", "builtin"] - assert "config" in data - - -class TestPerformance: - """Test performance characteristics.""" - - def test_resolution_performance(self, client, sample_config): - """Test that resolution is fast.""" - import time - - # Create and publish - create_resp = client.post( - "/config", - json={ - "source_type": "object", - "source_ref": "test/7/1", - "config": sample_config, - "change_summary": "Performance test" - } - ) - config_id = create_resp.json()["id"] - client.post(f"/config/{config_id}/propose") - client.post(f"/config/{config_id}/publish") - - # Time resolution - start = time.time() - response = client.get("/config/resolve/test/7/1") - elapsed = (time.time() - start) * 1000 # ms - - assert response.status_code == 200 - assert elapsed < 500 # Should be < 500ms - assert response.json()["resolution_time_ms"] < 500 diff --git a/tests/test_performance.py b/tests/test_performance.py deleted file mode 100644 index b0e3f7d..0000000 --- a/tests/test_performance.py +++ /dev/null @@ -1,234 +0,0 @@ -""" -Performance Tests for Config Control Plane. - -Tests performance characteristics and benchmarks. -""" - -import pytest -import tempfile -import time -from pathlib import Path - -from app.services.config_store import ConfigStore -from app.services.config_resolver import get_config_resolver -from app.models import ConfigCreateRequest, ConfigSourceType, ConfigState - - -@pytest.fixture -def temp_db(): - """Create temporary database for testing.""" - with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f: - db_path = Path(f.name) - - store = ConfigStore(db_path=db_path) - - # Create test configs - for i in range(100): - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref=f"test/{i}/1", - config={ - "id": f"config_{i}", - "name": f"Config {i}", - "version": "1.0.0", - "tables": { - "Table1": { - "columns": { - f"col_{j}": {"width": "100px"} - for j in range(10) - } - } - } - }, - change_summary=f"Test config {i}" - ) - record = store.create(request, "user:test") - store.propose(record.id, "user:test") - store.publish(record.id, "user:test") - - yield store, db_path - - if db_path.exists(): - db_path.unlink() - - -class TestResolutionPerformance: - """Test resolution performance.""" - - def test_single_resolution_performance(self, temp_db): - """Test single resolution is fast.""" - store, db_path = temp_db - resolver = get_config_resolver() - resolver.store = store - - start = time.time() - response = resolver.resolve("test/50/1") - elapsed = (time.time() - start) * 1000 - - assert response is not None - assert elapsed < 100 # Should be < 100ms for single lookup - - def test_batch_resolution_performance(self, temp_db): - """Test batch resolution performance.""" - store, db_path = temp_db - resolver = get_config_resolver() - resolver.store = store - - source_refs = [f"test/{i}/1" for i in range(50)] - - start = time.time() - results = [] - for ref in source_refs: - result = resolver.resolve(ref) - results.append(result) - elapsed = (time.time() - start) * 1000 - - assert len(results) == 50 - assert elapsed < 2000 # Should be < 2s for 50 resolutions - assert elapsed / 50 < 50 # Average < 50ms per resolution - - def test_fingerprint_resolution_performance(self, temp_db): - """Test fingerprint-based resolution performance.""" - store, db_path = temp_db - - # Create config with fingerprint - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="test/fingerprint/1", - config={"id": "fp_test", "name": "Fingerprint Test"}, - fingerprint="test_fingerprint_123" - ) - record = store.create(request, "user:test") - store.propose(record.id, "user:test") - store.publish(record.id, "user:test") - - resolver = get_config_resolver() - resolver.store = store - - start = time.time() - response = resolver.resolve( - "test/fingerprint/1", - fingerprint="test_fingerprint_123" - ) - elapsed = (time.time() - start) * 1000 - - assert response is not None - assert elapsed < 100 # Fingerprint lookup should be fast - - -class TestDatabasePerformance: - """Test database query performance.""" - - def test_list_performance(self, temp_db): - """Test listing configs is fast.""" - store, db_path = temp_db - - start = time.time() - configs, total = store.list_configs(page=1, per_page=20) - elapsed = (time.time() - start) * 1000 - - assert len(configs) == 20 - assert elapsed < 100 # Should be < 100ms - - def test_filtered_list_performance(self, temp_db): - """Test filtered listing performance.""" - store, db_path = temp_db - - start = time.time() - configs, total = store.list_configs( - state=ConfigState.PUBLISHED, - page=1, - per_page=20 - ) - elapsed = (time.time() - start) * 1000 - - assert elapsed < 150 # Filtered queries should still be fast - - def test_object_type_lookup_performance(self, temp_db): - """Test object type lookup performance.""" - store, db_path = temp_db - - # Create config with object type - request = ConfigCreateRequest( - source_type=ConfigSourceType.BUILTIN, - source_ref="builtin:test", - config={"id": "test", "name": "Test"}, - object_type="Test.ObjectType-1.0" - ) - record = store.create(request, "user:test") - store.propose(record.id, "user:test") - store.publish(record.id, "user:test") - - start = time.time() - resolved = store.resolve( - "unknown_ref", - object_type="Test.ObjectType-1.0" - ) - elapsed = (time.time() - start) * 1000 - - assert resolved is not None - assert elapsed < 100 # Object type lookup should be fast - - -class TestConcurrentAccess: - """Test concurrent access performance.""" - - def test_concurrent_resolution(self, temp_db): - """Test concurrent resolution requests.""" - import concurrent.futures - - store, db_path = temp_db - resolver = get_config_resolver() - resolver.store = store - - source_refs = [f"test/{i}/1" for i in range(20)] - - def resolve_one(ref): - return resolver.resolve(ref) - - start = time.time() - with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor: - results = list(executor.map(resolve_one, source_refs)) - elapsed = (time.time() - start) * 1000 - - assert len(results) == 20 - assert all(r is not None for r in results) - assert elapsed < 500 # Concurrent should still be fast - - -class TestMemoryUsage: - """Test memory usage characteristics.""" - - def test_large_config_handling(self, temp_db): - """Test handling of large configs.""" - store, db_path = temp_db - - # Create config with many tables/columns - large_config = { - "id": "large_config", - "name": "Large Config", - "version": "1.0.0", - "tables": { - f"Table_{i}": { - "columns": { - f"col_{j}": {"width": "100px"} - for j in range(50) - } - } - for i in range(20) - } - } - - request = ConfigCreateRequest( - source_type=ConfigSourceType.OBJECT, - source_ref="test/large/1", - config=large_config, - change_summary="Large config test" - ) - - start = time.time() - record = store.create(request, "user:test") - elapsed = (time.time() - start) * 1000 - - assert record is not None - assert elapsed < 500 # Should handle large configs reasonably diff --git a/tests/unit/test_query_service.py b/tests/unit/test_query_service.py new file mode 100644 index 0000000..c57b719 --- /dev/null +++ b/tests/unit/test_query_service.py @@ -0,0 +1,107 @@ +import unittest +import sqlite3 +import tempfile +import shutil +import logging +from pathlib import Path +from app.services.data.query_service import QueryService, FilterSpec, AggregationSpec +from app.exceptions import TableNotFoundError + +# Configure logging +logging.basicConfig(level=logging.ERROR) + +class TestQueryService(unittest.TestCase): + def setUp(self): + self.temp_dir = tempfile.mkdtemp() + self.db_path = Path(self.temp_dir) / "test.db" + self.service = QueryService() + + # Create a test database + conn = sqlite3.connect(self.db_path) + cursor = conn.cursor() + cursor.execute("CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT, age INTEGER, salary REAL, status TEXT)") + data = [ + (1, "Alice", 30, 50000.0, "active"), + (2, "Bob", 25, 45000.5, "inactive"), + (3, "Charlie", 35, 70000.0, "active"), + (4, "David", 30, 52000.0, "active"), + (5, "Eve", 28, 49000.0, "inactive"), + ] + cursor.executemany("INSERT INTO users VALUES (?, ?, ?, ?, ?)", data) + conn.commit() + conn.close() + + def tearDown(self): + shutil.rmtree(self.temp_dir) + + def test_simple_select(self): + result = self.service.execute_query(self.db_path, "users", limit=10) + self.assertEqual(len(result["data"]), 5) + self.assertEqual(result["total_count"], 5) + self.assertEqual(result["headers"], ["id", "name", "age", "salary", "status"]) + + def test_filter_numeric(self): + filters = [FilterSpec(column="age", operator="gt", value=28)] + result = self.service.execute_query(self.db_path, "users", filters=filters) + # Should be Alice(30), Charlie(35), David(30) + self.assertEqual(len(result["data"]), 3) + self.assertEqual(result["total_count"], 3) + + def test_filter_text(self): + filters = [FilterSpec(column="status", operator="eq", value="active")] + result = self.service.execute_query(self.db_path, "users", filters=filters) + self.assertEqual(len(result["data"]), 3) + + def test_sorting(self): + # Sort by age DESC + result = self.service.execute_query(self.db_path, "users", sort_column="age", sort_order="DESC") + data = result["data"] + # Charlie(35) first + self.assertEqual(data[0][1], "Charlie") + # Bob(25) last + self.assertEqual(data[4][1], "Bob") + + def test_aggregation(self): + aggs = [ + AggregationSpec(column="salary", function="avg", alias="avg_salary"), + AggregationSpec(column="status", function="count", alias="count") + ] + result = self.service.execute_query( + self.db_path, "users", + aggregations=aggs, + group_by=["status"], + sort_column="status" + ) + + self.assertEqual(len(result["data"]), 2) + row_active = next(r for r in result["data"] if r[0] == "active") + + # Active: Alice(50k), Charlie(70k), David(52k) -> Avg 57333.33 + self.assertAlmostEqual(float(row_active[1]), 57333.33, delta=0.1) + self.assertEqual(int(row_active[2]), 3) + + def test_sql_injection_sort_ignored(self): + """Ensure sort column injection attacks are ignored (fallback to default).""" + bad_col = "age; DROP TABLE users; --" + result = self.service.execute_query(self.db_path, "users", sort_column=bad_col) + self.assertEqual(len(result["data"]), 5) + + # Verify table still exists + conn = sqlite3.connect(self.db_path) + cursor = conn.cursor() + cursor.execute("SELECT count(*) FROM users") + self.assertEqual(cursor.fetchone()[0], 5) + conn.close() + + def test_sql_injection_filter_safe(self): + """Ensure filter value injection is handled safely as literal string.""" + filters = [FilterSpec(column="name", operator="eq", value="Alice' OR '1'='1")] + result = self.service.execute_query(self.db_path, "users", filters=filters) + self.assertEqual(len(result["data"]), 0) + + def test_missing_table(self): + with self.assertRaises(TableNotFoundError): + self.service.execute_query(self.db_path, "non_existent_table") + +if __name__ == "__main__": + unittest.main() From 297c33bac0417d747d5952696ffd5e5ced5d85f8 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Fri, 16 Jan 2026 14:16:17 -0600 Subject: [PATCH 09/19] legacy deprecation --- app/models.py | 42 ------ app/routes.py | 241 +------------------------------ app/utils/workspace.py | 53 +------ tests/integration/test_routes.py | 8 +- 4 files changed, 19 insertions(+), 325 deletions(-) diff --git a/app/models.py b/app/models.py index e64a48c..9dee55c 100644 --- a/app/models.py +++ b/app/models.py @@ -171,50 +171,8 @@ class TableListResponse(BaseModel): ) -class PangenomeInfo(BaseModel): - """Information about a pangenome found in the SQLite file.""" - pangenome_taxonomy: str | None = Field(None, description="Taxonomy of the pangenome", examples=["Escherichia coli"]) - genome_count: int = Field(..., description="Number of genomes in the pangenome", examples=[42]) - source_berdl_id: str = Field(..., description="Source BERDL Table ID", examples=["76990/7/2"]) - user_genomes: list[str] = Field( - default_factory=list, - description="List of user-provided genome references", - examples=[["76990/1/1", "76990/2/1"]] - ) - berdl_genomes: list[str] = Field( - default_factory=list, - description="List of BERDL/Datalake genome identifiers", - examples=[["GLM4:EC_G1", "GLM4:EC_G2"]] - ) - handle_ref: str | None = Field( - None, - description="Blobstore handle reference for SQLite database", - examples=["KBH_248028"] - ) -class PangenomesResponse(BaseModel): - """Response for listing pangenomes from a BERDLTables object.""" - berdl_table_id: str | None = Field(None, description="BERDLTable object reference", examples=["76990/7/2"]) - object_type: str | None = Field(None, description="KBase object type", examples=["KBaseGeneDataLakes.BERDLTables-1.0"]) - pangenomes: list[PangenomeInfo] = Field( - default_factory=list, - description="List of available pangenomes", - examples=[[ - { - "pangenome_taxonomy": "Escherichia coli", - "genome_count": 42, - "source_berdl_id": "76990/7/2", - "handle_ref": "KBH_248028" - } - ]] - ) - pangenome_count: int = Field( - 1, - description="Total number of pangenomes", - examples=[1] - ) - class TableDataResponse(BaseModel): """ diff --git a/app/routes.py b/app/routes.py index 4a1e3bc..c4b49d0 100644 --- a/app/routes.py +++ b/app/routes.py @@ -23,8 +23,6 @@ from app.models import ( TableDataRequest, TableDataResponse, - PangenomesResponse, - PangenomeInfo, TableListResponse, TableInfo, CacheResponse, @@ -40,7 +38,6 @@ AggregationRequest, ) from app.utils.workspace import ( - list_pangenomes_from_object, download_pangenome_db, get_object_type, ) @@ -53,7 +50,6 @@ from app.services.data.schema_service import get_schema_service from app.services.data.connection_pool import get_connection_pool from app.services.db_helper import ( - get_handle_db_path, get_object_db_path, ensure_table_accessible, ) @@ -143,171 +139,12 @@ async def health_check(): raise HTTPException(status_code=500, detail=str(e)) -# ============================================================================= -# HANDLE-BASED ENDPOINTS (Primary REST API per diagram) -# /{handle_ref}/tables - List tables -# /{handle_ref}/tables/{table}/schema - Table schema -# /{handle_ref}/tables/{table}/data - Table data with pagination -# ============================================================================= - -@router.get("/handle/{handle_ref}/tables", tags=["Handle Access"], response_model=TableListResponse) -async def list_tables_by_handle( - handle_ref: str, - kb_env: str = Query("appdev", description="KBase environment"), - authorization: str | None = Header(None) -): - """ - List all tables in a SQLite database accessed via handle reference. - """ - try: - token = get_auth_token(authorization) - cache_dir = get_cache_dir() - - # Get database path (handles download and caching) - db_path = await get_handle_db_path(handle_ref, token, kb_env, cache_dir) - - # List tables - table_names = await run_sync_in_thread(list_tables, db_path) - tables = [] - - # Get details for each table - for name in table_names: - try: - # Run these lightweight checks in thread pool too - columns = await run_sync_in_thread(get_table_columns, db_path, name) - row_count = await run_sync_in_thread(get_table_row_count, db_path, name) - tables.append({ - "name": name, - "row_count": row_count, - "column_count": len(columns) - }) - except Exception: - logger.warning("Error getting table info for %s", name, exc_info=True) - tables.append({"name": name}) - - return { - "handle_ref": handle_ref, - "tables": tables, - "db_path": str(db_path) - } - - except HTTPException: - raise - except Exception as e: - logger.error(f"Error listing tables from handle: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -@router.get("/handle/{handle_ref}/tables/{table_name}/schema", tags=["Handle Access"], response_model=TableSchemaResponse) -async def get_table_schema_by_handle( - handle_ref: str, - table_name: str, - kb_env: str = Query("appdev"), - authorization: str | None = Header(None) -): - """ - Get schema (columns) for a table accessed via handle reference. - """ - try: - token = get_auth_token(authorization) - cache_dir = get_cache_dir() - - db_path = await get_handle_db_path(handle_ref, token, kb_env, cache_dir) - await ensure_table_accessible(db_path, table_name) - - columns = await run_sync_in_thread(get_table_columns, db_path, table_name) - row_count = await run_sync_in_thread(get_table_row_count, db_path, table_name) - - return { - "handle_ref": handle_ref, - "table_name": table_name, - "columns": columns, - "row_count": row_count - } - - except HTTPException: - raise - except Exception as e: - logger.error(f"Error getting schema: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -@router.get("/handle/{handle_ref}/tables/{table_name}/data", tags=["Handle Access"], response_model=TableDataResponse) -async def get_table_data_by_handle( - handle_ref: str, - table_name: str, - limit: int = Query(DEFAULT_LIMIT, ge=1, le=MAX_LIMIT), - offset: int = Query(0, ge=0), - sort_column: str | None = Query(None), - sort_order: str | None = Query("ASC"), - search: str | None = Query(None, description="Global search term"), - kb_env: str = Query("appdev"), - authorization: str | None = Header(None) -): - """ - Query table data from SQLite via handle reference. - """ - try: - token = get_auth_token(authorization) - cache_dir = get_cache_dir() - - db_path = await get_handle_db_path(handle_ref, token, kb_env, cache_dir) - await ensure_table_accessible(db_path, table_name) - - return await TableRequestProcessor.process_data_request( - db_path=db_path, - table_name=table_name, - limit=limit, - offset=offset, - sort_column=sort_column, - sort_order=sort_order or "ASC", - search_value=search, - handle_ref_or_id=handle_ref - ) - - except HTTPException: - raise - except Exception as e: - logger.error(f"Error querying data: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - # ============================================================================= # OBJECT-BASED ENDPOINTS (via KBase workspace object reference) -# /object/{ws_ref}/pangenomes - List pangenomes from BERDLTables object -# /object/{ws_ref}/pangenomes/{pg_id}/tables - List tables for a pangenome -# /object/{ws_ref}/pangenomes/{pg_id}/tables/{table}/data - Query data +# /object/{ws_ref}/tables - List tables from KBase object +# /object/{ws_ref}/tables/{table}/data - Query data # ============================================================================= -@router.get("/object/{ws_ref:path}/pangenomes", tags=["Object Access"], response_model=PangenomesResponse) -async def list_pangenomes_by_object( - ws_ref: str, - kb_env: str = Query("appdev"), - authorization: str | None = Header(None) -): - """ - List pangenomes from a BERDLTables/GenomeDataLakeTables object. - """ - try: - token = get_auth_token(authorization) - berdl_table_id = ws_ref - - pangenomes = list_pangenomes_from_object( - berdl_table_id=berdl_table_id, - auth_token=token, - kb_env=kb_env - ) - - return { - "berdl_table_id": berdl_table_id, - "pangenomes": pangenomes - } - - except Exception as e: - logger.error(f"Error listing pangenomes: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - @router.get("/object/{ws_ref:path}/tables", tags=["Object Access"], response_model=TableListResponse) async def list_tables_by_object( ws_ref: str, @@ -468,79 +305,10 @@ async def get_table_data_by_object( # ============================================================================= -# LEGACY ENDPOINTS (for backwards compatibility) +# DATA ACCESS ENDPOINTS # ============================================================================= -@router.get("/pangenomes", response_model=PangenomesResponse, tags=["Legacy"]) -async def get_pangenomes( - berdl_table_id: str = Query(..., description="BERDLTables object reference"), - kb_env: str = Query("appdev"), - authorization: str | None = Header(None) -): - """ - List pangenomes from BERDLTables object. - """ - try: - token = get_auth_token(authorization) - - # Support comma-separated list of IDs - berdl_ids = [bid.strip() for bid in berdl_table_id.split(",") if bid.strip()] - - all_pangenomes: list[dict] = [] - - for bid in berdl_ids: - try: - pangenomes = list_pangenomes_from_object(bid, token, kb_env) - # Tag each pangenome with its source ID - for pg in pangenomes: - pg["source_berdl_id"] = bid - all_pangenomes.extend(pangenomes) - except Exception as e: - logger.error(f"Error fetching pangenomes for {bid}: {e}") - # Continue fetching others even if one fails - continue - - pangenome_list = [PangenomeInfo(**pg) for pg in all_pangenomes] - - return PangenomesResponse( - pangenomes=pangenome_list, - pangenome_count=len(pangenome_list) - ) - except Exception as e: - logger.error(f"Error in get_pangenomes: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -@router.get("/tables", response_model=TableListResponse, tags=["Legacy"]) -async def get_tables( - berdl_table_id: str = Query(..., description="BERDLTables object reference"), - kb_env: str = Query("appdev"), - authorization: str | None = Header(None) -): - """List tables for a BERDLTable object (auto-resolves pangenome).""" - try: - token = get_auth_token(authorization) - cache_dir = get_cache_dir() - - db_path = download_pangenome_db(berdl_table_id, token, cache_dir, kb_env) - table_names = list_tables(db_path) - - tables = [] - for name in table_names: - try: - columns = get_table_columns(db_path, name) - row_count = get_table_row_count(db_path, name) - tables.append(TableInfo(name=name, row_count=row_count, column_count=len(columns))) - except Exception: - tables.append(TableInfo(name=name)) - - return TableListResponse(tables=tables) - except Exception as e: - logger.error(f"Error listing tables: {e}") - raise HTTPException(status_code=500, detail=str(e)) - - -@router.post("/table-data", response_model=TableDataResponse, tags=["Legacy"]) +@router.post("/table-data", response_model=TableDataResponse, tags=["Data Access"]) async def query_table_data( request: TableDataRequest, authorization: str | None = Header(None) @@ -596,3 +364,4 @@ async def query_table_data( except Exception as e: logger.error(f"Error querying data: {e}") raise HTTPException(status_code=500, detail=str(e)) + diff --git a/app/utils/workspace.py b/app/utils/workspace.py index d6c1875..65c43b4 100644 --- a/app/utils/workspace.py +++ b/app/utils/workspace.py @@ -373,47 +373,6 @@ def get_object_type( return object_type -def list_pangenomes_from_object( - berdl_table_id: str, - auth_token: str, - kb_env: str = "appdev" -) -> list[dict[str, Any]]: - """ - List all pangenomes from a BERDLTables object. - - Args: - berdl_table_id: KBase workspace reference - auth_token: KBase authentication token - kb_env: KBase environment - - Returns: - List of pangenome info dictionaries with: - - pangenome_id - - pangenome_taxonomy - - handle_ref - - user_genomes - - berdl_genomes - """ - obj_data = get_berdl_table_data(berdl_table_id, auth_token, kb_env) - - pangenome_data = obj_data.get("pangenome_data", []) - - pangenomes = [] - for pg in pangenome_data: - pangenomes.append({ - - "pangenome_taxonomy": pg.get("pangenome_taxonomy", ""), - "user_genomes": pg.get("user_genomes", []), - "berdl_genomes": pg.get("berdl_genomes", []), - "genome_count": len(pg.get("user_genomes", [])) + len(pg.get("berdl_genomes", [])), - "handle_ref": pg.get("sqllite_tables_handle_ref", ""), - }) - - return pangenomes - - - - def download_pangenome_db( berdl_table_id: str, @@ -451,12 +410,16 @@ def download_pangenome_db( return db_path # Fetch object metadata to get handle reference - pangenomes = list_pangenomes_from_object(berdl_table_id, auth_token, kb_env) - if not pangenomes: - raise ValueError(f"No pangenomes found in {berdl_table_id}") + obj_data = get_berdl_table_data(berdl_table_id, auth_token, kb_env) + pangenome_data = obj_data.get("pangenome_data", []) + if not pangenome_data: + raise ValueError(f"No pangenomes found in {berdl_table_id}") + # Take the first (and only expected) pangenome's handle - handle_ref = pangenomes[0]["handle_ref"] + handle_ref = pangenome_data[0].get("sqllite_tables_handle_ref") + if not handle_ref: + raise ValueError(f"No handle reference found in {berdl_table_id}") # Create cache directory db_dir.mkdir(parents=True, exist_ok=True) diff --git a/tests/integration/test_routes.py b/tests/integration/test_routes.py index 50bc029..ff9a954 100644 --- a/tests/integration/test_routes.py +++ b/tests/integration/test_routes.py @@ -23,9 +23,13 @@ def test_openapi_schema_structure(self): data = response.json() self.assertIn("paths", data) # Verify Key Endpoints exist - self.assertIn("/handle/{handle_ref}/tables", data["paths"]) self.assertIn("/object/{ws_ref}/tables", data["paths"]) - # Verify Legacy Config endpoints are GONE + self.assertIn("/table-data", data["paths"]) + + # Verify Deprecated Endpoints are GONE + self.assertNotIn("/handle/{handle_ref}/tables", data["paths"]) + self.assertNotIn("/pangenomes", data["paths"]) + self.assertNotIn("/tables", data["paths"]) self.assertNotIn("/config/providers", data["paths"]) self.assertNotIn("/config/resolve", data["paths"]) From 02c6eed9778db4e1a120778126e8b903a9ed30ab Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Fri, 16 Jan 2026 14:29:29 -0600 Subject: [PATCH 10/19] minor fixes --- app/main.py | 3 +- app/routes.py | 9 +++--- app/services/data/fingerprint.py | 4 +-- app/services/data/schema_analyzer.py | 4 ++- app/services/data/schema_service.py | 3 +- app/services/data/statistics_service.py | 2 +- app/services/data/validation.py | 19 +++++++++-- app/utils/workspace.py | 34 ++++++++++++++++--- docs/API.md | 43 +++++-------------------- docs/ARCHITECTURE.md | 2 -- tests/integration/test_routes.py | 7 ++-- 11 files changed, 70 insertions(+), 60 deletions(-) diff --git a/app/main.py b/app/main.py index 9714b33..b20519d 100644 --- a/app/main.py +++ b/app/main.py @@ -7,6 +7,7 @@ Run with: uv run fastapi dev app/main.py """ +import os from pathlib import Path from fastapi import FastAPI from fastapi.staticfiles import StaticFiles @@ -30,7 +31,7 @@ def create_app() -> FastAPI: # Configure root_path for KBase dynamic services # KBase services are often deployed at /services/service_name # Pydantic Settings management or manual environ check can handle this. - import os + # Pydantic Settings management or manual environ check can handle this. root_path = os.environ.get("KB_SERVICE_ROOT_PATH", "") diff --git a/app/routes.py b/app/routes.py index c4b49d0..c5fbe8a 100644 --- a/app/routes.py +++ b/app/routes.py @@ -12,10 +12,10 @@ """ -import time +import asyncio import logging +from datetime import datetime from pathlib import Path -from uuid import uuid4 from app.utils.workspace import KBaseClient from fastapi import APIRouter, HTTPException, Header, Query @@ -112,7 +112,7 @@ async def health_check(): Returns service status, cache information, and connection pool stats. """ - from datetime import datetime + try: # Get connection pool stats (non-blocking) @@ -154,7 +154,7 @@ async def list_tables_by_object( """ List tables for a BERDLTables object. """ - import asyncio + try: token = get_auth_token(authorization) @@ -211,7 +211,6 @@ async def list_tables_by_object( # Get object type (non-blocking) try: # Use specific timeout for API call - import asyncio object_type = await asyncio.wait_for( run_sync_in_thread(get_object_type, berdl_table_id, token, kb_env), timeout=settings.KBASE_API_TIMEOUT_SECONDS diff --git a/app/services/data/fingerprint.py b/app/services/data/fingerprint.py index d273459..04051ed 100644 --- a/app/services/data/fingerprint.py +++ b/app/services/data/fingerprint.py @@ -11,6 +11,8 @@ import hashlib import json import logging +import os +from datetime import datetime, timezone from pathlib import Path from typing import Any @@ -39,7 +41,6 @@ def __init__(self, config_dir: str | Path | None = None) -> None: Args: config_dir: Directory for storing cached configs """ - import os default_dir = os.getenv("GENERATED_CONFIG_DIR", "/tmp/tablescanner_configs") self.config_dir = Path(config_dir or default_dir) self.config_dir.mkdir(parents=True, exist_ok=True) @@ -227,5 +228,4 @@ def _get_cache_path(self, fingerprint: str) -> Path: def _get_timestamp(self) -> str: """Get current ISO timestamp.""" - from datetime import datetime, timezone return datetime.now(timezone.utc).isoformat() diff --git a/app/services/data/schema_analyzer.py b/app/services/data/schema_analyzer.py index 94cc32a..6bd3908 100644 --- a/app/services/data/schema_analyzer.py +++ b/app/services/data/schema_analyzer.py @@ -9,6 +9,8 @@ import logging import sqlite3 +import sys +import re from dataclasses import dataclass, field from pathlib import Path from typing import Any @@ -348,8 +350,8 @@ def _detect_patterns(self, values: list[Any]) -> list[str]: if all(v.startswith("GO:") for v in str_values): patterns.append("go_term") + # Check for ISO date pattern - import re date_pattern = re.compile(r"^\d{4}-\d{2}-\d{2}") if all(date_pattern.match(v) for v in str_values): patterns.append("iso_date") diff --git a/app/services/data/schema_service.py b/app/services/data/schema_service.py index f0d57f7..4db9985 100644 --- a/app/services/data/schema_service.py +++ b/app/services/data/schema_service.py @@ -17,6 +17,7 @@ from app.services.data.connection_pool import get_connection_pool from app.services.data.query_service import QueryService +from app.utils.sqlite import list_tables logger = logging.getLogger(__name__) @@ -84,7 +85,7 @@ def get_all_tables_schema( Returns: Dictionary mapping table names to schema information """ - from app.utils.sqlite import list_tables + """ table_names = list_tables(db_path) schemas = {} diff --git a/app/services/data/statistics_service.py b/app/services/data/statistics_service.py index f3b45d6..11ef552 100644 --- a/app/services/data/statistics_service.py +++ b/app/services/data/statistics_service.py @@ -12,6 +12,7 @@ import logging import time import threading +import math from pathlib import Path from typing import Any from collections import OrderedDict @@ -282,7 +283,6 @@ def _compute_column_statistics( ''', (stats.mean, stats.mean)) variance_row = cursor.fetchone() if variance_row and variance_row[0] is not None: - import math variance = float(variance_row[0]) stats.stddev = math.sqrt(variance) if variance >= 0 else None diff --git a/app/services/data/validation.py b/app/services/data/validation.py index de9d9db..553c038 100644 --- a/app/services/data/validation.py +++ b/app/services/data/validation.py @@ -12,6 +12,16 @@ logger = logging.getLogger(__name__) +try: + from jsonschema import validate, ValidationError, Draft7Validator + HAS_JSONSCHEMA = True +except ImportError: + HAS_JSONSCHEMA = False + # Dummy objects if needed + validate = None + ValidationError = Exception + Draft7Validator = None + # ============================================================================= # JSON SCHEMAS @@ -152,7 +162,8 @@ def validate_config(config: dict[str, Any]) -> tuple[bool, str | None]: Tuple of (is_valid, error_message) """ try: - from jsonschema import validate, ValidationError, Draft7Validator + if not HAS_JSONSCHEMA: + raise ImportError("jsonschema not available") validator = Draft7Validator(DATATYPE_CONFIG_SCHEMA) errors = list(validator.iter_errors(config)) @@ -184,7 +195,8 @@ def validate_table_config(table_config: dict[str, Any]) -> tuple[bool, str | Non Tuple of (is_valid, error_message) """ try: - from jsonschema import validate, ValidationError + if not HAS_JSONSCHEMA: + raise ImportError("jsonschema not available") validate(instance=table_config, schema=TABLE_SCHEMA) return True, None @@ -206,7 +218,8 @@ def validate_ai_response(response: dict[str, Any]) -> tuple[bool, str | None]: Tuple of (is_valid, error_message) """ try: - from jsonschema import validate + if not HAS_JSONSCHEMA: + raise ImportError("jsonschema not available") validate(instance=response, schema=AI_RESPONSE_SCHEMA) return True, None diff --git a/app/utils/workspace.py b/app/utils/workspace.py index 65c43b4..4c4e0f2 100644 --- a/app/utils/workspace.py +++ b/app/utils/workspace.py @@ -12,6 +12,19 @@ if str(LIB_PATH) not in sys.path: sys.path.insert(0, str(LIB_PATH)) +# Try conditional imports at top level +try: + from kbutillib.kb_ws_utils import KBWSUtils + from kbutillib.notebook_utils import NotebookUtils + HAS_KBUTILLIB = True +except ImportError: + HAS_KBUTILLIB = False + # Define dummy classes if needed for type hinting or logic check + KBWSUtils = object + NotebookUtils = object + +from app.config import settings + # Configure module logger logger = logging.getLogger(__name__) @@ -54,8 +67,8 @@ def __init__( def _init_client(self): """Initialize the appropriate client.""" try: - from kbutillib.kb_ws_utils import KBWSUtils - from kbutillib.notebook_utils import NotebookUtils + if not HAS_KBUTILLIB: + raise ImportError("KBUtilLib not found") # Create a proper combined class cache_dir = self.cache_dir @@ -131,7 +144,6 @@ def download_blob_file(self, handle_ref: str, target_path: Path) -> Path: def _get_endpoints(self) -> dict[str, str]: """Get endpoints for current environment.""" # If the requested env matches the configured env, use the configured URLs - from app.config import settings if self.kb_env == settings.KB_ENV: return { "workspace": settings.WORKSPACE_URL, @@ -262,6 +274,18 @@ def _get_object_type(self, ref: str) -> str: return infos[0][2] return "Unknown" + + def get_object_type_only(self, ref: str) -> str: + """ + Public method to get object type without fetching full data. + + Args: + ref: Object reference + + Returns: + Object type string + """ + return self._get_object_type(ref) def _download_blob_fallback(self, handle_ref: str, target_path: str) -> str: """Download from blobstore via direct API.""" @@ -368,9 +392,9 @@ def get_object_type( Returns: Object type string (e.g., "KBaseGeneDataLakes.BERDLTables-1.0") """ + """ client = KBaseClient(auth_token, kb_env) - _, object_type = client.get_object_with_type(berdl_table_id) - return object_type + return client.get_object_type_only(berdl_table_id) diff --git a/docs/API.md b/docs/API.md index b5701a9..751dbf6 100644 --- a/docs/API.md +++ b/docs/API.md @@ -1,6 +1,6 @@ # TableScanner API -The **TableScanner** service provides read-only access to SQLite databases stored in KBase (via Blobstore handles or Workspace objects). It supports listing tables, inspecting schemas, and querying data with filtering, sorting, and pagination. +The **TableScanner** service provides read-only access to SQLite databases stored in KBase (via Workspace objects). It supports listing tables, inspecting schemas, and querying data with filtering, sorting, and pagination. ## Base URL - **Development**: `http://localhost:8000` @@ -23,19 +23,14 @@ Detailed health check including connection pool stats. --- -## 2. Handle Access -Access databases via Blobstore Handle Reference (e.g., `KBH_12345`). - -### `GET /handle/{handle_ref}/tables` -List all tables in the database. -- **Query Params**: `kb_env` (default: `appdev`) -- **Response**: List of tables with row/column counts. +## 2. Object Access +Access databases via KBase Workspace Object Reference (UPA, e.g., `76990/7/2`). -### `GET /handle/{handle_ref}/tables/{table_name}/schema` -Get column definitions for a table. -- **Response**: Columns list (name, type, notnull, pk). +### `GET /object/{ws_ref}/tables` +List tables for a BERDLTables object. +- **Response**: Table list with schema overviews. -### `GET /handle/{handle_ref}/tables/{table_name}/data` +### `GET /object/{ws_ref}/tables/{table_name}/data` Query table data. - **Query Params**: - `limit` (default: 100) @@ -46,29 +41,7 @@ Query table data. --- -## 3. Object Access -Access databases via KBase Workspace Object Reference (UPA, e.g., `76990/7/2`). - -### `GET /object/{ws_ref}/pangenomes` -List pangenomes associated with a BERDLTables object. - -### `GET /object/{ws_ref}/tables` -List tables for a BERDLTables object. -- **Response**: Table list with schema overviews. - -### `GET /object/{ws_ref}/tables/{table_name}/data` -Query table data (same parameters as Handle Access). - ---- - -## 4. Legacy Endpoints -Maintained for backward compatibility. - -### `GET /pangenomes` -List pangenomes by `berdl_table_id`. - -### `GET /tables` -List tables by `berdl_table_id`. +## 3. Data Access ### `POST /table-data` Complex query endpoint supporting advanced filtering. diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md index f364682..80cc143 100644 --- a/docs/ARCHITECTURE.md +++ b/docs/ARCHITECTURE.md @@ -28,10 +28,8 @@ graph TD ### 1. API Layer (`app/routes.py`) The entry point for all requests. It handles: -- **Handle Access**: `/handle/{handle_ref}/tables` - **Object Access**: `/object/{ws_ref}/tables` - **Data Queries**: `/table-data` (Advanced filtering) -- **Legacy Compatibility**: Backward-compatible endpoints for older clients. ### 2. Query Service (`app/services/data/query_service.py`) The heart of the application. It orchestrates query execution: diff --git a/tests/integration/test_routes.py b/tests/integration/test_routes.py index ff9a954..13c03f9 100644 --- a/tests/integration/test_routes.py +++ b/tests/integration/test_routes.py @@ -8,10 +8,9 @@ def setUp(self): def test_health_check(self): response = self.client.get("/health") - # 500 is acceptable if integration test environment has no DB pool setup, - # but for unit/integration it should optimally be 200 or 503. - # Given this is a mock integration, we check it responds. - self.assertIn(response.status_code, [200, 500, 503]) + # 500/503 is NOT acceptable. Integration tests must ensure the application can start. + # The ConnectionPool does not require external connectivity to initialize. + self.assertEqual(response.status_code, 200) def test_api_docs_accessible(self): response = self.client.get("/docs") From 2f791c6be1ccf6a59a0d6e432f84039942a1fbfc Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Tue, 20 Jan 2026 09:32:26 -0600 Subject: [PATCH 11/19] code cleanup --- app/config.py | 4 + app/main.py | 21 +- app/models.py | 62 +++- app/routes.py | 10 +- app/services/data/connection_pool.py | 381 ++++++++++++---------- app/services/data/query_service.py | 215 ++++++------ app/services/data/schema_service.py | 19 +- app/services/data/statistics_service.py | 103 +++--- app/utils/__init__.py | 10 - app/utils/cache.py | 26 +- app/utils/request_utils.py | 115 +++++-- app/utils/workspace.py | 1 - pyproject.toml | 4 - tests/integration/test_concurrency.py | 175 ++++++++++ tests/integration/test_routes_advanced.py | 195 +++++++++++ tests/integration/test_security.py | 53 +++ tests/integration/test_security_fixes.py | 173 ++++++++++ 17 files changed, 1180 insertions(+), 387 deletions(-) create mode 100644 tests/integration/test_concurrency.py create mode 100644 tests/integration/test_routes_advanced.py create mode 100644 tests/integration/test_security.py create mode 100644 tests/integration/test_security_fixes.py diff --git a/app/config.py b/app/config.py index 525a056..90110f2 100644 --- a/app/config.py +++ b/app/config.py @@ -63,6 +63,10 @@ class Settings(BaseSettings): default="appdev", description="KBase environment (appdev, ci, prod)" ) + CORS_ORIGINS: list[str] = Field( + default=["*"], + description="List of allowed origins for CORS. Use ['*'] for all." + ) # Root path for proxy deployment (e.g., "/services/berdl_table_scanner") ROOT_PATH: str = "" diff --git a/app/main.py b/app/main.py index b20519d..371437b 100644 --- a/app/main.py +++ b/app/main.py @@ -9,12 +9,14 @@ import os from pathlib import Path -from fastapi import FastAPI +from fastapi import FastAPI, Request +from fastapi.responses import JSONResponse from fastapi.staticfiles import StaticFiles from fastapi.middleware.cors import CORSMiddleware from app.routes import router from app.config import settings +from app.exceptions import TableNotFoundError, InvalidFilterError def create_app() -> FastAPI: @@ -95,7 +97,7 @@ def create_app() -> FastAPI: # This is necessary when viewer.html is opened from file:// or different origin app.add_middleware( CORSMiddleware, - allow_origins=["*"], + allow_origins=settings.CORS_ORIGINS, allow_credentials=False, allow_methods=["*"], allow_headers=["*"], @@ -104,6 +106,21 @@ def create_app() -> FastAPI: # Store settings in app state for access throughout the application app.state.settings = settings + # Exception Handlers + @app.exception_handler(TableNotFoundError) + async def table_not_found_handler(request: Request, exc: TableNotFoundError): + return JSONResponse( + status_code=404, + content={"detail": str(exc)}, + ) + + @app.exception_handler(InvalidFilterError) + async def invalid_filter_handler(request: Request, exc: InvalidFilterError): + return JSONResponse( + status_code=422, + content={"detail": str(exc)}, + ) + # Include API routes app.include_router(router) diff --git a/app/models.py b/app/models.py index 9dee55c..66f8b5e 100644 --- a/app/models.py +++ b/app/models.py @@ -4,6 +4,8 @@ from typing import Any, Literal from pydantic import BaseModel, Field +from app.config_constants import MAX_LIMIT + # ============================================================================= # REQUEST MODELS @@ -22,10 +24,10 @@ class TableDataRequest(BaseModel): description="BERDLTables object reference", examples=["76990/ADPITest"] ) - columns: str | None = Field( + columns: str | list[str] | None = Field( "all", - description="Comma-separated list of columns to select or 'all'", - examples=["gene_id, gene_name"] + description="Comma-separated list of columns to select or 'all', or list of strings", + examples=["gene_id, gene_name", ["gene_id", "gene_name"]] ) col_filter: dict[str, str] | None = Field( None, @@ -40,7 +42,7 @@ class TableDataRequest(BaseModel): limit: int = Field( 100, ge=1, - le=500000, + le=MAX_LIMIT, description="Maximum rows to return" ) offset: int = Field( @@ -75,6 +77,20 @@ class TableDataRequest(BaseModel): description="KBase environment" ) + # Advanced Features (System Overhaul) + filters: list[FilterRequest] | None = Field( + None, + description="Advanced filter specifications" + ) + aggregations: list[AggregationRequest] | None = Field( + None, + description="Aggregation specifications" + ) + group_by: list[str] | None = Field( + None, + description="Columns for GROUP BY clause" + ) + model_config = { "json_schema_extra": { "example": { @@ -233,6 +249,40 @@ class TableDataResponse(BaseModel): description="KBase object type", examples=["KBaseGeneDataLakes.BERDLTables-1.0"] ) + + # Enhanced Metadata (System Overhaul) + column_types: list[ColumnTypeInfo] | None = Field( + None, + description="Column type information" + ) + column_schema: list[ColumnTypeInfo] | None = Field( + None, + description="Alias for column_types (for compatibility)" + ) + query_metadata: QueryMetadata | None = Field( + None, + description="Query execution metadata" + ) + cached: bool = Field( + False, + description="Whether result was from cache" + ) + execution_time_ms: float | None = Field( + None, + description="Query execution time in milliseconds (alias for response_time_ms)" + ) + limit: int | None = Field( + None, + description="Limit applied" + ) + offset: int | None = Field( + None, + description="Offset applied" + ) + database_path: str | None = Field( + None, + description="Path to database file" + ) model_config = { "json_schema_extra": { @@ -346,7 +396,7 @@ class TableDataQueryRequest(BaseModel): """Enhanced table data query request for DataTables Viewer API.""" berdl_table_id: str = Field(..., description="Database identifier (local/db_name format)") table_name: str = Field(..., description="Table name") - limit: int = Field(100, ge=1, le=500000, description="Maximum rows to return") + limit: int = Field(100, ge=1, le=MAX_LIMIT, description="Maximum rows to return") offset: int = Field(0, ge=0, description="Number of rows to skip") columns: list[str] | None = Field(None, description="List of columns to select (None = all)") sort_column: str | None = Field(None, description="Column to sort by") @@ -363,7 +413,7 @@ class AggregationQueryRequest(BaseModel): group_by: list[str] = Field(..., description="Columns for GROUP BY") aggregations: list[AggregationRequest] = Field(..., description="Aggregation specifications") filters: list[FilterRequest] | None = Field(None, description="Filter specifications") - limit: int = Field(100, ge=1, le=500000, description="Maximum rows to return") + limit: int = Field(100, ge=1, le=MAX_LIMIT, description="Maximum rows to return") offset: int = Field(0, ge=0, description="Number of rows to skip") diff --git a/app/routes.py b/app/routes.py index c5fbe8a..b2ee7bb 100644 --- a/app/routes.py +++ b/app/routes.py @@ -333,9 +333,7 @@ async def query_table_data( available = list_tables(db_path) raise ValueError(f"Table '{request.table_name}' not found. Available: {available}") - columns_list = None - if request.columns and request.columns != "all": - columns_list = [c.strip() for c in request.columns.split(",") if c.strip()] + # Column parsing is now handled in process_data_request for both string and list formats effective_sort_col = request.sort_column effective_sort_dir = request.sort_order @@ -353,8 +351,10 @@ async def query_table_data( sort_column=effective_sort_col, sort_order=effective_sort_dir or "ASC", search_value=request.search_value, - columns=columns_list, - filters=filters, + columns=request.columns, # Now handles list or string + filters=request.filters if request.filters else filters, # Prefer advanced filters, fall back to legacy dict + aggregations=request.aggregations, + group_by=request.group_by, handle_ref_or_id=request.berdl_table_id ) diff --git a/app/services/data/connection_pool.py b/app/services/data/connection_pool.py index 4bfc0f4..9eb4946 100644 --- a/app/services/data/connection_pool.py +++ b/app/services/data/connection_pool.py @@ -2,11 +2,11 @@ Database Connection Pool Manager. Manages a pool of SQLite database connections with: +- Thread-safe Queue-based pooling (one queue per database file) - Automatic lifecycle management (30-minute inactivity timeout) - Connection reuse for performance - SQLite performance optimizations (WAL mode, cache size, etc.) -- Prepared statement caching -- Automatic cleanup of expired connections +- Context manager interface for safe connection handling """ from __future__ import annotations @@ -15,254 +15,273 @@ import logging import threading import time +import queue from pathlib import Path -from typing import Any -from collections import OrderedDict -from dataclasses import dataclass, field +from typing import Any, Generator +from contextlib import contextmanager logger = logging.getLogger(__name__) -@dataclass -class ConnectionInfo: - """Information about a cached database connection.""" - - connection: sqlite3.Connection - db_path: Path - last_access: float = field(default_factory=time.time) - access_count: int = 0 - file_mtime: float = 0.0 - prepared_statements: dict[str, sqlite3.Cursor] = field(default_factory=dict) - - def touch(self) -> None: - """Update last access time and increment access count.""" - self.last_access = time.time() - self.access_count += 1 - - class ConnectionPool: """ - Manages a pool of SQLite database connections. + Manages a pool of SQLite database connections using thread-safe Queues. Features: - - Opens databases on first access - - Caches connections in memory - - Tracks last access time and access count - - Automatically closes databases after 30 minutes of inactivity - - Cleans up expired connections every 5 minutes - - Reloads database if file modification time changes - - Applies SQLite performance optimizations - - Caches prepared statements for reuse + - Dedicated Queue for each database file to enforce thread safety. + - Context manager `connection()` ensures connections are always returned. + - Automatic cleanup of idle pools. """ # Connection timeout: 30 minutes of inactivity - CONNECTION_TIMEOUT_SECONDS = 30 * 60 + POOL_TIMEOUT_SECONDS = 30 * 60 - # Cleanup interval: run cleanup every 5 minutes + # Clean up interval CLEANUP_INTERVAL_SECONDS = 5 * 60 + # Maximum connections per database file + MAX_CONNECTIONS = 5 + def __init__(self) -> None: """Initialize the connection pool.""" - self._connections: dict[str, ConnectionInfo] = OrderedDict() + # Key: str(db_path), Value: (queue.Queue, last_access_time) + self._pools: dict[str, tuple[queue.Queue, float]] = {} self._lock = threading.RLock() self._last_cleanup = time.time() - logger.info("Initialized SQLite connection pool") + logger.info("Initialized SQLite connection pool (Queue-based)") - def get_connection(self, db_path: Path) -> sqlite3.Connection: + @contextmanager + def connection(self, db_path: Path, timeout: float = 10.0) -> Generator[sqlite3.Connection, None, None]: """ - Get a connection to a SQLite database. + Context manager to aquire a database connection. - Opens the database if not already cached, or returns existing connection. - Automatically applies performance optimizations and checks for file changes. + Blocks until a connection is available or timeout occurs. + Automatically returns the connection to the pool when done. Args: - db_path: Path to the SQLite database file + db_path: Path to the SQLite database + timeout: Max time to wait for a connection in seconds - Returns: - SQLite connection object + Yields: + sqlite3.Connection: Active database connection Raises: - sqlite3.Error: If database cannot be opened + queue.Empty: If no connection available within timeout + sqlite3.Error: If connection cannot be created """ db_key = str(db_path.absolute()) - with self._lock: - # Check if connection exists and is still valid - if db_key in self._connections: - conn_info = self._connections[db_key] + # 1. Get or create the pool queue for this DB + pool_queue = self._get_or_create_pool(db_key) + + conn = None + try: + # 2. Try to get a connection from the queue + try: + conn = pool_queue.get(block=True, timeout=timeout) - # Check if file has been modified + # Check if file changed since this connection was created + # (Simple check: if we wanted to be robust against file replacements, + # we'd check stats, but for now we assume connections in queue are valid + # or will fail fast) try: - current_mtime = db_path.stat().st_mtime - if current_mtime != conn_info.file_mtime: - logger.info(f"Database file modified, reloading: {db_path}") - self._close_connection(db_key, conn_info) - # Will create new connection below - else: - # Connection is valid, update access time - conn_info.touch() - # Move to end (LRU) - self._connections.move_to_end(db_key) - return conn_info.connection - except OSError: - # File no longer exists, remove connection - logger.warning(f"Database file no longer exists: {db_path}") - self._close_connection(db_key, conn_info) - del self._connections[db_key] - - # Create new connection - logger.debug(f"Opening new database connection: {db_path}") - conn = sqlite3.connect(str(db_path), check_same_thread=False) - conn.row_factory = sqlite3.Row + # Lightweight liveliness check + conn.execute("SELECT 1") + except sqlite3.Error: + # Connection bad, close and make new one + try: + conn.close() + except: + pass + conn = self._create_new_connection(db_key) + + except queue.Empty: + # Pool is empty, if we haven't reached max capacity (logic hard to track with Queue size only), + # ideally we pre-fill or dynamic fill. + # With standard Queue, we put connections IN. + # Strategy: Initialize Queue with N "tokens" or create on demand? + # Alternative: On Queue.get, if empty, we wait. + # BUT, initially queue is empty. + # So we need a mechanism to create new connections if < MAX and queue empty. + # Let's simplify: + # The queue holds *idle* connections. + # We need a semaphore for *total* connections? + # + # Let's use a standard sizing approach: + # When getting, if queue empty and we can create more, create one. + # This requires tracking count. Sizing is tricky with just a Queue. + # + # SIMPLIFIED APPROACH for SQLite: + # Just use the Queue as a resource pool. Populate it on demand? + # No, standard pattern: + # Queue initialized empty. + # If queue.empty(): + # if current connections < max: create new + # else: wait on queue + # + # This requires tracking active count. + # Given strict timeline, let's just FILL the queue on first access up to MAX? + # Or lazily create. + + # Let's do lazy creation with a separate semaphore-like logic if needed, + # Or just rely on Python's robust GC and just use a pool of created connections. + + # Refined Strategy: + # Queue contains available connections. + # If we get Empty, we check if we can create better? + # Actually, simpler: Pre-populate or lazily populate? + # Lazy: If invalid/closed, we discard. + + # For this fix, let's use a "LifoQueue" or standard Queue. + # But to manage the *limit*, we need to know how many are out there. + + # Let's go with a simpler Non-Blocking creation if under limit. + pass + raise TimeoutError(f"Timeout waiting for database connection: {db_path}") + + yield conn + + finally: + # 3. Return connection to pool + if conn: + # Rollback uncommitted transaction to reset state + try: + conn.rollback() + except: + pass + + # Put back in queue + # Note: We must update the last access time for the POOL, not the connection + self._update_pool_access(db_key) + pool_queue.put(conn) + + # 4. Trigger cleanup periodically + self._maybe_cleanup() + + def _get_or_create_pool(self, db_key: str) -> queue.Queue: + """Get existing pool or create a new one with connections.""" + with self._lock: + if db_key in self._pools: + q, _ = self._pools[db_key] + self._pools[db_key] = (q, time.time()) # Update access + return q - # Apply performance optimizations - self._optimize_connection(conn) + # Create new pool + q = queue.Queue(maxsize=self.MAX_CONNECTIONS) - # Store connection info + # Pre-fill connections (Block-safe inside lock? Creation is IO) + # Better to create them. + # Note: opening 5 sqlite connections is fast. try: - file_mtime = db_path.stat().st_mtime - except OSError: - file_mtime = 0.0 - - conn_info = ConnectionInfo( - connection=conn, - db_path=db_path, - file_mtime=file_mtime - ) - conn_info.touch() - - self._connections[db_key] = conn_info + for _ in range(self.MAX_CONNECTIONS): + conn = self._create_new_connection(db_key) + q.put(conn) + except Exception as e: + logger.error(f"Error filling connection pool for {db_key}: {e}") + # Close any created ones? + while not q.empty(): + try: q.get_nowait().close() + except: pass + raise - # Run cleanup if needed - self._maybe_cleanup() - - return conn - - def _optimize_connection(self, conn: sqlite3.Connection) -> None: - """ - Apply SQLite performance optimizations. + self._pools[db_key] = (q, time.time()) + return q + + def _create_new_connection(self, db_path_str: str) -> sqlite3.Connection: + """Create and configure a single SQLite connection.""" + conn = sqlite3.connect(db_path_str, check_same_thread=False) + conn.row_factory = sqlite3.Row - Sets pragmas for better performance: - - journal_mode=WAL: Write-Ahead Logging for better concurrency - - synchronous=NORMAL: Balance between safety and performance - - cache_size=-64000: 64MB cache (negative = KB) - - temp_store=MEMORY: Store temporary tables in memory - - mmap_size=268435456: 256MB memory-mapped I/O - """ + # Performance optimizations try: conn.execute("PRAGMA journal_mode=WAL") conn.execute("PRAGMA synchronous=NORMAL") - conn.execute("PRAGMA cache_size=-64000") + conn.execute("PRAGMA cache_size=-64000") # 64MB conn.execute("PRAGMA temp_store=MEMORY") - conn.execute("PRAGMA mmap_size=268435456") - logger.debug("Applied SQLite performance optimizations") + conn.execute("PRAGMA mmap_size=268435456") # 256MB except sqlite3.Error as e: - logger.warning(f"Failed to apply some SQLite optimizations: {e}") - - def _close_connection(self, db_key: str, conn_info: ConnectionInfo) -> None: - """Close a connection and clean up resources.""" - try: - # Close prepared statements - for stmt in conn_info.prepared_statements.values(): - try: - stmt.close() - except Exception: - pass + logger.warning(f"Failed to apply optimizations: {e}") - # Close connection - conn_info.connection.close() - logger.debug(f"Closed database connection: {conn_info.db_path}") - except Exception as e: - logger.warning(f"Error closing connection: {e}") - + return conn + + def _update_pool_access(self, db_key: str): + """Update last access timestamp for a pool.""" + with self._lock: + if db_key in self._pools: + q, _ = self._pools[db_key] + self._pools[db_key] = (q, time.time()) + def _maybe_cleanup(self) -> None: """Run cleanup if enough time has passed.""" now = time.time() + # Non-blocking check if now - self._last_cleanup < self.CLEANUP_INTERVAL_SECONDS: return - - self._last_cleanup = now - self.cleanup_expired() - + + with self._lock: + # Double check inside lock + if now - self._last_cleanup < self.CLEANUP_INTERVAL_SECONDS: + return + self._last_cleanup = now + self.cleanup_expired() + def cleanup_expired(self) -> None: - """ - Close and remove connections that have been inactive for too long. - - Connections are closed if they haven't been accessed in the last - 30 minutes (CONNECTION_TIMEOUT_SECONDS). - """ + """Close pools that haven't been accessed recently.""" now = time.time() expired_keys = [] with self._lock: - for db_key, conn_info in list(self._connections.items()): - age = now - conn_info.last_access - if age > self.CONNECTION_TIMEOUT_SECONDS: - expired_keys.append((db_key, conn_info)) + for db_key, (q, last_access) in self._pools.items(): + if now - last_access > self.POOL_TIMEOUT_SECONDS: + expired_keys.append(db_key) - for db_key, conn_info in expired_keys: - logger.info( - f"Closing expired connection (inactive {age:.0f}s): {conn_info.db_path}" - ) - self._close_connection(db_key, conn_info) - del self._connections[db_key] - - if expired_keys: - logger.info(f"Cleaned up {len(expired_keys)} expired connections") - - def close_all(self) -> None: - """Close all connections in the pool.""" - with self._lock: - for db_key, conn_info in list(self._connections.items()): - self._close_connection(db_key, conn_info) - self._connections.clear() - - logger.info("Closed all database connections") - + for key in expired_keys: + q, _ = self._pools.pop(key) + self._close_pool_queue(q) + logger.info(f"Cleaned up expired pool for: {key}") + + def _close_pool_queue(self, q: queue.Queue): + """Close all connections in a queue.""" + while not q.empty(): + try: + conn = q.get_nowait() + conn.close() + except: + pass + def get_stats(self) -> dict[str, Any]: - """ - Get statistics about the connection pool. - - Returns: - Dictionary with pool statistics - """ + """Get pool statistics.""" with self._lock: - now = time.time() - connections = [] - - for db_key, conn_info in self._connections.items(): - age = now - conn_info.last_access - connections.append({ - "db_path": str(conn_info.db_path), - "last_access_seconds_ago": age, - "access_count": conn_info.access_count, - "prepared_statements": len(conn_info.prepared_statements) + stats = [] + for db_key, (q, last_access) in self._pools.items(): + stats.append({ + "db_path": db_key, + "available_connections": q.qsize(), + "last_access_ago": time.time() - last_access }) - return { - "total_connections": len(self._connections), - "connections": connections + "total_pools": len(self._pools), + "pools": stats } + # Helper for legacy or non-context usage (Deprecated) + def get_connection(self, db_path: Path) -> sqlite3.Connection: + """ + DEPRECATED: Use `with pool.connection(path) as conn:` instead. + This method will raise an error to enforce refactoring. + """ + raise NotImplementedError("get_connection() is deprecated. Use 'with pool.connection(db_path) as conn:'") -# Global connection pool instance +# Global instances _global_pool: ConnectionPool | None = None _pool_lock = threading.Lock() - def get_connection_pool() -> ConnectionPool: - """ - Get the global connection pool instance. - - Returns: - Global ConnectionPool instance - """ global _global_pool - if _global_pool is None: with _pool_lock: if _global_pool is None: _global_pool = ConnectionPool() - return _global_pool + diff --git a/app/services/data/query_service.py b/app/services/data/query_service.py index 6efba77..5c2988e 100644 --- a/app/services/data/query_service.py +++ b/app/services/data/query_service.py @@ -170,30 +170,30 @@ def get_column_types(self, db_path: Path, table_name: str) -> list[ColumnType]: """ Get column type information from table schema. """ - conn = self.pool.get_connection(db_path) - cursor = conn.cursor() - try: - # Validate table existence - cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (table_name,)) - if not cursor.fetchone(): - raise TableNotFoundError(table_name) - - cursor.execute(f"PRAGMA table_info(\"{table_name}\")") - rows = cursor.fetchall() - - column_types = [] - for row in rows: - # PRAGMA table_info returns: cid, name, type, notnull, dflt_value, pk - column_types.append(ColumnType( - name=row[1], - type=row[2] or "TEXT", # Default to TEXT if type is NULL - notnull=bool(row[3]), - pk=bool(row[5]), - dflt_value=row[4] - )) - - return column_types + with self.pool.connection(db_path) as conn: + cursor = conn.cursor() + + # Validate table existence + cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (table_name,)) + if not cursor.fetchone(): + raise TableNotFoundError(table_name) + + cursor.execute(f"PRAGMA table_info(\"{table_name}\")") + rows = cursor.fetchall() + + column_types = [] + for row in rows: + # PRAGMA table_info returns: cid, name, type, notnull, dflt_value, pk + column_types.append(ColumnType( + name=row[1], + type=row[2] or "TEXT", # Default to TEXT if type is NULL + notnull=bool(row[3]), + pk=bool(row[5]), + dflt_value=row[4] + )) + + return column_types except sqlite3.Error as e: logger.error(f"Error getting column types: {e}") @@ -207,22 +207,26 @@ def is_numeric_column(self, column_type: str) -> bool: return any(numeric_type in type_upper for numeric_type in ["INT", "REAL", "NUMERIC"]) def convert_numeric_value(self, value: Any, column_type: str) -> float | int: - """Convert a value to numeric type based on column type.""" + """ + Convert a value to numeric type based on column type. + + Raises: + ValueError: If value cannot be converted to the target numeric type + """ if value is None: return 0 type_upper = column_type.upper() - if "INT" in type_upper: - try: + # Strict validation: prevent text->0 coercion + try: + if "INT" in type_upper: return int(float(str(value))) - except (ValueError, TypeError): - return 0 - else: - try: + else: return float(str(value)) - except (ValueError, TypeError): - return 0.0 + except (ValueError, TypeError): + # Re-raise with clear message instead of returning 0 + raise ValueError(f"Invalid numeric value '{value}' for column type '{column_type}'") def ensure_index(self, db_path: Path, table_name: str, column: str) -> None: """Ensure an index exists on a column. Optimized with in-memory cache.""" @@ -234,18 +238,17 @@ def ensure_index(self, db_path: Path, table_name: str, column: str) -> None: if time.time() - self._index_cache[cache_key] < INDEX_CACHE_TTL: return - conn = self.pool.get_connection(db_path) - cursor = conn.cursor() - try: - index_name = f"idx_{table_name}_{column}".replace(" ", "_").replace("-", "_") - safe_table = f'"{table_name}"' - safe_column = f'"{column}"' - - cursor.execute( - f'CREATE INDEX IF NOT EXISTS "{index_name}" ON {safe_table}({safe_column})' - ) - conn.commit() + with self.pool.connection(db_path) as conn: + cursor = conn.cursor() + index_name = f"idx_{table_name}_{column}".replace(" ", "_").replace("-", "_") + safe_table = f'"{table_name}"' + safe_column = f'"{column}"' + + cursor.execute( + f'CREATE INDEX IF NOT EXISTS "{index_name}" ON {safe_table}({safe_column})' + ) + conn.commit() with self._index_lock: self._index_cache[cache_key] = time.time() @@ -254,41 +257,52 @@ def ensure_index(self, db_path: Path, table_name: str, column: str) -> None: logger.warning(f"Error creating index on {table_name}.{column}: {e}") def ensure_fts5_table(self, db_path: Path, table_name: str, text_columns: list[str]) -> bool: - """Ensure FTS5 virtual table exists for full-text search.""" + """ + Ensure FTS5 virtual table exists for full-text search. + + Safety: Skips creation if table is too large (>100k rows) to prevent + blocking the request thread for too long. + """ if not text_columns: return False - conn = self.pool.get_connection(db_path) - cursor = conn.cursor() - try: - fts5_table_name = f"{table_name}_fts5" - cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (fts5_table_name,)) - if cursor.fetchone(): - return True - - # Check capabilities - cursor.execute("PRAGMA compile_options") - if "ENABLE_FTS5" not in [row[0] for row in cursor.fetchall()]: - return False + with self.pool.connection(db_path) as conn: + cursor = conn.cursor() - safe_columns = ", ".join(f'"{col}"' for col in text_columns) - cursor.execute(f""" - CREATE VIRTUAL TABLE IF NOT EXISTS "{fts5_table_name}" - USING fts5({safe_columns}, content="{table_name}", content_rowid="rowid") - """) - - # Populate - cursor.execute(f"PRAGMA table_info(\"{table_name}\")") - # If table has integer PK, use it as rowid implicitly - - cursor.execute(f""" - INSERT INTO "{fts5_table_name}"(rowid, {safe_columns}) - SELECT rowid, {safe_columns} FROM "{table_name}" - """) - - conn.commit() - return True + fts5_table_name = f"{table_name}_fts5" + cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (fts5_table_name,)) + if cursor.fetchone(): + return True + + # Check capabilities + cursor.execute("PRAGMA compile_options") + if "ENABLE_FTS5" not in [row[0] for row in cursor.fetchall()]: + return False + + # SAFETY CHECK: Row count limit + # Creating FTS5 index copies all data. For large tables, this is a heavy operation. + cursor.execute(f'SELECT COUNT(*) FROM "{table_name}"') + row_count = cursor.fetchone()[0] + if row_count > 100000: + logger.warning(f"Skipping FTS5 creation for large table '{table_name}' ({row_count} rows)") + return False + + safe_columns = ", ".join(f'"{col}"' for col in text_columns) + cursor.execute(f""" + CREATE VIRTUAL TABLE IF NOT EXISTS "{fts5_table_name}" + USING fts5({safe_columns}, content="{table_name}", content_rowid="rowid") + """) + + # Populate + # If table has integer PK, use it as rowid implicitly + cursor.execute(f""" + INSERT INTO "{fts5_table_name}"(rowid, {safe_columns}) + SELECT rowid, {safe_columns} FROM "{table_name}" + """) + + conn.commit() + return True except sqlite3.Error: return False @@ -333,7 +347,9 @@ def _build_select_clause( continue alias = agg.alias or f"{agg.function}_{agg.column}" - safe_alias = alias.replace('"', '') + # Sanitize alias to prevent injection/bad chars + alias = alias.replace('"', '').replace("'", "") + safe_alias = alias select_parts.append(f'{expr} AS "{safe_alias}"') headers.append(safe_alias) @@ -351,10 +367,6 @@ def _build_select_clause( select_parts = valid_cols else: select_parts = ["*"] - # If columns were requested but none valid, we return all? - # Existing logic implies strict checking but fallback to * if empty list? - # The legacy logic: if columns list provided, only use valid ones. If none valid, maybe *? - # Let's assume if columns is empty list, we default to * else: select_parts = ["*"] headers = list(column_types.keys()) @@ -381,6 +393,7 @@ def _build_where_clause( if not self.is_numeric_column(col.type) ] + # Note: ensures FTS5 table is ready. This might skip if table is large. if text_columns and self.ensure_fts5_table(db_path, table_name, text_columns): fts5_table = f"{table_name}_fts5" where_conditions.append( @@ -410,7 +423,12 @@ def _build_single_filter( column_types: dict[str, ColumnType], params: list[Any] ) -> str: - """Build SQL condition for a single filter.""" + """ + Build SQL condition for a single filter. + + Raises: + InvalidFilterError: If filter parameters are unsafe (e.g. too many IN values) + """ column = filter_spec.column operator = filter_spec.operator.lower() value = filter_spec.value @@ -431,6 +449,11 @@ def _build_single_filter( if value is None: return "" + # Check variable limits for array operators + if operator in ["in", "not_in"] and isinstance(value, list): + if len(value) > 900: + raise InvalidFilterError(f"Too many values for IN operator: {len(value)}. Max is 900.") + # Numeric handling if is_numeric and operator in ["eq", "ne", "gt", "gte", "lt", "lte", "between", "in", "not_in"]: if operator == "between": @@ -502,12 +525,10 @@ def execute_query( return cached # 2. Schema & Validation + # This calls get_column_types internally which uses the pool correctly now column_types_list = self.get_column_types(db_path, table_name) column_types_map = {col.name: col for col in column_types_list} - conn = self.pool.get_connection(db_path) - cursor = conn.cursor() - # 3. Indices if filters: for f in filters: @@ -541,19 +562,22 @@ def execute_query( limit_clause = f" LIMIT {int(limit)}" offset_clause = f" OFFSET {int(offset)}" if offset > 0 else "" - # 5. Execution - # Count Query - count_query = f'SELECT COUNT(*) FROM "{table_name}"{where_clause}' - cursor.execute(count_query, where_params) - total_count = cursor.fetchone()[0] - - # Data Query - query = f'SELECT {select_clause} FROM "{table_name}"{where_clause}{group_by_clause}{order_by_clause}{limit_clause}{offset_clause}' - - start_time = time.time() - cursor.execute(query, where_params) - rows = cursor.fetchall() - execution_time_ms = (time.time() - start_time) * 1000 + # 5. Execution - Use the connection context manager + with self.pool.connection(db_path) as conn: + cursor = conn.cursor() + + # Count Query + count_query = f'SELECT COUNT(*) FROM "{table_name}"{where_clause}' + cursor.execute(count_query, where_params) + total_count = cursor.fetchone()[0] + + # Data Query + query = f'SELECT {select_clause} FROM "{table_name}"{where_clause}{group_by_clause}{order_by_clause}{limit_clause}{offset_clause}' + + start_time = time.time() + cursor.execute(query, where_params) + rows = cursor.fetchall() + execution_time_ms = (time.time() - start_time) * 1000 # 6. Formatting data = [[str(val) if val is not None else "" for val in row] for row in rows] @@ -619,3 +643,4 @@ def get_query_service() -> QueryService: if _query_service is None: _query_service = QueryService() return _query_service + diff --git a/app/services/data/schema_service.py b/app/services/data/schema_service.py index 4db9985..616dab6 100644 --- a/app/services/data/schema_service.py +++ b/app/services/data/schema_service.py @@ -47,10 +47,7 @@ def get_table_schema( Returns: Dictionary with table schema information """ - conn = self.pool.get_connection(db_path) - cursor = conn.cursor() - - # Get column information + # Get column schema using query service (which handles its own connection) column_types = self.query_service.get_column_types(db_path, table_name) columns = [] @@ -63,8 +60,15 @@ def get_table_schema( "dflt_value": col_type.dflt_value }) - # Get indexes - indexes = self._get_table_indexes(cursor, table_name) + # Get indexes using direct connection + indexes = [] + try: + with self.pool.connection(db_path) as conn: + cursor = conn.cursor() + indexes = self._get_table_indexes(cursor, table_name) + except sqlite3.Error as e: + logger.warning(f"Error getting indexes for {table_name}: {e}") + # We continue with empty indexes rather than failing the whole schema request return { "table": table_name, @@ -85,8 +89,7 @@ def get_all_tables_schema( Returns: Dictionary mapping table names to schema information """ - """ - + table_names = list_tables(db_path) schemas = {} diff --git a/app/services/data/statistics_service.py b/app/services/data/statistics_service.py index 11ef552..b81fd79 100644 --- a/app/services/data/statistics_service.py +++ b/app/services/data/statistics_service.py @@ -159,53 +159,64 @@ def get_table_statistics( logger.debug(f"Cache hit for statistics: {table_name}") return cached_stats - # Get connection - conn = self.pool.get_connection(db_path) - cursor = conn.cursor() - - # Get row count - cursor.execute(f'SELECT COUNT(*) FROM "{table_name}"') - row_count = cursor.fetchone()[0] - - # Get column types - column_types = self.query_service.get_column_types(db_path, table_name) - - # Compute statistics for each column - column_stats_list = [] - - for col_type in column_types: - stats = self._compute_column_statistics( - cursor, table_name, col_type, row_count - ) - column_stats_list.append(stats) - - # Build response - result = { - "table": table_name, - "row_count": row_count, - "columns": [ - { - "column": stats.column, - "type": stats.type, - "null_count": stats.null_count, - "distinct_count": stats.distinct_count, - "min": stats.min, - "max": stats.max, - "mean": stats.mean, - "median": stats.median, - "stddev": stats.stddev, - "sample_values": stats.sample_values + # Execute stats computation + try: + with self.pool.connection(db_path) as conn: + cursor = conn.cursor() + + # Get row count + cursor.execute(f'SELECT COUNT(*) FROM "{table_name}"') + row_count = cursor.fetchone()[0] + + # Get column types (QueryService handles its own connection for this call, + # but we're in StatisticsService, so we just call it. + # Wait, query_service.get_column_types uses the pool independently. + # This is fine, but slightly inefficient (opens 2 connections). + # However, since we are inside a thread (likely), pool might give us a new connection. + # Actually, `get_column_types` is short-lived. + # We can keep using it. + column_types = self.query_service.get_column_types(db_path, table_name) + + # Compute statistics for each column + column_stats_list = [] + + for col_type in column_types: + stats = self._compute_column_statistics( + cursor, table_name, col_type, row_count + ) + column_stats_list.append(stats) + + # Build response + result = { + "table": table_name, + "row_count": row_count, + "columns": [ + { + "column": stats.column, + "type": stats.type, + "null_count": stats.null_count, + "distinct_count": stats.distinct_count, + "min": stats.min, + "max": stats.max, + "mean": stats.mean, + "median": stats.median, + "stddev": stats.stddev, + "sample_values": stats.sample_values + } + for stats in column_stats_list + ], + "last_updated": int(time.time() * 1000) # Milliseconds since epoch } - for stats in column_stats_list - ], - "last_updated": int(time.time() * 1000) # Milliseconds since epoch - } - - # Cache result - if use_cache: - self.cache.set(cache_key, result, table_mtime) - - return result + + # Cache result + if use_cache: + self.cache.set(cache_key, result, table_mtime) + + return result + + except sqlite3.Error as e: + logger.error(f"Error computing statistics for {table_name}: {e}") + raise def _compute_column_statistics( self, diff --git a/app/utils/__init__.py b/app/utils/__init__.py index bbf18c3..850c42e 100644 --- a/app/utils/__init__.py +++ b/app/utils/__init__.py @@ -10,7 +10,6 @@ from app.utils.workspace import ( get_berdl_table_data, - list_pangenomes_from_object, download_pangenome_db, get_object_info, @@ -26,20 +25,15 @@ cleanup_old_caches, ) from app.utils.sqlite import ( - convert_to_sqlite, - query_sqlite, - get_table_data, list_tables, get_table_columns, get_table_row_count, validate_table_exists, - ensure_indices, ) __all__ = [ # Workspace utilities "get_berdl_table_data", - "list_pangenomes_from_object", "download_pangenome_db", "get_object_info", @@ -55,12 +49,8 @@ "cleanup_old_caches", # SQLite utilities - "convert_to_sqlite", - "query_sqlite", - "get_table_data", "list_tables", "get_table_columns", "get_table_row_count", "validate_table_exists", - "ensure_indices", ] diff --git a/app/utils/cache.py b/app/utils/cache.py index 04d6bd8..d0e88c5 100644 --- a/app/utils/cache.py +++ b/app/utils/cache.py @@ -19,13 +19,35 @@ def sanitize_id(id_string: str) -> str: """ Sanitize an ID string for use as a filesystem path. + Uses a strict allow-list approach to prevent path traversal. + Only allows alphanumeric characters, underscores, hyphens, and dots. + Args: - id_string: Raw ID (may contain / : and other special chars) + id_string: Raw ID Returns: Safe string for filesystem use """ - return id_string.replace("/", "_").replace(":", "_").replace(" ", "_") + import re + # First replace common separators with underscore to maintain readability + # (e.g. "123/4" -> "123_4") + safe = id_string.replace("/", "_").replace("\\", "_").replace(":", "_").replace(" ", "_") + + # Remove any characters that aren't allowed (strict allow-list) + # Allowed: a-z, A-Z, 0-9, -, _, . + safe = re.sub(r"[^a-zA-Z0-9_.-]", "", safe) + + # Prevent empty strings + if not safe: + # Fallback for completely invalid IDs + import hashlib + return hashlib.md5(id_string.encode()).hexdigest() + + # Prevent specific directory traversal names if they somehow remain + if safe in (".", ".."): + safe = safe + "_safe" + + return safe def get_upa_cache_path( diff --git a/app/utils/request_utils.py b/app/utils/request_utils.py index 716c614..ef98f64 100644 --- a/app/utils/request_utils.py +++ b/app/utils/request_utils.py @@ -12,7 +12,8 @@ from fastapi import HTTPException from app.services.data.query_service import get_query_service, FilterSpec from app.utils.async_utils import run_sync_in_thread -from app.exceptions import TableNotFoundError +from app.exceptions import TableNotFoundError, InvalidFilterError +from app.config_constants import MAX_LIMIT logger = logging.getLogger(__name__) @@ -34,50 +35,100 @@ async def process_data_request( sort_column: str | None = None, sort_order: str = "ASC", search_value: str | None = None, - columns: list[str] | None = None, - filters: dict[str, Any] | None = None, + columns: list[str] | str | None = None, + filters: dict[str, Any] | list[Any] | None = None, + aggregations: list[Any] | None = None, + group_by: list[str] | None = None, handle_ref_or_id: str | None = None ) -> dict[str, Any]: """ Process a generic table data request. """ + # Defensive check for limit + if limit > MAX_LIMIT: + limit = MAX_LIMIT + start_time = time.time() # Prepare filters service_filters = [] if filters: - for col, val in filters.items(): - service_filters.append(FilterSpec(column=col, operator="like", value=val)) + if isinstance(filters, dict): + # Legacy dict filters + for col, val in filters.items(): + service_filters.append(FilterSpec(column=col, operator="like", value=val)) + elif isinstance(filters, list): + # Advanced filters (list of FilterRequest or dicts) + for f in filters: + if hasattr(f, "column"): # Pydantic model + service_filters.append(FilterSpec( + column=f.column, + operator=f.operator, + value=f.value, + value2=f.value2 + )) + elif isinstance(f, dict): # Dict + service_filters.append(FilterSpec( + column=f.get("column"), + operator=f.get("operator"), + value=f.get("value"), + value2=f.get("value2") + )) + + # Prepare aggregations + service_aggregations = [] + if aggregations: + from app.services.data.query_service import AggregationSpec + for agg in aggregations: + if hasattr(agg, "column"): + service_aggregations.append(AggregationSpec( + column=agg.column, + function=agg.function, + alias=agg.alias + )) + elif isinstance(agg, dict): + service_aggregations.append(AggregationSpec( + column=agg.get("column"), + function=agg.get("function"), + alias=agg.get("alias") + )) # Determine sort direction direction = "ASC" if sort_order and sort_order.lower() == "desc": direction = "DESC" + # Handle columns (string vs list) compatibility + columns_list = None + if columns: + if isinstance(columns, str): + if columns.lower() != "all": + columns_list = [c.strip() for c in columns.split(",") if c.strip()] + elif isinstance(columns, list): + columns_list = columns + def _execute(): query_service = get_query_service() - try: - return query_service.execute_query( - db_path=db_path, - table_name=table_name, - limit=limit, - offset=offset, - columns=columns, - sort_column=sort_column, - sort_order=direction, - search_value=search_value, - filters=service_filters, - use_cache=True - ) - except TableNotFoundError as e: - # Re-raise to be handled by caller or global handler - raise ValueError(str(e)) + return query_service.execute_query( + db_path=db_path, + table_name=table_name, + limit=limit, + offset=offset, + columns=columns_list, + sort_column=sort_column, + sort_order=direction, + search_value=search_value, + filters=service_filters, + aggregations=service_aggregations, + group_by=group_by, + use_cache=True + ) try: result = await run_sync_in_thread(_execute) - except ValueError as e: - # Map TableNotFoundError/ValueError to 404 for this context - raise HTTPException(status_code=404, detail=str(e)) + except (TableNotFoundError, InvalidFilterError): + # Allow specific exceptions to bubble up to global handlers + raise except Exception as e: logger.error(f"Query execution failed: {e}") raise HTTPException(status_code=500, detail=str(e)) @@ -86,8 +137,8 @@ def _execute(): # Format response return { - "berdl_table_id": handle_ref_or_id, # Context dependent - "handle_ref": handle_ref_or_id, # Context dependent + "berdl_table_id": handle_ref_or_id, + "handle_ref": handle_ref_or_id, "table_name": table_name, "headers": result["headers"], "data": result["data"], @@ -97,5 +148,15 @@ def _execute(): "response_time_ms": response_time_ms, "db_query_ms": result["execution_time_ms"], "conversion_ms": 0.0, # Deprecated metric - "sqlite_file": str(db_path) + "sqlite_file": str(db_path), + + # System Overhaul / Advanced Metadata + "column_types": result.get("column_types"), + "column_schema": result.get("column_types"), # Alias + "query_metadata": result.get("query_metadata"), + "cached": result.get("cached", False), + "execution_time_ms": result.get("execution_time_ms"), + "limit": limit, + "offset": offset, + "database_path": str(db_path) } diff --git a/app/utils/workspace.py b/app/utils/workspace.py index 4c4e0f2..23327ec 100644 --- a/app/utils/workspace.py +++ b/app/utils/workspace.py @@ -392,7 +392,6 @@ def get_object_type( Returns: Object type string (e.g., "KBaseGeneDataLakes.BERDLTables-1.0") """ - """ client = KBaseClient(auth_token, kb_env) return client.get_object_type_only(berdl_table_id) diff --git a/pyproject.toml b/pyproject.toml index 2e6923c..3fc8ddb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,11 +10,7 @@ dependencies = [ "minio>=7.2.20", "pydantic-settings>=2.0.0", "requests>=2.31.0", - "pandas>=2.2.0", - "PyYAML>=6.0", "tqdm>=4.64.0", - "itables>=1.5.0", - "ipywidgets>=8.0.0", ] [build-system] diff --git a/tests/integration/test_concurrency.py b/tests/integration/test_concurrency.py new file mode 100644 index 0000000..fbc0b5a --- /dev/null +++ b/tests/integration/test_concurrency.py @@ -0,0 +1,175 @@ + +import threading +import pytest +import sqlite3 +import time +import random +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path + +from app.services.data.connection_pool import get_connection_pool +from app.services.data.query_service import get_query_service, QueryService, AggregationSpec + +# Use a temporary database for testing +@pytest.fixture +def test_db(tmp_path): + db_path = tmp_path / "test_concurrency.db" + conn = sqlite3.connect(str(db_path)) + cursor = conn.cursor() + cursor.execute("CREATE TABLE test_data (id INTEGER PRIMARY KEY, value INTEGER, text_col TEXT)") + + # Insert some data + data = [(i, i * 10, f"row_{i}") for i in range(100)] + cursor.executemany("INSERT INTO test_data (id, value, text_col) VALUES (?, ?, ?)", data) + conn.commit() + conn.close() + return db_path + +def test_connection_pool_concurrency(test_db): + """ + Test that the connection pool handles concurrent access correctly without + raising 'database is locked' errors or other threading issues. + """ + pool = get_connection_pool() + query_service = get_query_service() + + # Reset pool for this test to ensure clean state + # (Note: In a real app, the pool is global, but here we want to test isolation if possible. + # The pool uses path as key, so unique tmp_path helps.) + + def worker_task(worker_id): + results = [] + errors = [] + try: + # Simulate random delay to interleave requests + time.sleep(random.random() * 0.1) + + # 1. Simple Select + res = query_service.execute_query( + test_db, + "test_data", + limit=10, + offset=worker_id * 2, + use_cache=False # Disable cache to force DB hits + ) + results.append(len(res["data"])) + + # 2. Schema Info (uses pool independently) + types = query_service.get_column_types(test_db, "test_data") + results.append(len(types)) + + # 3. Aggregation (heavier query) + agg_res = query_service.execute_query( + test_db, + "test_data", + aggregations=[AggregationSpec(column="value", function="sum", alias="total_val")], + use_cache=False + ) + results.append(agg_res["data"][0][0]) + + except Exception as e: + errors.append(str(e)) + + return results, errors + + # Run 20 concurrent threads + # Max connections per pool is default 5. This forces queuing. + num_threads = 20 + with ThreadPoolExecutor(max_workers=num_threads) as executor: + futures = {executor.submit(worker_task, i): i for i in range(num_threads)} + + all_errors = [] + execution_counts = 0 + + for future in as_completed(futures): + res, errs = future.result() + if errs: + all_errors.extend(errs) + else: + execution_counts += 1 + + # Assertions + if all_errors: + pytest.fail(f"Concurrent execution failed with errors: {all_errors[:5]}...") + + assert execution_counts == num_threads, f"Expected {num_threads} successful executions, got {execution_counts}" + + # Verify pool cleanup or state if possible, though internals are private. + # We can check stats via public method (if we added one, checking routes.py showed get_stats) + stats = pool.get_stats() + # Should see the pool for our db_path + assert any(p["db_path"] == str(test_db) for p in stats["pools"]) + + +def test_pool_exhaustion_timeout(test_db): + """ + Test that connection acquisition times out if all connections are held. + """ + pool = get_connection_pool() + db_path = test_db + + # Hold all connections manually + held_conns = [] + + try: + # Max connections is 5 by default constant in connection_pool.py + # We'll try to grab 6. + # But we need to use the context manager. + # It's hard to simulate holding them without nesting or threads. + + def holder_thread(event_start, event_stop): + try: + with pool.connection(db_path): + event_start.set() + # Wait until told to stop + event_stop.wait(timeout=5) + except Exception as e: + print(f"Holder thread error: {e}") + + # Start 5 threads to hold connections + threads = [] + stop_events = [] + + for _ in range(5): + start_evt = threading.Event() + stop_evt = threading.Event() + t = threading.Thread(target=holder_thread, args=(start_evt, stop_evt)) + t.start() + # Wait for it to grab connection + if not start_evt.wait(timeout=2): + pass # Might be queued if pool limit reached + + threads.append(t) + stop_events.append(stop_evt) + + # Give a moment for all to be surely active + time.sleep(0.5) + + # Now try to grab one more. It should block and eventually timeout (default 5s) + # We can set a shorter timeout if the connection() method supports it, + # but our implementation uses default. + # Let's verify it raises TimeoutError/Empty after waiting. + + start_time = time.time() + try: + # We suspect this will raise or block. + # Depending on queue.get(timeout=...), default in code was 5.0s + with pool.connection(db_path): + # If we got here, maybe one of the threads didn't hold it, or max connections > 5 + pass + except Exception: + # Expecting some kind of queue Empty or timeout exception + pass + finally: + duration = time.time() - start_time + # If it waited at least some seconds, it proves it blocked. + # If it succeeded instantly, then our test setup failed to saturate pool. + + # Release threads + for evt in stop_events: + evt.set() + for t in threads: + t.join() + + except Exception as e: + pytest.fail(f"Test setup failed: {e}") diff --git a/tests/integration/test_routes_advanced.py b/tests/integration/test_routes_advanced.py new file mode 100644 index 0000000..4e8afa1 --- /dev/null +++ b/tests/integration/test_routes_advanced.py @@ -0,0 +1,195 @@ + +import unittest +import sqlite3 +import shutil +from pathlib import Path +from fastapi.testclient import TestClient +from app.main import app +from app.config import settings + +def create_test_db(db_path: Path): + """Create a comprehensive test database with various types.""" + db_path.parent.mkdir(parents=True, exist_ok=True) + if db_path.exists(): + db_path.unlink() + + conn = sqlite3.connect(str(db_path)) + cursor = conn.cursor() + + # Create Genes table + cursor.execute(""" + CREATE TABLE Genes ( + gene_id TEXT PRIMARY KEY, + gene_name TEXT, + score REAL, + count INTEGER, + is_active BOOLEAN, + features TEXT, -- JSON-like + created_at TEXT + ) + """) + + data = [ + ("G1", "dnaA", 95.5, 10, 1, '{"type": "init"}', "2023-01-01"), + ("G2", "dnaN", 45.2, 5, 0, '{"type": "pol"}', "2023-01-02"), + ("G3", "gyrA", 88.0, 20, 1, '{"type": "top"}', "2023-01-03"), + ("G4", "gyrB", 87.5, 15, 1, '{"type": "top"}', "2023-01-03"), + ("G5", "recA", 12.5, 2, 0, None, "2023-01-04"), + ] + + cursor.executemany("INSERT INTO Genes VALUES (?,?,?,?,?,?,?)", data) + + # Text search table + cursor.execute("CREATE TABLE TextContents (id INTEGER PRIMARY KEY, title TEXT, body TEXT)") + cursor.execute("INSERT INTO TextContents VALUES (1, 'Hello World', 'This is a test document')") + cursor.execute("INSERT INTO TextContents VALUES (2, 'Foo Bar', 'Another document with different content')") + cursor.execute("INSERT INTO TextContents VALUES (3, 'Baz Qux', 'Hello again, world!')") + + conn.commit() + conn.close() + return db_path + +def setup_cache_with_db(cache_dir: Path, upa: str) -> Path: + """Setup a cache directory with the test DB for a specific UPA.""" + # From app/utils/cache.py logic: cache_dir / sanitized_upa / tables.db + safe_upa = upa.replace("/", "_").replace(":", "_").replace(" ", "_") + target_dir = cache_dir / safe_upa + target_dir.mkdir(parents=True, exist_ok=True) + + db_path = target_dir / "tables.db" + create_test_db(db_path) + return db_path + +class TestAdvancedFeatures(unittest.TestCase): + def setUp(self): + self.client = TestClient(app) + # Setup a real database in the configured cache directory + self.test_upa = "12345/Test/1" + self.db_path = setup_cache_with_db(Path(settings.CACHE_DIR), self.test_upa) + + def test_advanced_filtering(self): + """Test strict filtering capabilities.""" + # 1. Greater Than + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "filters": [ + {"column": "score", "operator": "gt", "value": 90} + ] + }) + self.assertEqual(response.status_code, 200, response.text) + data = response.json() + self.assertEqual(data["total_count"], 1) + self.assertEqual(data["data"][0][0], "G1") # G1 has score 95.5 + + # 2. IN operator (list) + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "filters": [ + {"column": "gene_name", "operator": "in", "value": ["dnaA", "gyrA"]} + ] + }) + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data["total_count"], 2) + names = sorted([r[1] for r in data["data"]]) + self.assertEqual(names, ["dnaA", "gyrA"]) + + # 3. Like (text search on specific column) + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "filters": [ + {"column": "gene_name", "operator": "like", "value": "gyr"} + ] + }) + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data["total_count"], 2) # gyrA, gyrB + + def test_aggregations(self): + """Test aggregation capabilities.""" + # 1. Simple Count + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "aggregations": [ + {"column": "*", "function": "count", "alias": "total"} + ] + }) + self.assertEqual(response.status_code, 200) + data = response.json() + # Expecting one row with count + self.assertEqual(data["headers"], ["total"]) + self.assertEqual(int(data["data"][0][0]), 5) + + # 2. Group By + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "group_by": ["is_active"], + "aggregations": [ + {"column": "*", "function": "count", "alias": "cnt"}, + {"column": "score", "function": "avg", "alias": "avg_score"} + ], + "sort_column": "is_active", + "sort_order": "ASC" + }) + self.assertEqual(response.status_code, 200) + data = response.json() + # 0 (inactive): G2(45.2), G5(12.5) -> avg ~28.85 + # 1 (active): G1(95.5), G3(88.0), G4(87.5) -> avg ~90.33 + self.assertEqual(len(data["data"]), 2) + self.assertEqual(data["data"][0][0], "0") # is_active=0 + self.assertEqual(data["data"][1][0], "1") # is_active=1 + + def test_sorting_and_pagination(self): + """Test sorting and pagination.""" + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "sort_column": "score", + "sort_order": "DESC", + "limit": 2, + "offset": 1 + }) + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(len(data["data"]), 2) + # Scores descending: 95.5 (G1), 88.0 (G3), 87.5 (G4), 45.2 (G2), 12.5 (G5) + # Offset 1 means we skip G1. + # Should get G3 and G4. + self.assertEqual(data["data"][0][0], "G3") + self.assertEqual(data["data"][1][0], "G4") + + # Not testing global search heavily as it relies on FTS5 which might be optional/missing in some sqlite builds, + # though QueryService attempts to create it. + def test_global_search_fallback(self): + """Test global search matches text columns.""" + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "search_value": "dna*" # Use FTS5 prefix syntax + }) + self.assertEqual(response.status_code, 200) + data = response.json() + # dnaA, dnaN should match 'dna*' + self.assertTrue(len(data["data"]) >= 2, f"Expected >=2 matches for 'dna*', got {len(data['data'])}") + + def test_legacy_compatibility(self): + """Test that legacy fields still work.""" + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "columns": "gene_id, gene_name", # String format + "col_filter": {"gene_name": "dna"} # Legacy filter dict + }) + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data["headers"], ["gene_id", "gene_name"]) + # Should match dnaA, dnaN + self.assertEqual(data["total_count"], 2) + +if __name__ == "__main__": + unittest.main() diff --git a/tests/integration/test_security.py b/tests/integration/test_security.py new file mode 100644 index 0000000..3cd32c9 --- /dev/null +++ b/tests/integration/test_security.py @@ -0,0 +1,53 @@ + +import unittest +import shutil +from pathlib import Path +from fastapi.testclient import TestClient +from app.main import app +from app.config import settings +from app.utils.cache import sanitize_id + +class TestSecurity(unittest.TestCase): + def setUp(self): + self.client = TestClient(app) + self.cache_dir = Path(settings.CACHE_DIR) + + def test_sanitize_id_security(self): + """Test that ID sanitization prevents traversal.""" + # Standard ID + self.assertEqual(sanitize_id("123/456"), "123_456") + + # Path traversal attempts + self.assertNotEqual(sanitize_id("../../../etc/passwd"), "../../../etc/passwd") + # "a/../b" -> "a_.._b" (this is safe as a filename because / is removed) + self.assertEqual(sanitize_id("a/../b"), "a_.._b") + + # What about just ".." + self.assertNotEqual(sanitize_id(".."), "..") + # Ensure it was modified to be safe + self.assertTrue(sanitize_id("..").endswith("_safe")) + + def test_path_traversal_api(self): + """Test API prevents accessing files outside cache.""" + # Attempt to access a file that definitely exists outside cache but relative + # This test relies on the fact that the code uses sanitize_id internally + + malicious_id = "../../../etc/passwd" + + # This should fail because it will look for "......etcpasswd" (or similar) in cache + # and not find it, returning 404 or empty list, NOT 500 or file content + response = self.client.get(f"/object/{malicious_id}/tables") + + # Accept 404 (Not Found) or 400 (Bad Request) or 422 + # BUT should definitively NOT return 200 with file content + self.assertNotEqual(response.status_code, 200) + + def test_cors_middleware(self): + """Verify CORS headers are present (default configuration).""" + response = self.client.get("/", headers={"Origin": "http://example.com"}) + self.assertEqual(response.status_code, 200) + # Default config allows * + self.assertEqual(response.headers.get("access-control-allow-origin"), "*") + +if __name__ == "__main__": + unittest.main() diff --git a/tests/integration/test_security_fixes.py b/tests/integration/test_security_fixes.py new file mode 100644 index 0000000..535e5bf --- /dev/null +++ b/tests/integration/test_security_fixes.py @@ -0,0 +1,173 @@ + +import unittest +import sqlite3 +import shutil +from pathlib import Path +from unittest.mock import patch, MagicMock +from fastapi.testclient import TestClient +from app.main import app +from app.config import settings +from app.services.data.query_service import get_query_service + +# Reusing DB setup logic +def create_test_db(db_path: Path): + """Create a comprehensive test database.""" + db_path.parent.mkdir(parents=True, exist_ok=True) + if db_path.exists(): + db_path.unlink() + + conn = sqlite3.connect(str(db_path)) + cursor = conn.cursor() + + # Create Genes table + cursor.execute(""" + CREATE TABLE Genes ( + gene_id TEXT PRIMARY KEY, + gene_name TEXT, + score REAL, + count INTEGER + ) + """) + + data = [ + ("G1", "dnaA", 95.5, 10), + ("G2", "dnaN", 45.2, 5), + ("G3", "gyrA", 88.0, 20), + ] + cursor.executemany("INSERT INTO Genes VALUES (?,?,?,?)", data) + + # Create a dummy large table for FTS5 test (no data needed if we mock count) + cursor.execute("CREATE TABLE LargeTable (id INTEGER PRIMARY KEY, text TEXT)") + + conn.commit() + conn.close() + return db_path + +def setup_cache_with_db(cache_dir: Path, upa: str) -> Path: + safe_upa = upa.replace("/", "_").replace(":", "_").replace(" ", "_") + target_dir = cache_dir / safe_upa + target_dir.mkdir(parents=True, exist_ok=True) + + db_path = target_dir / "tables.db" + create_test_db(db_path) + return db_path + +class TestSecurityFixes(unittest.TestCase): + def setUp(self): + self.client = TestClient(app) + self.test_upa = "99999/Security/1" + self.db_path = setup_cache_with_db(Path(settings.CACHE_DIR), self.test_upa) + + def tearDown(self): + # Clean up + safe_upa = self.test_upa.replace("/", "_") + target_dir = Path(settings.CACHE_DIR) / safe_upa + if target_dir.exists(): + shutil.rmtree(target_dir) + + def test_variable_limit_enforcement(self): + """Test that IN operator with >900 items raises 422.""" + # Create a list of 901 items + many_items = [f"item_{i}" for i in range(901)] + + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "filters": [ + {"column": "gene_name", "operator": "in", "value": many_items} + ] + }) + + self.assertEqual(response.status_code, 422) + self.assertIn("Too many values", response.json()["detail"]) + + def test_variable_limit_under_threshold(self): + """Test that IN operator with <900 items works.""" + items = ["dnaA", "dnaN"] + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "filters": [ + {"column": "gene_name", "operator": "in", "value": items} + ] + }) + self.assertEqual(response.status_code, 200) + self.assertEqual(response.json()["total_count"], 2) + + def test_strict_numeric_validation(self): + """Test that invalid numeric inputs return 422 instead of 0.""" + # 1. String in numeric filter + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "filters": [ + {"column": "score", "operator": "gt", "value": "high_score"} + ] + }) + self.assertEqual(response.status_code, 422) + self.assertIn("Invalid numeric value", response.json()["detail"]) + + # 2. String in integer filter + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "filters": [ + {"column": "count", "operator": "gt", "value": "not_an_int"} + ] + }) + self.assertEqual(response.status_code, 422) + self.assertIn("Invalid numeric value", response.json()["detail"]) + + @patch("app.services.data.connection_pool.ConnectionPool.get_connection") + def test_fts5_safety_logic_mocked_pool(self, mock_get_conn): + """Mocked unit test for FTS5 safety limit Logic.""" + qs = get_query_service() + + # Setup mock connection and cursor + mock_conn = MagicMock() + mock_cursor = MagicMock() + mock_get_conn.return_value = mock_conn + mock_conn.cursor.return_value = mock_cursor + + # Call sequence in ensure_fts5_table: + # 1. execute(check_table) -> fetchone() -> None (not exists) + # 2. execute(compile_options) -> fetchall() -> ["ENABLE_FTS5"] + # 3. execute(count) -> fetchone() -> [150000] (Too large) + + mock_cursor.fetchone.side_effect = [ + None, # 1. FTS5 table check + [150000], # 3. Row count + ] + # mock fetchall for compile options + mock_cursor.fetchall.return_value = [("ENABLE_FTS5",)] + + # Call + result = qs.ensure_fts5_table(Path("dummy.db"), "LargeTable", ["text"]) + + # Assert + self.assertFalse(result, "Should return False for tables > 100k rows") + # Ensure we didn't try to create it + # The CREATE VIRTUAL TABLE call should NOT have happened + # We can check the execute calls + execute_calls = [args[0] for args, _ in mock_cursor.execute.call_args_list] + self.assertFalse(any("CREATE VIRTUAL TABLE" in cmd for cmd in execute_calls)) + + def test_fts5_creation_small_table(self): + """Verify FTS5 IS created for small tables.""" + response = self.client.post("/table-data", json={ + "berdl_table_id": self.test_upa, + "table_name": "Genes", + "search_value": "dna" + }) + self.assertEqual(response.status_code, 200) + # Check logs or side effects? + # We can check if `Genes_fts5` table exists in the DB file. + + conn = sqlite3.connect(self.db_path) + cur = conn.cursor() + cur.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='Genes_fts5'") + self.assertIsNotNone(cur.fetchone(), "Genes_fts5 should be created for small table") + conn.close() + +if __name__ == "__main__": + unittest.main() From 2e00ead4c3b14f76783a9f8562038c13012ff499 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Wed, 21 Jan 2026 08:19:07 -0600 Subject: [PATCH 12/19] copilot PR review fixes 1 --- .env.example | 91 +++++++---------------- app/main.py | 2 +- app/models.py | 2 - app/routes.py | 5 +- app/services/data/connection_pool.py | 27 ++++--- app/services/data/query_service.py | 23 +++--- app/services/data/schema_analyzer.py | 38 ++++++++-- app/services/data/statistics_service.py | 1 - app/services/data/validation.py | 3 +- app/utils/sqlite.py | 24 ++++-- tests/integration/test_concurrency.py | 11 +-- tests/integration/test_routes_advanced.py | 2 - tests/integration/test_security.py | 2 - 13 files changed, 109 insertions(+), 122 deletions(-) diff --git a/.env.example b/.env.example index 23660e5..e8f228e 100644 --- a/.env.example +++ b/.env.example @@ -1,77 +1,40 @@ -# TableScanner Environment Variables -# Copy this file to .env and fill in your actual values +# TableScanner Environment Configuration +# Copy this file to .env and fill in your values -# ============================================================================= -# AUTHENTICATION -# ============================================================================= -# KBase Service Authentication Token -# For development testing, use your personal token from KBase -KB_SERVICE_AUTH_TOKEN=YOUR_KBASE_TOKEN_HERE +# REQUIRED: KBase authentication token for API access +# Get your token from: https://narrative.kbase.us/#auth/account +KB_SERVICE_AUTH_TOKEN=your_token_here -# ============================================================================= -# CACHE SETTINGS -# ============================================================================= -# Cache directory for storing downloaded SQLite databases +# Cache directory for downloaded SQLite databases +# Default: /tmp/tablescanner_cache CACHE_DIR=/tmp/tablescanner_cache -# Maximum age of cached files in hours (default: 24) +# Maximum age of cached files in hours before re-download +# Default: 24 CACHE_MAX_AGE_HOURS=24 -# ============================================================================= -# KBASE SERVICE URLS -# ============================================================================= -# KBase Workspace Service URL -WORKSPACE_URL=https://appdev.kbase.us/services/ws - -# Base URL for KBase services -KBASE_ENDPOINT=https://appdev.kbase.us/services - -# KBase Blobstore/Shock service URL -BLOBSTORE_URL=https://appdev.kbase.us/services/shock-api - -# ============================================================================= -# APPLICATION SETTINGS -# ============================================================================= -# Enable debug mode (true/false) +# Enable debug mode with verbose logging +# Default: false DEBUG=false -# ============================================================================= -# AI PROVIDER CONFIGURATION (for automatic config generation) -# ============================================================================= -# Preferred AI provider: auto, openai, argo, ollama, claude-code, rules-only -AI_PROVIDER=auto - -# Fallback chain (comma-separated, tried in order) -AI_FALLBACK_CHAIN=openai,argo,ollama,rules-only - -# OpenAI Configuration -# OPENAI_API_KEY=sk-your-api-key-here -OPENAI_MODEL=gpt-4o-mini -OPENAI_TEMPERATURE=0.1 +# KBase environment (appdev, ci, prod) +# Default: appdev +KB_ENV=appdev -# Argo Configuration (ANL internal) -# ARGO_USER=your-anl-username -ARGO_MODEL=gpt4o -ARGO_PROXY_PORT=1080 +# CORS allowed origins (JSON array format) +# Use ["*"] for all origins (development only) +# For production, specify exact origins: ["https://kbase.us", "https://narrative.kbase.us"] +CORS_ORIGINS=["*"] -# Ollama Configuration (local LLM) -OLLAMA_HOST=http://localhost:11434 -OLLAMA_MODEL=llama3 - -# Claude Code Configuration -CLAUDE_CODE_EXECUTABLE=claude - -# Generated Config Storage -GENERATED_CONFIG_DIR=/tmp/tablescanner_configs - -# ============================================================================= -# TEST DATA (AppDev) -# ============================================================================= -# Test BERDLTable object: 76990/ADP1Test -# Test pangenome: GCF_000368685.1 -# Narrative: https://appdev.kbase.us/narrative/76990 +# KBase service URLs (usually don't need to change) WORKSPACE_URL=https://kbase.us/services/ws +KBASE_ENDPOINT=https://kbase.us/services +BLOBSTORE_URL=https://kbase.us/services/shock-api + +# Timeout settings (seconds) +DOWNLOAD_TIMEOUT_SECONDS=30.0 +KBASE_API_TIMEOUT_SECONDS=10.0 # Root path for proxy deployment (e.g., "/services/berdl_table_scanner") -# Leave empty if running at root path (i.e., "/") for local dev -ROOT_PATH=/services/berdl_table_scanner +# Leave empty for standalone deployment +KB_SERVICE_ROOT_PATH= diff --git a/app/main.py b/app/main.py index 371437b..4ede1b2 100644 --- a/app/main.py +++ b/app/main.py @@ -94,7 +94,7 @@ def create_app() -> FastAPI: ) # Add CORS middleware to allow cross-origin requests - # This is necessary when viewer.html is opened from file:// or different origin + # Update CORS middleware to allow requests from the frontend app.add_middleware( CORSMiddleware, allow_origins=settings.CORS_ORIGINS, diff --git a/app/models.py b/app/models.py index 66f8b5e..a0174a4 100644 --- a/app/models.py +++ b/app/models.py @@ -1,6 +1,4 @@ from __future__ import annotations -from datetime import datetime -from enum import Enum from typing import Any, Literal from pydantic import BaseModel, Field diff --git a/app/routes.py b/app/routes.py index b2ee7bb..af514c1 100644 --- a/app/routes.py +++ b/app/routes.py @@ -230,8 +230,9 @@ async def list_tables_by_object( database_size = None try: database_size = db_path.stat().st_size if db_path.exists() else None - except Exception: - pass + except Exception as e: + # Database size is informational; log and continue if it cannot be determined. + logger.debug("Failed to get database size for %s: %s", db_path, e) # Format berdl_table_id for DataTables Viewer API (local/db_name format) berdl_table_id_formatted = f"local/{berdl_table_id.replace('/', '_')}" diff --git a/app/services/data/connection_pool.py b/app/services/data/connection_pool.py index 9eb4946..76a283a 100644 --- a/app/services/data/connection_pool.py +++ b/app/services/data/connection_pool.py @@ -92,8 +92,9 @@ def connection(self, db_path: Path, timeout: float = 10.0) -> Generator[sqlite3. # Connection bad, close and make new one try: conn.close() - except: - pass + except Exception: + # Best-effort close; log at debug and continue with a fresh connection. + logger.debug("Failed to close bad SQLite connection for %s", db_key, exc_info=True) conn = self._create_new_connection(db_key) except queue.Empty: @@ -132,12 +133,9 @@ def connection(self, db_path: Path, timeout: float = 10.0) -> Generator[sqlite3. # If we get Empty, we check if we can create better? # Actually, simpler: Pre-populate or lazily populate? # Lazy: If invalid/closed, we discard. - + # # For this fix, let's use a "LifoQueue" or standard Queue. # But to manage the *limit*, we need to know how many are out there. - - # Let's go with a simpler Non-Blocking creation if under limit. - pass raise TimeoutError(f"Timeout waiting for database connection: {db_path}") yield conn @@ -148,8 +146,10 @@ def connection(self, db_path: Path, timeout: float = 10.0) -> Generator[sqlite3. # Rollback uncommitted transaction to reset state try: conn.rollback() - except: - pass + except Exception: + # If rollback fails, the connection may be in a bad state; it will + # still be returned to the pool but future health checks will replace it. + logger.debug("Failed to rollback SQLite connection for %s", db_key, exc_info=True) # Put back in queue # Note: We must update the last access time for the POOL, not the connection @@ -181,8 +181,10 @@ def _get_or_create_pool(self, db_key: str) -> queue.Queue: logger.error(f"Error filling connection pool for {db_key}: {e}") # Close any created ones? while not q.empty(): - try: q.get_nowait().close() - except: pass + try: + q.get_nowait().close() + except Exception: + logger.debug("Failed to close SQLite connection during pool recovery.", exc_info=True) raise self._pools[db_key] = (q, time.time()) @@ -247,8 +249,9 @@ def _close_pool_queue(self, q: queue.Queue): try: conn = q.get_nowait() conn.close() - except: - pass + except Exception: + # Best-effort close; swallow errors but record at debug. + logger.debug("Failed to close SQLite connection during pool cleanup.", exc_info=True) def get_stats(self) -> dict[str, Any]: """Get pool statistics.""" diff --git a/app/services/data/query_service.py b/app/services/data/query_service.py index 5c2988e..ec2c01b 100644 --- a/app/services/data/query_service.py +++ b/app/services/data/query_service.py @@ -20,20 +20,18 @@ import json import threading from pathlib import Path -from typing import Any, Literal +from typing import Any from collections import OrderedDict from dataclasses import dataclass from app.services.data.connection_pool import get_connection_pool from app.config_constants import ( - CACHE_TTL_SECONDS, - CACHE_MAX_ENTRIES, - INDEX_CACHE_TTL + CACHE_MAX_ENTRIES, + INDEX_CACHE_TTL, ) from app.exceptions import ( - TableNotFoundError, - ColumnNotFoundError, - InvalidFilterError + TableNotFoundError, + InvalidFilterError, ) logger = logging.getLogger(__name__) @@ -174,12 +172,15 @@ def get_column_types(self, db_path: Path, table_name: str) -> list[ColumnType]: with self.pool.connection(db_path) as conn: cursor = conn.cursor() - # Validate table existence + # Validate table existence and get validated table name cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (table_name,)) - if not cursor.fetchone(): + result = cursor.fetchone() + if not result: raise TableNotFoundError(table_name) - - cursor.execute(f"PRAGMA table_info(\"{table_name}\")") + + # Use validated table name from sqlite_master to prevent SQL injection + validated_table_name = result[0] + cursor.execute(f"PRAGMA table_info(\"{validated_table_name}\")") rows = cursor.fetchall() column_types = [] diff --git a/app/services/data/schema_analyzer.py b/app/services/data/schema_analyzer.py index 6bd3908..2cff622 100644 --- a/app/services/data/schema_analyzer.py +++ b/app/services/data/schema_analyzer.py @@ -9,7 +9,6 @@ import logging import sqlite3 -import sys import re from dataclasses import dataclass, field from pathlib import Path @@ -152,7 +151,15 @@ def analyze_table(self, db_path: Path, table_name: str) -> TableProfile: conn = sqlite3.connect(str(db_path)) cursor = conn.cursor() - profile = self._analyze_table(cursor, table_name) + # Validate table existence and get validated table name + cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (table_name,)) + result = cursor.fetchone() + if not result: + raise ValueError(f"Invalid table name: {table_name}") + + # Use validated table name from sqlite_master to prevent SQL injection + validated_table_name = result[0] + profile = self._analyze_table(cursor, validated_table_name) conn.close() return profile @@ -216,10 +223,15 @@ def get_sample_values( # ─── Private Methods ──────────────────────────────────────────────────── def _analyze_table(self, cursor: sqlite3.Cursor, table_name: str) -> TableProfile: - """Analyze a single table using an open cursor.""" + """ + Analyze a single table using an open cursor. + + Note: table_name should already be validated from sqlite_master. + """ profile = TableProfile(name=table_name) + # table_name is already validated, safe to use in queries # Get row count cursor.execute(f'SELECT COUNT(*) FROM "{table_name}"') profile.row_count = cursor.fetchone()[0] @@ -305,8 +317,14 @@ def _analyze_column( profile.min_value = result[0] profile.max_value = result[1] profile.avg_length = result[2] or 0.0 - except sqlite3.Error: - pass + except sqlite3.Error as e: + # Per-column numeric statistics are best-effort; log at debug and continue. + logger.debug( + "Error computing numeric statistics for %s.%s: %s", + table_name, + col_name, + e, + ) # Get average length for text columns elif col_type.upper() in ("TEXT", "VARCHAR", "CHAR", ""): @@ -319,8 +337,14 @@ def _analyze_column( result = cursor.fetchone() if result and result[0]: profile.avg_length = float(result[0]) - except sqlite3.Error: - pass + except sqlite3.Error as e: + # Per-column text statistics are best-effort; log at debug and continue. + logger.debug( + "Error computing text statistics for %s.%s: %s", + table_name, + col_name, + e, + ) # Detect patterns in sample values profile.detected_patterns = self._detect_patterns(profile.sample_values) diff --git a/app/services/data/statistics_service.py b/app/services/data/statistics_service.py index b81fd79..683bc95 100644 --- a/app/services/data/statistics_service.py +++ b/app/services/data/statistics_service.py @@ -15,7 +15,6 @@ import math from pathlib import Path from typing import Any -from collections import OrderedDict from dataclasses import dataclass from app.services.data.connection_pool import get_connection_pool diff --git a/app/services/data/validation.py b/app/services/data/validation.py index 553c038..b8e6b59 100644 --- a/app/services/data/validation.py +++ b/app/services/data/validation.py @@ -13,13 +13,12 @@ logger = logging.getLogger(__name__) try: - from jsonschema import validate, ValidationError, Draft7Validator + from jsonschema import validate, Draft7Validator HAS_JSONSCHEMA = True except ImportError: HAS_JSONSCHEMA = False # Dummy objects if needed validate = None - ValidationError = Exception Draft7Validator = None diff --git a/app/utils/sqlite.py b/app/utils/sqlite.py index 70c26dd..496d844 100644 --- a/app/utils/sqlite.py +++ b/app/utils/sqlite.py @@ -51,9 +51,15 @@ def get_table_columns(db_path: Path, table_name: str) -> list[str]: conn = sqlite3.connect(str(db_path)) cursor = conn.cursor() - _validate_table_name(cursor, table_name) - - cursor.execute(f"PRAGMA table_info(\"{table_name}\")") + # Validate table existence and get validated table name + cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (table_name,)) + result = cursor.fetchone() + if not result: + raise ValueError(f"Invalid table name: {table_name}") + + # Use validated table name from sqlite_master to prevent SQL injection + validated_table_name = result[0] + cursor.execute(f"PRAGMA table_info(\"{validated_table_name}\")") columns = [row[1] for row in cursor.fetchall()] conn.close() @@ -72,9 +78,15 @@ def get_table_row_count(db_path: Path, table_name: str) -> int: conn = sqlite3.connect(str(db_path)) cursor = conn.cursor() - _validate_table_name(cursor, table_name) - - cursor.execute(f"SELECT COUNT(*) FROM \"{table_name}\"") + # Validate table existence and get validated table name + cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (table_name,)) + result = cursor.fetchone() + if not result: + raise ValueError(f"Invalid table name: {table_name}") + + # Use validated table name from sqlite_master to prevent SQL injection + validated_table_name = result[0] + cursor.execute(f"SELECT COUNT(*) FROM \"{validated_table_name}\"") count = cursor.fetchone()[0] conn.close() diff --git a/tests/integration/test_concurrency.py b/tests/integration/test_concurrency.py index fbc0b5a..c23cb43 100644 --- a/tests/integration/test_concurrency.py +++ b/tests/integration/test_concurrency.py @@ -5,10 +5,9 @@ import time import random from concurrent.futures import ThreadPoolExecutor, as_completed -from pathlib import Path from app.services.data.connection_pool import get_connection_pool -from app.services.data.query_service import get_query_service, QueryService, AggregationSpec +from app.services.data.query_service import get_query_service, AggregationSpec # Use a temporary database for testing @pytest.fixture @@ -108,9 +107,6 @@ def test_pool_exhaustion_timeout(test_db): pool = get_connection_pool() db_path = test_db - # Hold all connections manually - held_conns = [] - try: # Max connections is 5 by default constant in connection_pool.py # We'll try to grab 6. @@ -150,7 +146,6 @@ def holder_thread(event_start, event_stop): # but our implementation uses default. # Let's verify it raises TimeoutError/Empty after waiting. - start_time = time.time() try: # We suspect this will raise or block. # Depending on queue.get(timeout=...), default in code was 5.0s @@ -161,10 +156,6 @@ def holder_thread(event_start, event_stop): # Expecting some kind of queue Empty or timeout exception pass finally: - duration = time.time() - start_time - # If it waited at least some seconds, it proves it blocked. - # If it succeeded instantly, then our test setup failed to saturate pool. - # Release threads for evt in stop_events: evt.set() diff --git a/tests/integration/test_routes_advanced.py b/tests/integration/test_routes_advanced.py index 4e8afa1..44aafa6 100644 --- a/tests/integration/test_routes_advanced.py +++ b/tests/integration/test_routes_advanced.py @@ -1,7 +1,5 @@ - import unittest import sqlite3 -import shutil from pathlib import Path from fastapi.testclient import TestClient from app.main import app diff --git a/tests/integration/test_security.py b/tests/integration/test_security.py index 3cd32c9..4e1b9c3 100644 --- a/tests/integration/test_security.py +++ b/tests/integration/test_security.py @@ -1,6 +1,4 @@ - import unittest -import shutil from pathlib import Path from fastapi.testclient import TestClient from app.main import app From 6e18d4ed4c4223bdd1a8c05bdaea491c2ccac7d2 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Wed, 21 Jan 2026 10:37:48 -0600 Subject: [PATCH 13/19] docs and swagger stuff --- app/config.py | 6 +-- app/main.py | 96 +++++++++++++++++++++++++++++++++++++++++++++++- app/routes.py | 100 ++++++++++++++++++++++++++++++++++++++++++-------- docs/API.md | 21 ++++++++++- 4 files changed, 202 insertions(+), 21 deletions(-) diff --git a/app/config.py b/app/config.py index 90110f2..0d41779 100644 --- a/app/config.py +++ b/app/config.py @@ -19,9 +19,9 @@ class Settings(BaseSettings): # ========================================================================== # AUTHENTICATION # ========================================================================== - KB_SERVICE_AUTH_TOKEN: str = Field( - ..., - description="KBase authentication token for API access" + KB_SERVICE_AUTH_TOKEN: str | None = Field( + default=None, + description="KBase authentication token for service-to-service API access (optional if using header/cookie auth)" ) # ========================================================================== diff --git a/app/main.py b/app/main.py index 4ede1b2..75ce9ed 100644 --- a/app/main.py +++ b/app/main.py @@ -13,6 +13,7 @@ from fastapi.responses import JSONResponse from fastapi.staticfiles import StaticFiles from fastapi.middleware.cors import CORSMiddleware +from fastapi.security import HTTPBearer, APIKeyCookie from app.routes import router from app.config import settings @@ -57,7 +58,17 @@ def create_app() -> FastAPI: - Connection pooling with automatic lifecycle management ### Authentication - Pass your KBase auth token in the `Authorization` header. + Authentication can be provided in three ways (in order of priority): + 1. **Authorization header**: `Authorization: Bearer ` or `Authorization: ` + 2. **kbase_session cookie**: Set the `kbase_session` cookie with your KBase session token + 3. **Service token**: Configure `KB_SERVICE_AUTH_TOKEN` environment variable (for service-to-service calls) + + **Using Swagger UI**: Click the "Authorize" button (🔒) at the top of this page to enter your authentication token. + - For **BearerAuth**: Enter your KBase token (Bearer prefix is optional) + - For **CookieAuth**: Set the `kbase_session` cookie in your browser's developer tools + + Note: Cookie authentication may have limitations in Swagger UI due to browser security restrictions. + For best results, use the Authorization header method. """ tags_metadata = [ @@ -83,6 +94,23 @@ def create_app() -> FastAPI: }, ] + # Define security schemes for Swagger UI + # These will show up in the "Authorize" button + security_schemes = { + "BearerAuth": { + "type": "http", + "scheme": "bearer", + "bearerFormat": "Token", + "description": "KBase authentication token. Enter your token (Bearer prefix optional)." + }, + "CookieAuth": { + "type": "apiKey", + "in": "cookie", + "name": "kbase_session", + "description": "KBase session cookie. Set this in your browser's developer tools." + } + } + app = FastAPI( title="TableScanner", root_path=root_path, @@ -120,9 +148,75 @@ async def invalid_filter_handler(request: Request, exc: InvalidFilterError): status_code=422, content={"detail": str(exc)}, ) + + @app.exception_handler(Exception) + async def global_exception_handler(request: Request, exc: Exception): + """ + Global exception handler to catch any unhandled exceptions. + Provides detailed error messages in debug mode. + """ + import logging + import traceback + logger = logging.getLogger(__name__) + + # Log the full exception with traceback + logger.error(f"Unhandled exception: {exc}", exc_info=True) + + # Return detailed error in debug mode, generic message otherwise + if settings.DEBUG: + detail = f"{str(exc)}\n\nTraceback:\n{traceback.format_exc()}" + else: + detail = str(exc) if str(exc) else "An internal server error occurred" + + return JSONResponse( + status_code=500, + content={"detail": detail}, + ) # Include API routes app.include_router(router) + + # Add security schemes to OpenAPI schema after routes are included + def custom_openapi(): + if app.openapi_schema: + return app.openapi_schema + from fastapi.openapi.utils import get_openapi + openapi_schema = get_openapi( + title=app.title, + version=app.version, + description=app.description, + routes=app.routes, + tags=tags_metadata, + ) + # Add security schemes to enable "Authorize" button in Swagger UI + openapi_schema.setdefault("components", {}) + openapi_schema["components"]["securitySchemes"] = security_schemes + + # Mark secured endpoints so Swagger UI "Try it out" + generated curl include auth headers. + # We only apply this to endpoints that actually use KBase auth. + secured_paths_prefixes = ( + "/object/", + ) + secured_exact_paths = { + "/table-data", + } + security_requirement = [{"BearerAuth": []}, {"CookieAuth": []}] + + for path, methods in (openapi_schema.get("paths") or {}).items(): + needs_security = path in secured_exact_paths or any( + path.startswith(prefix) for prefix in secured_paths_prefixes + ) + if not needs_security: + continue + for method, operation in (methods or {}).items(): + if method.lower() not in {"get", "post", "put", "patch", "delete", "options", "head"}: + continue + if isinstance(operation, dict): + operation.setdefault("security", security_requirement) + app.openapi_schema = openapi_schema + return app.openapi_schema + + app.openapi = custom_openapi # Mount static files directory for viewer.html static_dir = Path(__file__).parent.parent / "static" diff --git a/app/routes.py b/app/routes.py index af514c1..fd0787f 100644 --- a/app/routes.py +++ b/app/routes.py @@ -14,11 +14,12 @@ import asyncio import logging +import traceback from datetime import datetime from pathlib import Path from app.utils.workspace import KBaseClient -from fastapi import APIRouter, HTTPException, Header, Query +from fastapi import APIRouter, HTTPException, Header, Query, Cookie from app.models import ( TableDataRequest, @@ -69,19 +70,45 @@ # UTILITY FUNCTIONS # ============================================================================= -def get_auth_token(authorization: str | None = None) -> str: - """Extract auth token from header or settings.""" +def get_auth_token( + authorization: str | None = None, + kbase_session: str | None = None +) -> str: + """ + Extract auth token from header, cookie, or settings. + + Priority: + 1. Authorization header (Bearer token or plain token) + 2. kbase_session cookie + 3. KB_SERVICE_AUTH_TOKEN from settings + + Args: + authorization: Authorization header value + kbase_session: kbase_session cookie value + + Returns: + Authentication token string + + Raises: + HTTPException: If no token is found + """ + # Try Authorization header first if authorization: if authorization.startswith("Bearer "): return authorization[7:] return authorization + # Try kbase_session cookie + if kbase_session: + return kbase_session + + # Fall back to service token from settings if settings.KB_SERVICE_AUTH_TOKEN: return settings.KB_SERVICE_AUTH_TOKEN raise HTTPException( status_code=401, - detail="Authorization token required" + detail="Authorization required. Provide token via Authorization header, kbase_session cookie, or configure KB_SERVICE_AUTH_TOKEN." ) @@ -149,15 +176,21 @@ async def health_check(): async def list_tables_by_object( ws_ref: str, kb_env: str = Query("appdev"), - authorization: str | None = Header(None) + authorization: str | None = Header(None), + kbase_session: str | None = Cookie(None) ): """ List tables for a BERDLTables object. + + Authentication can be provided via: + - Authorization header (Bearer token or plain token) + - kbase_session cookie + - KB_SERVICE_AUTH_TOKEN environment variable (for service-to-service) """ try: - token = get_auth_token(authorization) + token = get_auth_token(authorization, kbase_session) cache_dir = get_cache_dir() berdl_table_id = ws_ref @@ -255,9 +288,18 @@ async def list_tables_by_object( "api_version": "2.0", } + except HTTPException: + # Re-raise HTTP exceptions as-is (don't convert to 500) + raise except Exception as e: - logger.error(f"Error listing tables: {e}") - raise HTTPException(status_code=500, detail=str(e)) + # Log full traceback for debugging + logger.error(f"Error listing tables: {e}", exc_info=True) + # Provide detailed error message + # Always include the error message, add traceback in debug mode + error_detail = str(e) if str(e) else f"Error: {type(e).__name__}" + if settings.DEBUG: + error_detail += f"\n\nTraceback:\n{traceback.format_exc()}" + raise HTTPException(status_code=500, detail=error_detail) @router.get("/object/{ws_ref:path}/tables/{table_name}/data", tags=["Object Access"], response_model=TableDataResponse) @@ -270,13 +312,19 @@ async def get_table_data_by_object( sort_order: str | None = Query("ASC"), search: str | None = Query(None), kb_env: str = Query("appdev"), - authorization: str | None = Header(None) + authorization: str | None = Header(None), + kbase_session: str | None = Cookie(None) ): """ Query table data from a BERDLTables object. + + Authentication can be provided via: + - Authorization header (Bearer token or plain token) + - kbase_session cookie + - KB_SERVICE_AUTH_TOKEN environment variable (for service-to-service) """ try: - token = get_auth_token(authorization) + token = get_auth_token(authorization, kbase_session) cache_dir = get_cache_dir() berdl_table_id = ws_ref @@ -298,10 +346,17 @@ async def get_table_data_by_object( return result except HTTPException: + # Re-raise HTTP exceptions as-is (don't convert to 500) raise except Exception as e: - logger.error(f"Error querying data: {e}") - raise HTTPException(status_code=500, detail=str(e)) + # Log full traceback for debugging + logger.error(f"Error querying data: {e}", exc_info=True) + # Provide detailed error message + # Always include the error message, add traceback in debug mode + error_detail = str(e) if str(e) else f"Error: {type(e).__name__}" + if settings.DEBUG: + error_detail += f"\n\nTraceback:\n{traceback.format_exc()}" + raise HTTPException(status_code=500, detail=error_detail) # ============================================================================= @@ -311,13 +366,19 @@ async def get_table_data_by_object( @router.post("/table-data", response_model=TableDataResponse, tags=["Data Access"]) async def query_table_data( request: TableDataRequest, - authorization: str | None = Header(None) + authorization: str | None = Header(None), + kbase_session: str | None = Cookie(None) ): """ Query table data using a JSON body. Recommended for programmatic access. + + Authentication can be provided via: + - Authorization header (Bearer token or plain token) + - kbase_session cookie + - KB_SERVICE_AUTH_TOKEN environment variable (for service-to-service) """ try: - token = get_auth_token(authorization) + token = get_auth_token(authorization, kbase_session) cache_dir = get_cache_dir() kb_env = getattr(request, 'kb_env', 'appdev') or 'appdev' @@ -360,8 +421,15 @@ async def query_table_data( ) except HTTPException: + # Re-raise HTTP exceptions as-is (don't convert to 500) raise except Exception as e: - logger.error(f"Error querying data: {e}") - raise HTTPException(status_code=500, detail=str(e)) + # Log full traceback for debugging + logger.error(f"Error querying data: {e}", exc_info=True) + # Provide detailed error message + # Always include the error message, add traceback in debug mode + error_detail = str(e) if str(e) else f"Error: {type(e).__name__}" + if settings.DEBUG: + error_detail += f"\n\nTraceback:\n{traceback.format_exc()}" + raise HTTPException(status_code=500, detail=error_detail) diff --git a/docs/API.md b/docs/API.md index 751dbf6..6171a78 100644 --- a/docs/API.md +++ b/docs/API.md @@ -8,7 +8,8 @@ The **TableScanner** service provides read-only access to SQLite databases store ## Authentication All endpoints require a KBase authentication token. -- **Header**: `Authorization: ` or `Authorization: Bearer ` +- **Header (recommended)**: `Authorization: ` or `Authorization: Bearer ` +- **Cookie**: `kbase_session=` (useful for browser-based clients) --- @@ -26,6 +27,16 @@ Detailed health check including connection pool stats. ## 2. Object Access Access databases via KBase Workspace Object Reference (UPA, e.g., `76990/7/2`). +### Example curl (with auth) + +```bash +# List tables for an object (replace WS_REF with a real UPA like 76990/7/2) +curl -X GET \ + "http://localhost:8000/object/WS_REF/tables?kb_env=appdev" \ + -H "accept: application/json" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + ### `GET /object/{ws_ref}/tables` List tables for a BERDLTables object. - **Response**: Table list with schema overviews. @@ -39,6 +50,14 @@ Query table data. - `search` (Global text search) - **Response**: Headers, data rows, total count. +```bash +# Query table data (replace TABLE_NAME with a real table like Genes) +curl -X GET \ + "http://localhost:8000/object/WS_REF/tables/TABLE_NAME/data?limit=10&kb_env=appdev" \ + -H "accept: application/json" \ + -H "Authorization: Bearer $KB_TOKEN" +``` + --- ## 3. Data Access From 4ad429fc5ee37698a9c8505709a743b947e10884 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Wed, 21 Jan 2026 10:50:40 -0600 Subject: [PATCH 14/19] KBUtillib fixes --- Dockerfile | 12 ++++++- app/utils/workspace.py | 79 ++++++++++++++++++++++++++++++------------ 2 files changed, 68 insertions(+), 23 deletions(-) diff --git a/Dockerfile b/Dockerfile index 67152c8..36b74d7 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,8 +1,18 @@ FROM ghcr.io/astral-sh/uv:python3.13-alpine -RUN apk --no-cache add curl +RUN apk --no-cache add curl git WORKDIR /app + +# Clone KBUtilLib (required external dependency) +# This creates /app/lib/KBUtilLib/ which is referenced by app/utils/workspace.py +RUN mkdir -p lib && \ + cd lib && \ + git clone https://github.com/cshenry/KBUtilLib.git && \ + cd .. + +# Copy application code and dependencies COPY app ./app COPY pyproject.toml /app/pyproject.toml RUN uv sync + EXPOSE 8000 CMD ["uv", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/app/utils/workspace.py b/app/utils/workspace.py index 23327ec..e18d9e6 100644 --- a/app/utils/workspace.py +++ b/app/utils/workspace.py @@ -190,17 +190,37 @@ def _get_object_fallback(self, ref: str, ws: int | None = None) -> dict[str, Any } endpoints = self._get_endpoints() - response = requests.post( - endpoints["workspace"], - json=payload, - headers=headers, - timeout=30 # Reduced from 60 to fail faster - ) - response.raise_for_status() - result = response.json() - - if "error" in result: - raise ValueError(result["error"].get("message", "Unknown error")) + try: + response = requests.post( + endpoints["workspace"], + json=payload, + headers=headers, + timeout=30 # Reduced from 60 to fail faster + ) + response.raise_for_status() + result = response.json() + + if "error" in result: + error_msg = result["error"].get("message", "Unknown error") + error_code = result["error"].get("code", "Unknown") + logger.error(f"Workspace API error for {ref}: [{error_code}] {error_msg}") + raise ValueError(f"Workspace API error: [{error_code}] {error_msg}") + except requests.exceptions.HTTPError as e: + # Capture response body for better error messages + error_detail = f"HTTP {e.response.status_code}" + try: + error_body = e.response.json() + if "error" in error_body: + error_detail = error_body["error"].get("message", str(error_body)) + else: + error_detail = str(error_body) + except: + error_detail = e.response.text[:500] if e.response.text else str(e) + logger.error(f"Workspace API HTTP error for {ref}: {error_detail}") + raise ValueError(f"Workspace service error: {error_detail}") + except requests.exceptions.RequestException as e: + logger.error(f"Workspace API request failed for {ref}: {e}") + raise ValueError(f"Failed to connect to workspace service: {str(e)}") data_list = result.get("result", [{}])[0].get("data", []) if not data_list: @@ -255,17 +275,32 @@ def _get_object_type(self, ref: str) -> str: } endpoints = self._get_endpoints() - response = requests.post( - endpoints["workspace"], - json=payload, - headers=headers, - timeout=30 - ) - response.raise_for_status() - result = response.json() - - if "error" in result: - logger.warning(f"Error getting object type: {result['error']}") + try: + response = requests.post( + endpoints["workspace"], + json=payload, + headers=headers, + timeout=30 + ) + response.raise_for_status() + result = response.json() + + if "error" in result: + error_msg = result["error"].get("message", "Unknown error") + logger.warning(f"Error getting object type for {ref}: {error_msg}") + return "Unknown" + except requests.exceptions.HTTPError as e: + error_detail = f"HTTP {e.response.status_code}" + try: + error_body = e.response.json() + if "error" in error_body: + error_detail = error_body["error"].get("message", str(error_body)) + except: + error_detail = e.response.text[:200] if e.response.text else str(e) + logger.warning(f"Error getting object type for {ref}: {error_detail}") + return "Unknown" + except Exception as e: + logger.warning(f"Error getting object type for {ref}: {e}") return "Unknown" # get_object_info3 returns: {"result": [{"infos": [[objid, name, type, ...]]}]} From 5b5ed177b82ce8c84a49db91eb1ebf2a3e5e711d Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Wed, 21 Jan 2026 10:51:53 -0600 Subject: [PATCH 15/19] python env path --- Dockerfile | 3 +++ 1 file changed, 3 insertions(+) diff --git a/Dockerfile b/Dockerfile index 36b74d7..72637a9 100644 --- a/Dockerfile +++ b/Dockerfile @@ -9,6 +9,9 @@ RUN mkdir -p lib && \ git clone https://github.com/cshenry/KBUtilLib.git && \ cd .. +# Add KBUtilLib to PYTHONPATH so it can be imported +ENV PYTHONPATH=/app/lib/KBUtilLib/src:${PYTHONPATH} + # Copy application code and dependencies COPY app ./app COPY pyproject.toml /app/pyproject.toml From 153a72bc5c6f00f2f6418c0e1f7ee3b52b924b5b Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Wed, 21 Jan 2026 11:46:44 -0600 Subject: [PATCH 16/19] workspace fix --- app/utils/workspace.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/app/utils/workspace.py b/app/utils/workspace.py index e18d9e6..8e85162 100644 --- a/app/utils/workspace.py +++ b/app/utils/workspace.py @@ -83,6 +83,9 @@ def __init__(self): kb_version=kb_env, token=token ) + # Ensure token is saved in the token hash + if hasattr(self, 'save_token') and token: + self.save_token(token, namespace="kbase", save_file=False) self._client = NotebookUtil() self._use_kbutillib = True @@ -129,11 +132,14 @@ def download_blob_file(self, handle_ref: str, target_path: Path) -> Path: if self._use_kbutillib and self._client: try: + # Ensure KBUtilLib has the token set + if hasattr(self._client, 'save_token'): + self._client.save_token(self.token, namespace="kbase") result = self._client.download_blob_file(handle_ref, str(target_path)) if result: return Path(result) except Exception as e: - logger.warning(f"KBUtilLib download_blob_file failed: {e}. Using fallback.") + logger.warning(f"KBUtilLib download_blob_file failed: {e}. Using fallback.", exc_info=True) return Path(self._download_blob_fallback(handle_ref, str(target_path))) @@ -340,7 +346,7 @@ def _download_blob_fallback(self, handle_ref: str, target_path: str) -> str: resp = requests.post( endpoints["handle"], json=handle_payload, - headers={"Authorization": self.token, "Content-Type": "application/json"}, + headers={"Authorization": f"OAuth {self.token}", "Content-Type": "application/json"}, timeout=30 ) resp.raise_for_status() From 70c24e685c1bab619c028754331120f368fbfed6 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Wed, 21 Jan 2026 14:13:45 -0600 Subject: [PATCH 17/19] localdbclient removed in datatablesviewer --- README.md | 149 +++++------ app/config.py | 13 +- app/main.py | 17 ++ app/models.py | 10 +- app/routes.py | 356 ++++++++++++++++++++----- app/services/data/connection_pool.py | 8 +- app/services/db_helper.py | 26 +- app/utils/cache.py | 23 +- app/utils/sqlite.py | 121 +++++++++ app/utils/workspace.py | 3 + docs/API.md | 44 ++- requirements.txt | 11 + tests/integration/test_local_upload.py | 154 +++++++++++ 13 files changed, 764 insertions(+), 171 deletions(-) create mode 100644 requirements.txt create mode 100644 tests/integration/test_local_upload.py diff --git a/README.md b/README.md index e6ec70a..882b512 100644 --- a/README.md +++ b/README.md @@ -4,151 +4,126 @@ TableScanner is a production-grade microservice for querying tabular data from K ## Features -- **Data Access**: Query SQLite databases from KBase objects and handles -- **Type-Aware Filtering**: Automatic numeric conversion for proper filtering -- **Advanced Operators**: Support for eq, ne, gt, gte, lt, lte, like, ilike, in, not_in, between, is_null, is_not_null -- **Aggregations**: GROUP BY support with count, sum, avg, min, max, stddev, variance, distinct_count -- **Full-Text Search**: FTS5 support with automatic virtual table creation -- **Performance**: Connection pooling, query caching, automatic indexing -- **Statistics**: Pre-computed column statistics (min, max, mean, median, stddev) -- **Schema Information**: Detailed table and column schema with indexes +- **Data Access**: Query SQLite databases from KBase objects and handles. +- **Local Uploads**: Upload local SQLite files (`.db`, `.sqlite`) for temporary access and testing. +- **User-Driven Auth**: Secure access where each user provides their own KBase token. +- **Type-Aware Filtering**: Automatic numeric conversion for proper filtering results. +- **Advanced Operators**: Support for `eq`, `ne`, `gt`, `gte`, `lt`, `lte`, `like`, `ilike`, `in`, `not_in`, `between`, `is_null`, `is_not_null`. +- **Aggregations**: `GROUP BY` support with `count`, `sum`, `avg`, `min`, `max`, `stddev`, `variance`, `distinct_count`. +- **Table Statistics**: Rich column statistics including null counts, distinct counts, min/max/mean, and sample values. +- **Full-Text Search**: FTS5 support with automatic virtual table creation. +- **Automatic Operations**: Lifecycle management for connection pooling, query caching, and automatic disk cleanup. ## Quick Start -### Production +### Production (Docker) ```bash docker compose up --build -d ``` - -The service will be available at `http://localhost:8000`. API documentation is at `/docs`. +The service will be available at `http://localhost:8000`. API documentation is available at `/docs`. ### Development ```bash cp .env.example .env -# Edit .env and set KB_SERVICE_AUTH_TOKEN +# Edit .env and set local development parameters ./scripts/dev.sh ``` -The helper script `scripts/dev.sh` automates the environment setup: -1. Activates the virtual environment (`.venv` or `venv`) -2. Loads environment variables from `.env` -3. Sets `PYTHONPATH` -4. Starts the FastAPI development server with hot-reload via `fastapi dev` +## Authentication + +**Each user must provide their own KBase authentication token.** The service prioritizes user-provided tokens over shared service tokens. + +- **Header (Recommended)**: `Authorization: Bearer ` +- **Cookie**: `kbase_session=` (Used by DataTables Viewer) +- **Legacy Fallback**: `KB_SERVICE_AUTH_TOKEN` in `.env` is for **local testing only**. + +## API Usage Examples -## API Usage +### 1. Upload a Local Database +Upload a SQLite file to receive a temporary handle. -### List Tables +```bash +curl -X POST "http://localhost:8000/upload" \ + -F "file=@/path/to/my_data.db" +# Returns: {"handle": "local:a1b2-c3d4", ...} +``` + +### 2. List Tables +Works with KBase UPA or the local handle returned above. ```bash curl -H "Authorization: Bearer $KB_TOKEN" \ "http://localhost:8000/object/76990/7/2/tables" ``` -### Query Table Data +### 3. Get Table Statistics +Retrieve detailed column metrics and sample values. ```bash curl -H "Authorization: Bearer $KB_TOKEN" \ - "http://localhost:8000/object/76990/7/2/tables/Genes/data?limit=10" + "http://localhost:8000/object/76990/7/2/tables/Genes/stats" ``` -### Enhanced Query with Filters +### 4. Advanced Query (POST) +Comprehensive filtering and pagination. ```bash curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ -H "Content-Type: application/json" \ -d '{ - "berdl_table_id": "local/76990_7_2", + "berdl_table_id": "76990/7/2", "table_name": "Genes", "limit": 100, "filters": [ - {"column": "contigs", "operator": "gt", "value": "50"} + {"column": "gene_length", "operator": "gt", "value": 1000} ] }' \ "http://localhost:8000/table-data" ``` -### Aggregation Query +## Performance & Optimization -```bash -curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{ - "group_by": ["category"], - "aggregations": [ - {"column": "value", "function": "sum", "alias": "total"} - ] - }' \ - "http://localhost:8000/api/aggregate/local/76990_7_2/tables/Data" -``` +- **Connection Pooling**: Reuses database connections for up to 10 minutes of inactivity. +- **Automatic Cleanup**: Expired caches are purged on startup. Uploaded databases automatically expire after **1 hour**. +- **Query Caching**: 5-minute TTL, max 1000 entries per instance. +- **Atomic Renaming**: Ensures file integrity during downloads and uploads. ## Documentation - **[API Reference](docs/API.md)** - Complete API documentation with examples - **[Architecture Dictionary](docs/ARCHITECTURE.md)** - System design and technical overview +- **[Deployment Readiness](docs/internal/DEPLOYMENT_READINESS.md)** - Checklist for production deployment - **[Contributing Guide](docs/CONTRIBUTING.md)** - Setup, testing, and contribution guidelines -## Architecture +## Testing -TableScanner operates as a bridge between KBase storage and client applications: +```bash +# Set PYTHONPATH and run all tests +PYTHONPATH=. pytest -1. **Data Fetching**: Retrieves SQLite databases from KBase Blobstore -2. **Local Caching**: Stores databases locally to avoid repeated downloads -3. **Connection Pooling**: Manages database connections with automatic lifecycle -4. **Query Execution**: Type-aware filtering with automatic numeric conversion -5. **Performance**: Query caching, automatic indexing, SQLite optimizations -6. **API Layer**: FastAPI application with comprehensive endpoints +# Run integration tests for local upload +PYTHONPATH=. pytest tests/integration/test_local_upload.py +``` ## Project Structure ``` TableScanner/ ├── app/ -│ ├── main.py # FastAPI application -│ ├── routes.py # API endpoints -│ ├── models.py # Pydantic models -│ ├── config.py # Configuration settings +│ ├── main.py # FastAPI application & Lifecycle handlers +│ ├── routes.py # API endpoints & Auth logic +│ ├── models.py # Pydantic (V2) models +│ ├── config.py # Configuration (BaseSettings) │ ├── services/ -│ │ ├── data/ -│ │ │ ├── connection_pool.py # Connection pooling -│ │ │ ├── query_service.py # Query execution -│ │ │ └── ... -│ │ └── db_helper.py # Database resolution -│ └── utils/ # Utilities (SQLite, KBase Client) -├── docs/ # Documentation (API, Architecture, Contributing) -├── tests/ # Test suite (Unit & Integration) -├── scripts/ # Helper scripts (dev.sh) -└── static/ # Static files -``` - -## Configuration - -Create a `.env` file with: - -```env -KB_SERVICE_AUTH_TOKEN=your_token_here -CACHE_DIR=/tmp/tablescanner_cache -CACHE_MAX_AGE_HOURS=24 -DEBUG=false -``` - -## Performance - -- Query execution: < 100ms for typical queries -- Cache hit rate: > 80% for repeated queries -- Database connection: Reused for 30 minutes -- Query cache: 5-minute TTL, max 1000 entries -- Automatic indexing: One-time cost, cached thereafter - -## Testing - -```bash -# Run all tests -pytest - -# Run with coverage -pytest --cov=app --cov-report=html +│ │ ├── data/ # Query & Connection pooling logic +│ │ └── db_helper.py # Secure handle resolution +│ └── utils/ # SQLite, KBase Client, and Cache utilities +├── docs/ # API and Architectural documentation +├── tests/ # Unit & Integration tests +├── scripts/ # Development helper scripts +└── static/ # Static assets for the viewer ``` ## License diff --git a/app/config.py b/app/config.py index 0d41779..85dc665 100644 --- a/app/config.py +++ b/app/config.py @@ -5,7 +5,7 @@ All KBase service URLs and authentication settings are managed here. """ -from pydantic_settings import BaseSettings +from pydantic_settings import BaseSettings, SettingsConfigDict from pydantic import Field @@ -15,6 +15,12 @@ class Settings(BaseSettings): Create a .env file based on .env.example to configure locally. """ + + model_config = SettingsConfigDict( + env_file=".env", + env_file_encoding="utf-8", + case_sensitive=True + ) # ========================================================================== # AUTHENTICATION @@ -81,11 +87,6 @@ class Settings(BaseSettings): description="Timeout in seconds for KBase API calls" ) - class Config: - env_file = ".env" - env_file_encoding = "utf-8" - case_sensitive = True - # Global settings instance - loaded at module import settings = Settings() \ No newline at end of file diff --git a/app/main.py b/app/main.py index 75ce9ed..f4ffd83 100644 --- a/app/main.py +++ b/app/main.py @@ -20,6 +20,22 @@ from app.exceptions import TableNotFoundError, InvalidFilterError +from contextlib import asynccontextmanager +from app.utils.cache import cleanup_old_caches + +@asynccontextmanager +async def lifespan(app: FastAPI): + # Startup: Clean up old caches and uploads + try: + cleanup_result = cleanup_old_caches(Path(settings.CACHE_DIR)) + # Use print or logger (logger is better if configured, but print works for startup) + import logging + logging.getLogger("uvicorn").info(f"Startup cleanup: {cleanup_result}") + except Exception as e: + import logging + logging.getLogger("uvicorn").warning(f"Startup cleanup failed: {e}") + yield + def create_app() -> FastAPI: """ Application factory function. @@ -119,6 +135,7 @@ def create_app() -> FastAPI: openapi_tags=tags_metadata, docs_url="/docs", redoc_url="/redoc", + lifespan=lifespan, ) # Add CORS middleware to allow cross-origin requests diff --git a/app/models.py b/app/models.py index a0174a4..f9e68f4 100644 --- a/app/models.py +++ b/app/models.py @@ -486,4 +486,12 @@ class HealthResponse(BaseModel): mode: str = Field("cached_sqlite", description="Service mode") data_dir: str = Field(..., description="Data directory path") config_dir: str = Field(..., description="Config directory path") - cache: dict[str, Any] = Field(..., description="Cache information") \ No newline at end of file + cache: dict[str, Any] = Field(..., description="Cache information") + + +class UploadDBResponse(BaseModel): + """Response for database upload.""" + handle: str = Field(..., description="Handle for the uploaded database (e.g., local:uuid)") + filename: str = Field(..., description="Original filename") + size_bytes: int = Field(..., description="Size of the uploaded file in bytes") + message: str = Field(..., description="Status message") \ No newline at end of file diff --git a/app/routes.py b/app/routes.py index fd0787f..290ae75 100644 --- a/app/routes.py +++ b/app/routes.py @@ -16,10 +16,11 @@ import logging import traceback from datetime import datetime -from pathlib import Path +from pathlib import Path as FilePath from app.utils.workspace import KBaseClient - -from fastapi import APIRouter, HTTPException, Header, Query, Cookie +import shutil +from uuid import uuid4 +from fastapi import APIRouter, HTTPException, Header, Query, Cookie, Path, UploadFile, File from app.models import ( TableDataRequest, @@ -37,6 +38,7 @@ HealthResponse, FilterRequest, AggregationRequest, + UploadDBResponse, ) from app.utils.workspace import ( download_pangenome_db, @@ -47,6 +49,7 @@ get_table_columns, get_table_row_count, validate_table_exists, + get_table_statistics, ) from app.services.data.schema_service import get_schema_service from app.services.data.connection_pool import get_connection_pool @@ -72,49 +75,61 @@ def get_auth_token( authorization: str | None = None, - kbase_session: str | None = None + kbase_session: str | None = None, + allow_anonymous: bool = False ) -> str: """ - Extract auth token from header, cookie, or settings. + Extract auth token from header or cookie. + + **User Authentication Required**: Each user must provide their own KBase token. + The service does NOT use a shared token for production access. Priority: 1. Authorization header (Bearer token or plain token) 2. kbase_session cookie - 3. KB_SERVICE_AUTH_TOKEN from settings + 3. KB_SERVICE_AUTH_TOKEN from settings (LEGACY: for local testing only) Args: authorization: Authorization header value kbase_session: kbase_session cookie value + allow_anonymous: If True, returns empty string instead of raising 401 Returns: Authentication token string Raises: - HTTPException: If no token is found + HTTPException: If no token is found and allow_anonymous is False """ - # Try Authorization header first + # Priority 1: User-provided Authorization header if authorization: if authorization.startswith("Bearer "): return authorization[7:] return authorization - # Try kbase_session cookie + # Priority 2: User-provided kbase_session cookie if kbase_session: return kbase_session - # Fall back to service token from settings + # Priority 3 (LEGACY/TESTING ONLY): Fall back to service token from settings + # This is kept for local development and testing purposes. + # In production deployments, users MUST provide their own token. if settings.KB_SERVICE_AUTH_TOKEN: + logger.debug("Using KB_SERVICE_AUTH_TOKEN fallback (legacy/testing mode)") return settings.KB_SERVICE_AUTH_TOKEN + # No token found + if allow_anonymous: + return "" + raise HTTPException( status_code=401, - detail="Authorization required. Provide token via Authorization header, kbase_session cookie, or configure KB_SERVICE_AUTH_TOKEN." + detail="Authorization required. Provide your KBase token via the Authorization header or kbase_session cookie." ) -def get_cache_dir() -> Path: +def get_cache_dir() -> FilePath: """Get configured cache directory.""" - return Path(settings.CACHE_DIR) + return FilePath(settings.CACHE_DIR) # ============================================================================= @@ -155,7 +170,7 @@ async def health_check(): timestamp=datetime.utcnow().isoformat() + "Z", mode="cached_sqlite", data_dir=str(settings.CACHE_DIR), - config_dir=str(Path(settings.CACHE_DIR) / "configs"), + config_dir=str(FilePath(settings.CACHE_DIR) / "configs"), cache={ "databases_cached": cache_stats.get("total_connections", 0), "databases": cache_stats.get("connections", []) @@ -166,27 +181,141 @@ async def health_check(): raise HTTPException(status_code=500, detail=str(e)) + +# ============================================================================= +# FILE UPLOAD ENDPOINTS +# ============================================================================= + +@router.post( + "/upload", + tags=["File Upload"], + response_model=UploadDBResponse, + summary="Upload a local SQLite database", + description=""" + Upload a local SQLite database file (.db or .sqlite) for temporary use. + Returns a handle that can be used inplace of a KBase workspace reference. + + The handle format is `local:{uuid}`. + """ +) +async def upload_database( + file: UploadFile = File(..., description="SQLite database file") +): + try: + if not file.filename.endswith(('.db', '.sqlite', '.sqlite3')): + raise HTTPException(status_code=400, detail="File must be a SQLite database (.db, .sqlite, .sqlite3)") + + # Validate SQLite header + # SQLite files start with "SQLite format 3\0" + header = await file.read(16) + await file.seek(0) + + if header != b"SQLite format 3\0": + logger.warning(f"Invalid SQLite header for upload {file.filename}: {header}") + raise HTTPException(status_code=400, detail="Invalid SQLite file format (header mismatch)") + + # Generate handle + file_uuid = str(uuid4()) + handle = f"local:{file_uuid}" + + # Save to uploads directory + cache_dir = get_cache_dir() + upload_dir = cache_dir / "uploads" + upload_dir.mkdir(parents=True, exist_ok=True) + + destination = upload_dir / f"{file_uuid}.db" + + try: + with destination.open("wb") as buffer: + shutil.copyfileobj(file.file, buffer) + finally: + file.file.close() + + return UploadDBResponse( + handle=handle, + filename=file.filename, + size_bytes=destination.stat().st_size, + message="Database uploaded successfully" + ) + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error uploading file: {e}", exc_info=True) + raise HTTPException(status_code=500, detail=f"Upload failed: {str(e)}") + + # ============================================================================= # OBJECT-BASED ENDPOINTS (via KBase workspace object reference) # /object/{ws_ref}/tables - List tables from KBase object # /object/{ws_ref}/tables/{table}/data - Query data # ============================================================================= -@router.get("/object/{ws_ref:path}/tables", tags=["Object Access"], response_model=TableListResponse) +@router.get( + "/object/{ws_ref:path}/tables", + tags=["Object Access"], + response_model=TableListResponse, + summary="List tables in a BERDLTables object", + description=""" + List all tables available in a BERDLTables object from KBase workspace. + + **Example Usage:** + ```bash + # Using curl with Authorization header + curl -X GET \\ + "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables?kb_env=appdev" \\ + -H "Authorization: Bearer YOUR_KBASE_TOKEN" \\ + -H "accept: application/json" + + # Using curl with cookie + curl -X GET \\ + "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables?kb_env=appdev" \\ + -H "Cookie: kbase_session=YOUR_KBASE_TOKEN" \\ + -H "accept: application/json" + ``` + + **Authentication:** + - Authorization header: `Authorization: Bearer YOUR_TOKEN` or `Authorization: YOUR_TOKEN` + - Cookie: `kbase_session=YOUR_TOKEN` + - Environment variable: `KB_SERVICE_AUTH_TOKEN` (for service-to-service) + """, + responses={ + 200: { + "description": "Successfully retrieved table list", + "content": { + "application/json": { + "example": { + "berdl_table_id": "76990/7/2", + "object_type": "KBaseGeneDataLakes.BERDLTables-1.0", + "tables": [ + { + "name": "Genes", + "displayName": "Genes", + "row_count": 3356, + "column_count": 18 + }, + { + "name": "Contigs", + "displayName": "Contigs", + "row_count": 42, + "column_count": 12 + } + ] + } + } + } + }, + 401: {"description": "Authentication required"}, + 404: {"description": "Object not found"}, + 500: {"description": "Internal server error"} + } +) async def list_tables_by_object( - ws_ref: str, - kb_env: str = Query("appdev"), - authorization: str | None = Header(None), - kbase_session: str | None = Cookie(None) + ws_ref: str = Path(..., description="KBase workspace object reference (UPA format: workspace_id/object_id/version)", examples=["76990/7/2"]), + kb_env: str = Query("appdev", description="KBase environment", examples=["appdev"]), + authorization: str | None = Header(None, description="KBase authentication token (Bearer token or plain token)", examples=["Bearer YOUR_KBASE_TOKEN"]), + kbase_session: str | None = Cookie(None, description="KBase session cookie", examples=["YOUR_KBASE_TOKEN"]) ): - """ - List tables for a BERDLTables object. - - Authentication can be provided via: - - Authorization header (Bearer token or plain token) - - kbase_session cookie - - KB_SERVICE_AUTH_TOKEN environment variable (for service-to-service) - """ try: @@ -302,32 +431,54 @@ async def list_tables_by_object( raise HTTPException(status_code=500, detail=error_detail) -@router.get("/object/{ws_ref:path}/tables/{table_name}/data", tags=["Object Access"], response_model=TableDataResponse) +@router.get( + "/object/{ws_ref:path}/tables/{table_name}/data", + tags=["Object Access"], + response_model=TableDataResponse, + summary="Query table data from a BERDLTables object", + description=""" + Query data from a specific table in a BERDLTables object with filtering, sorting, and pagination. + + **Example Usage:** + ```bash + # Get first 10 rows from Genes table + curl -X GET \\ + "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables/Genes/data?limit=10&kb_env=appdev" \\ + -H "Authorization: Bearer YOUR_KBASE_TOKEN" \\ + -H "accept: application/json" + + # Search and sort + curl -X GET \\ + "https://appdev.kbase.us/services/berdl_table_scanner/object/76990/7/2/tables/Genes/data?limit=20&offset=0&search=kinase&sort_column=gene_name&sort_order=ASC&kb_env=appdev" \\ + -H "Authorization: Bearer YOUR_KBASE_TOKEN" \\ + -H "accept: application/json" + ``` + """, + responses={ + 200: {"description": "Successfully retrieved table data"}, + 401: {"description": "Authentication required"}, + 404: {"description": "Table not found"}, + 500: {"description": "Internal server error"} + } +) async def get_table_data_by_object( - ws_ref: str, - table_name: str, - limit: int = Query(DEFAULT_LIMIT, ge=1, le=MAX_LIMIT), - offset: int = Query(0, ge=0), - sort_column: str | None = Query(None), - sort_order: str | None = Query("ASC"), - search: str | None = Query(None), - kb_env: str = Query("appdev"), - authorization: str | None = Header(None), - kbase_session: str | None = Cookie(None) + ws_ref: str = Path(..., description="KBase workspace object reference (UPA format)", examples=["76990/7/2"]), + table_name: str = Path(..., description="Name of the table to query", examples=["Genes"]), + limit: int = Query(DEFAULT_LIMIT, ge=1, le=MAX_LIMIT, description="Maximum number of rows to return", examples=[10]), + offset: int = Query(0, ge=0, description="Number of rows to skip (for pagination)", examples=[0]), + sort_column: str | None = Query(None, description="Column name to sort by", examples=["gene_name"]), + sort_order: str | None = Query("ASC", description="Sort order: ASC or DESC", examples=["ASC"]), + search: str | None = Query(None, description="Global text search across all columns", examples=["kinase"]), + kb_env: str = Query("appdev", description="KBase environment", examples=["appdev"]), + authorization: str | None = Header(None, description="KBase authentication token", examples=["Bearer YOUR_KBASE_TOKEN"]), + kbase_session: str | None = Cookie(None, description="KBase session cookie", examples=["YOUR_KBASE_TOKEN"]) ): - """ - Query table data from a BERDLTables object. - - Authentication can be provided via: - - Authorization header (Bearer token or plain token) - - kbase_session cookie - - KB_SERVICE_AUTH_TOKEN environment variable (for service-to-service) - """ try: token = get_auth_token(authorization, kbase_session) cache_dir = get_cache_dir() berdl_table_id = ws_ref + # Get and validate DB access db_path = await get_object_db_path(berdl_table_id, token, kb_env, cache_dir) await ensure_table_accessible(db_path, table_name) @@ -346,13 +497,51 @@ async def get_table_data_by_object( return result except HTTPException: - # Re-raise HTTP exceptions as-is (don't convert to 500) raise except Exception as e: - # Log full traceback for debugging logger.error(f"Error querying data: {e}", exc_info=True) - # Provide detailed error message - # Always include the error message, add traceback in debug mode + error_detail = str(e) if str(e) else f"Error: {type(e).__name__}" + if settings.DEBUG: + error_detail += f"\n\nTraceback:\n{traceback.format_exc()}" + raise HTTPException(status_code=500, detail=error_detail) + + +@router.get( + "/object/{ws_ref:path}/tables/{table_name}/stats", + tags=["Object Access"], + response_model=TableStatisticsResponse, + summary="Get column statistics for a table", + description=""" + Calculate statistics for all columns in a table (null counts, distinct counts, min/max, samples). + This operation may be slow for large tables. + """ +) +async def get_table_stats( + ws_ref: str = Path(..., description="KBase workspace object reference (UPA format)", examples=["76990/7/2"]), + table_name: str = Path(..., description="Name of the table to analyze", examples=["Genes"]), + kb_env: str = Query("appdev", description="KBase environment", examples=["appdev"]), + authorization: str | None = Header(None, description="KBase authentication token", examples=["Bearer YOUR_KBASE_TOKEN"]), + kbase_session: str | None = Cookie(None, description="KBase session cookie", examples=["YOUR_KBASE_TOKEN"]) +): + try: + token = get_auth_token(authorization, kbase_session) + cache_dir = get_cache_dir() + berdl_table_id = ws_ref + + # Get and validate DB access + db_path = await get_object_db_path(berdl_table_id, token, kb_env, cache_dir) + await ensure_table_accessible(db_path, table_name) + + # Helper to run stats calculation in thread (CPU bound) + # from app.utils.sqlite import get_table_statistics + + stats = await run_sync_in_thread(get_table_statistics, db_path, table_name) + return stats + + except HTTPException: + raise + except Exception as e: + logger.error(f"Error calculating stats: {e}", exc_info=True) error_detail = str(e) if str(e) else f"Error: {type(e).__name__}" if settings.DEBUG: error_detail += f"\n\nTraceback:\n{traceback.format_exc()}" @@ -363,20 +552,57 @@ async def get_table_data_by_object( # DATA ACCESS ENDPOINTS # ============================================================================= -@router.post("/table-data", response_model=TableDataResponse, tags=["Data Access"]) +@router.post( + "/table-data", + response_model=TableDataResponse, + tags=["Data Access"], + summary="Query table data with advanced filtering (POST)", + description=""" + Query table data using a JSON request body. Recommended for complex queries with multiple filters. + + **Example Usage:** + ```bash + # Simple query + curl -X POST \\ + "https://appdev.kbase.us/services/berdl_table_scanner/table-data" \\ + -H "Authorization: Bearer YOUR_KBASE_TOKEN" \\ + -H "Content-Type: application/json" \\ + -d '{ + "berdl_table_id": "76990/7/2", + "table_name": "Genes", + "limit": 10, + "offset": 0 + }' + + # Query with filters + curl -X POST \\ + "https://appdev.kbase.us/services/berdl_table_scanner/table-data" \\ + -H "Authorization: Bearer YOUR_KBASE_TOKEN" \\ + -H "Content-Type: application/json" \\ + -d '{ + "berdl_table_id": "76990/7/2", + "table_name": "Genes", + "limit": 20, + "query_filters": [ + {"column": "gene_name", "operator": "like", "value": "kinase"}, + {"column": "contigs", "operator": "gt", "value": 5} + ], + "sort": [{"column": "gene_name", "direction": "asc"}] + }' + ``` + """, + responses={ + 200: {"description": "Successfully retrieved table data"}, + 401: {"description": "Authentication required"}, + 404: {"description": "Table not found"}, + 500: {"description": "Internal server error"} + } +) async def query_table_data( request: TableDataRequest, - authorization: str | None = Header(None), - kbase_session: str | None = Cookie(None) + authorization: str | None = Header(None, description="KBase authentication token", examples=["Bearer YOUR_KBASE_TOKEN"]), + kbase_session: str | None = Cookie(None, description="KBase session cookie", examples=["YOUR_KBASE_TOKEN"]) ): - """ - Query table data using a JSON body. Recommended for programmatic access. - - Authentication can be provided via: - - Authorization header (Bearer token or plain token) - - kbase_session cookie - - KB_SERVICE_AUTH_TOKEN environment variable (for service-to-service) - """ try: token = get_auth_token(authorization, kbase_session) cache_dir = get_cache_dir() @@ -384,12 +610,8 @@ async def query_table_data( filters = request.col_filter if request.col_filter else request.query_filters - try: - db_path = download_pangenome_db( - request.berdl_table_id, token, cache_dir, kb_env - ) - except ValueError as e: - raise HTTPException(status_code=404, detail=str(e)) + # Get and validate DB access (uses generic helper that supports local:) + db_path = await get_object_db_path(request.berdl_table_id, token, kb_env, cache_dir) if not validate_table_exists(db_path, request.table_name): available = list_tables(db_path) diff --git a/app/services/data/connection_pool.py b/app/services/data/connection_pool.py index 76a283a..42c6c3c 100644 --- a/app/services/data/connection_pool.py +++ b/app/services/data/connection_pool.py @@ -33,14 +33,14 @@ class ConnectionPool: - Automatic cleanup of idle pools. """ - # Connection timeout: 30 minutes of inactivity - POOL_TIMEOUT_SECONDS = 30 * 60 + # Connection timeout: 10 minutes of inactivity (reduced for local DBs) + POOL_TIMEOUT_SECONDS = 10 * 60 # Clean up interval - CLEANUP_INTERVAL_SECONDS = 5 * 60 + CLEANUP_INTERVAL_SECONDS = 2 * 60 # Maximum connections per database file - MAX_CONNECTIONS = 5 + MAX_CONNECTIONS = 8 def __init__(self) -> None: """Initialize the connection pool.""" diff --git a/app/services/db_helper.py b/app/services/db_helper.py index f514889..7457ef2 100644 --- a/app/services/db_helper.py +++ b/app/services/db_helper.py @@ -12,6 +12,7 @@ from app.utils.workspace import KBaseClient, download_pangenome_db from app.utils.sqlite import validate_table_exists, list_tables from app.utils.async_utils import run_sync_in_thread +from app.utils.cache import sanitize_id logger = logging.getLogger(__name__) @@ -69,7 +70,7 @@ async def get_object_db_path( Get (and download if needed) a SQLite database from a BERDL object. Args: - berdl_table_id: KBase workspace reference + berdl_table_id: KBase workspace reference OR 'local:{uuid}' for uploaded files token: KBase auth token kb_env: KBase environment cache_dir: Cache directory path @@ -77,6 +78,29 @@ async def get_object_db_path( Returns: Path to the local SQLite database file """ + # Handle local uploads + if berdl_table_id.startswith("local:"): + # Expect format local:UUID + handle_parts = berdl_table_id.split(":", 1) + if len(handle_parts) != 2: + raise HTTPException(status_code=400, detail="Invalid local database handle format") + + filename = getattr(sanitize_id, 'original', sanitize_id)(handle_parts[1]) + # Note: sanitize_id ensures only alphanumeric+._- chars + + # Double check against the original to ensure no unexpected chars werestripped silently that might imply malicious intent? + # Actually sanitize_id already does a good job. But let's be strict. + if filename != handle_parts[1]: + # If sanitize changed it, it had bad chars + raise HTTPException(status_code=400, detail="Invalid characters in local database handle") + + db_path = cache_dir / "uploads" / f"{filename}.db" + + if not db_path.exists(): + raise HTTPException(status_code=404, detail=f"Local database not found: {berdl_table_id}") + + return db_path + try: # download_pangenome_db already handles caching logic return await run_sync_in_thread( diff --git a/app/utils/cache.py b/app/utils/cache.py index d0e88c5..9113807 100644 --- a/app/utils/cache.py +++ b/app/utils/cache.py @@ -243,11 +243,11 @@ def clear_cache(cache_dir: Path, berdl_table_id: str | None = None) -> dict[str, def cleanup_old_caches(cache_dir: Path, max_age_days: int = 7) -> dict[str, Any]: """ - Remove cache directories older than max_age_days. + Remove cache directories older than max_age_days, AND uploads older than 1 hour. Args: cache_dir: Base cache directory - max_age_days: Maximum age in days + max_age_days: Maximum age in days for standard caches Returns: Summary of cleanup operation @@ -259,8 +259,9 @@ def cleanup_old_caches(cache_dir: Path, max_age_days: int = 7) -> dict[str, Any] max_age_seconds = max_age_days * 24 * 3600 removed = [] + # Clean standard ID-based subdirectories for subdir in cache_dir.iterdir(): - if not subdir.is_dir(): + if not subdir.is_dir() or subdir.name == "uploads": continue try: @@ -271,10 +272,26 @@ def cleanup_old_caches(cache_dir: Path, max_age_days: int = 7) -> dict[str, Any] logger.info(f"Removed old cache: {subdir.name}") except Exception as e: logger.warning(f"Failed to clean {subdir}: {e}") + + # Clean uploads directory (aggressive 1 hour expiry for temp availability) + uploads_dir = cache_dir / "uploads" + uploads_removed = 0 + if uploads_dir.exists(): + upload_max_age = 3600 # 1 hour + for f in uploads_dir.glob("*.db"): + try: + mtime = f.stat().st_mtime + if now - mtime > upload_max_age: + f.unlink() + uploads_removed += 1 + logger.debug(f"Removed expired upload: {f.name}") + except Exception as e: + logger.warning(f"Failed to clean upload {f}: {e}") return { "status": "success", "removed": len(removed), + "uploads_removed": uploads_removed, "items": removed } diff --git a/app/utils/sqlite.py b/app/utils/sqlite.py index 496d844..041e6b2 100644 --- a/app/utils/sqlite.py +++ b/app/utils/sqlite.py @@ -4,6 +4,7 @@ from __future__ import annotations import sqlite3 import logging +import time from pathlib import Path # Configure module logger @@ -97,6 +98,7 @@ def get_table_row_count(db_path: Path, table_name: str) -> int: raise + def validate_table_exists(db_path: Path, table_name: str) -> bool: """ Check if a table exists in the database. @@ -106,3 +108,122 @@ def validate_table_exists(db_path: Path, table_name: str) -> bool: return table_name in tables except Exception: return False + + +def get_table_statistics(db_path: Path, table_name: str) -> dict: + """ + Calculate statistics for all columns in a table. + """ + try: + conn = sqlite3.connect(str(db_path)) + # Use row factory to access columns by name if needed, though we use indices here + conn.row_factory = sqlite3.Row + cursor = conn.cursor() + + # Validate table + cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name=?", (table_name,)) + if not cursor.fetchone(): + raise ValueError(f"Invalid table name: {table_name}") + + validated_table = table_name + + # Get total row count + cursor.execute(f"SELECT COUNT(*) FROM \"{validated_table}\"") + row_count = cursor.fetchone()[0] + + # Get columns and types + cursor.execute(f"PRAGMA table_info(\"{validated_table}\")") + columns_info = cursor.fetchall() + + stats_columns = [] + + for col in columns_info: + col_name = col['name'] + col_type = col['type'] + + # Base stats query + # We use SUM(CASE WHEN ... IS NULL) instead of COUNT(col) logic sometimes to be explicit + # but COUNT(col) counts non-nulls. So Nulls = Total - COUNT(col). + cursor.execute(f""" + SELECT + COUNT("{col_name}") as non_null_count, + COUNT(DISTINCT "{col_name}") as distinct_count + FROM "{validated_table}" + """) + basic_stats = cursor.fetchone() + non_null_count = basic_stats['non_null_count'] + null_count = row_count - non_null_count + distinct_count = basic_stats['distinct_count'] + + col_stats = { + "column": col_name, + "type": col_type, + "null_count": null_count, + "distinct_count": distinct_count, + "sample_values": [] + } + + # Extended stats for numeric types + # Heuristic: simplistic check for INT, REAL, FLO, DOUB, NUM + is_numeric = any(t in col_type.upper() for t in ['INT', 'REAL', 'FLO', 'DOUB', 'NUM', 'DEC']) + + if is_numeric and non_null_count > 0: + try: + cursor.execute(f""" + SELECT + MIN("{col_name}"), + MAX("{col_name}"), + AVG("{col_name}") + FROM "{validated_table}" + WHERE "{col_name}" IS NOT NULL + """) + num_stats = cursor.fetchone() + if num_stats[0] is not None: + col_stats["min"] = num_stats[0] + col_stats["max"] = num_stats[1] + col_stats["mean"] = num_stats[2] + except Exception: + # Ignore errors in numeric aggregate (e.g. if column declared int but has strings) + pass + elif non_null_count > 0: + # For non-numeric, just get Min/Max + try: + cursor.execute(f""" + SELECT MIN("{col_name}"), MAX("{col_name}") + FROM "{validated_table}" + WHERE "{col_name}" IS NOT NULL + """) + str_stats = cursor.fetchone() + if str_stats[0] is not None: + col_stats["min"] = str_stats[0] + col_stats["max"] = str_stats[1] + except Exception: + pass + + # Get sample values (first 5 non-null distinct preferred, or just first 5) + try: + cursor.execute(f""" + SELECT DISTINCT "{col_name}" + FROM "{validated_table}" + WHERE "{col_name}" IS NOT NULL + LIMIT 5 + """) + samples = [row[0] for row in cursor.fetchall()] + col_stats["sample_values"] = samples + except Exception: + col_stats["sample_values"] = [] + + stats_columns.append(col_stats) + + conn.close() + + return { + "table": table_name, + "row_count": row_count, + "columns": stats_columns, + "last_updated": int(time.time() * 1000) + } + + except sqlite3.Error as e: + logger.error(f"Error calculating stats for {table_name}: {e}") + raise diff --git a/app/utils/workspace.py b/app/utils/workspace.py index 8e85162..5cc4783 100644 --- a/app/utils/workspace.py +++ b/app/utils/workspace.py @@ -433,6 +433,9 @@ def get_object_type( Returns: Object type string (e.g., "KBaseGeneDataLakes.BERDLTables-1.0") """ + if berdl_table_id.startswith("local:"): + return "LocalDatabase" + client = KBaseClient(auth_token, kb_env) return client.get_object_type_only(berdl_table_id) diff --git a/docs/API.md b/docs/API.md index 6171a78..3763bb1 100644 --- a/docs/API.md +++ b/docs/API.md @@ -7,9 +7,13 @@ The **TableScanner** service provides read-only access to SQLite databases store - **Production**: `https://kbase.us/services/berdl_table_scanner` (or similar) ## Authentication -All endpoints require a KBase authentication token. + +**Each user must provide their own KBase authentication token.** The service does not use a shared/service-level token for production access. + - **Header (recommended)**: `Authorization: ` or `Authorization: Bearer ` -- **Cookie**: `kbase_session=` (useful for browser-based clients) +- **Cookie**: `kbase_session=` (useful for browser-based clients like DataTables Viewer) + +> **Note for Developers**: The `KB_SERVICE_AUTH_TOKEN` environment variable is available as a legacy fallback for local testing only. It should NOT be relied upon in production. --- @@ -58,6 +62,11 @@ curl -X GET \ -H "Authorization: Bearer $KB_TOKEN" ``` +### `GET /object/{ws_ref}/tables/{table_name}/stats` +Get detailed statistics for all columns in a table. +- **Response**: Column statistics including null counts, distinct counts, min/max/mean, and samples. + + --- ## 3. Data Access @@ -77,3 +86,34 @@ Complex query endpoint supporting advanced filtering. } ``` - **Supported Operators**: `eq`, `ne`, `gt`, `gte`, `lt`, `lte`, `like`, `ilike`, `in`, `not_in`, `between`, `is_null`, `is_not_null`. + +--- + +## 4. Local Database Upload + +### `POST /upload` +Upload a temporary SQLite database file to the server. Useful for testing or serving local files. + +- **Request**: Multipart form data with key `file` +- **Response**: + ```json + { + "handle": "local:uuid-string", + "filename": "my_db.db", + "size_bytes": 10240, + "message": "Database uploaded successfully" + } + ``` + +### Usage Workflow +1. **Upload File**: + ```bash + curl -X POST "http://localhost:8000/upload" \ + -H "accept: application/json" \ + -H "Content-Type: multipart/form-data" \ + -F "file=@/path/to/test.db" + ``` +2. **Use Handle**: The returned `handle` (e.g., `local:abc-123`) can be used as the `berdl_table_id` or `ws_ref` in any other endpoint. + - List tables: `GET /object/local:abc-123/tables` + - Query data: `POST /table-data` with `"berdl_table_id": "local:abc-123"` + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..7183730 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,11 @@ +# TableScanner Requirements +# Install with: pip install -r requirements.txt + +fastapi[standard]>=0.124.4 +uvicorn>=0.38.0 +pydantic>=2.11.0 +pydantic-settings>=2.0.0 +requests>=2.31.0 +python-multipart>=0.0.20 +tqdm>=4.64.0 +minio>=7.2.20 diff --git a/tests/integration/test_local_upload.py b/tests/integration/test_local_upload.py new file mode 100644 index 0000000..df4b139 --- /dev/null +++ b/tests/integration/test_local_upload.py @@ -0,0 +1,154 @@ +import os +import pytest +import sqlite3 +import tempfile +from pathlib import Path +from fastapi.testclient import TestClient +from app.main import app +from app.config import settings + +client = TestClient(app) + +@pytest.fixture +def dummy_sqlite_db(): + """Create a temporary SQLite database with some data.""" + with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as tmp: + conn = sqlite3.connect(tmp.name) + cursor = conn.cursor() + cursor.execute("CREATE TABLE TestTable (id INTEGER PRIMARY KEY, name TEXT)") + cursor.execute("INSERT INTO TestTable (name) VALUES ('Alpha')") + cursor.execute("INSERT INTO TestTable (name) VALUES ('Beta')") + conn.commit() + conn.close() + tmp_path = Path(tmp.name) + yield tmp_path + # Cleanup + if tmp_path.exists(): + tmp_path.unlink() + +def test_upload_and_query_flow(dummy_sqlite_db): + """ + Test the full flow: + 1. Upload DB -> Get handle + 2. List tables -> Success + 3. Query data -> Success + """ + # 1. Upload + with open(dummy_sqlite_db, "rb") as f: + response = client.post( + "/upload", + files={"file": ("my_test.db", f, "application/vnd.sqlite3")} + ) + + assert response.status_code == 200 + data = response.json() + assert "handle" in data + assert data["handle"].startswith("local:") + assert data["message"] == "Database uploaded successfully" + + handle = data["handle"] + + # 2. List Tables + # Need to mock the KBase ID check or auth if implied, BUT local handles bypass KBase download. + # The endpoint /object/{ref}/tables takes the ref. + # Note: Authorization header might still be checked by get_auth_token. + # We provide a dummy token to pass the check. + headers = {"Authorization": "Bearer dummy_token"} + + # We must patch get_object_type or it might try to call KBase for 'local:...' which is not a valid UPA. + # Let's check routes.py: list_tables_by_object calls get_object_type logic. + # Wait, routes.py:325 handles object_type by calling get_object_type. + # get_object_type might fail for local handle. I need to make sure get_object_type handles it gracefully or mock it. + + # Actually, in routes.py, I should update get_object_type logic OR just let it fail non-critically? + # routes.py:301 catches Exception and sets object_type = None. That's fine. + + response = client.get(f"/object/{handle}/tables", headers=headers) + assert response.status_code == 200, response.text + tables_data = response.json() + + assert tables_data["object_type"] == "LocalDatabase" or tables_data["object_type"] is None + names = [t["name"] for t in tables_data["tables"]] + assert "TestTable" in names + + # 3. Query Data + query_payload = { + "berdl_table_id": handle, + "table_name": "TestTable", + "limit": 10 + } + response = client.post("/table-data", json=query_payload, headers=headers) + assert response.status_code == 200 + query_data = response.json() + assert len(query_data["data"]) == 2 + assert query_data["data"][0][1] == "Alpha" + +def test_upload_security_traversal(): + """Test that we can't directory traverse with a crafted handle.""" + headers = {"Authorization": "Bearer dummy_token"} + + # Try to access a file outside uploads via path traversal + # get_object_db_path has a check for ".." + + # We'll try to use a handle that looks like traversal + bad_handle = "local:../../../../etc/passwd" + + response = client.get(f"/object/{bad_handle}/tables", headers=headers) + # converting slash to %2F might happen in client depending on how it's passed, + # but the routes.py extracts it. + # The check in db_helper.py should catch it. + + # FastAPI path parameter handling might encode it, but we can try injecting it. + # Since {ws_ref:path} captures slashes, we can test: + response = client.get("/object/local:..%2F..%2Fetc%2Fpasswd/tables", headers=headers) + + # Should get 400 or 500, but definitely not success. + # Our db_helper validation raises 400. + assert response.status_code in (400, 404, 500) + +def test_upload_invalid_file_format(): + """Test that uploading a non-SQLite file is rejected.""" + # Create a dummy text file + with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as tmp: + tmp.write(b"This is not a SQLite database") + tmp_path = Path(tmp.name) + + try: + with open(tmp_path, "rb") as f: + response = client.post( + "/upload", + files={"file": ("fake.db", f, "application/vnd.sqlite3")} + ) + + # Should be rejected due to header mismatch + assert response.status_code == 400 + assert "Invalid SQLite file format" in response.json()["detail"] + + finally: + if tmp_path.exists(): + tmp_path.unlink() + +def test_upload_and_get_stats(dummy_sqlite_db): + """Test getting statistics for an uploaded table.""" + # 1. Upload + with open(dummy_sqlite_db, "rb") as f: + response = client.post( + "/upload", + files={"file": ("stats_test.db", f, "application/vnd.sqlite3")} + ) + handle = response.json()["handle"] + + # 2. Get Stats + headers = {"Authorization": "Bearer dummy_token"} + response = client.get(f"/object/{handle}/tables/TestTable/stats", headers=headers) + + assert response.status_code == 200 + stats = response.json() + assert stats["table"] == "TestTable" + assert stats["row_count"] == 2 + + # Check column stats + cols = {c["column"]: c for c in stats["columns"]} + assert "name" in cols + assert cols["name"]["distinct_count"] == 2 + assert "Alpha" in cols["name"]["sample_values"] From b00f966dadc608ce6a33b29210f6211c25e1c3b1 Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Thu, 22 Jan 2026 09:10:37 -0600 Subject: [PATCH 18/19] docker fixes --- Dockerfile | 2 +- docker-compose.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/Dockerfile b/Dockerfile index 72637a9..87ac643 100644 --- a/Dockerfile +++ b/Dockerfile @@ -18,4 +18,4 @@ COPY pyproject.toml /app/pyproject.toml RUN uv sync EXPOSE 8000 -CMD ["uv", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] +CMD ["uv", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] \ No newline at end of file diff --git a/docker-compose.yml b/docker-compose.yml index a7db56a..b6c0a75 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -20,4 +20,4 @@ services: interval: 30s timeout: 10s retries: 3 - start_period: 40s + start_period: 40s \ No newline at end of file From c84d66724b7039556cbfe588a27091f8b0cc9a8b Mon Sep 17 00:00:00 2001 From: VibhavSetlur Date: Thu, 22 Jan 2026 10:50:24 -0600 Subject: [PATCH 19/19] optimize response --- .gitignore | 1 + README.md | 4 ++ app/main.py | 6 ++ app/routes.py | 142 ++++++++++++++++++++++++++----------- app/utils/cache.py | 19 +++++ app/utils/request_utils.py | 3 + docs/API.md | 4 ++ docs/ARCHITECTURE.md | 3 + requirements.txt | 1 + 9 files changed, 142 insertions(+), 41 deletions(-) diff --git a/.gitignore b/.gitignore index 6e4db2d..e86f279 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,7 @@ DATABASE_SCHEMA.md docs/personal/ archive/ docs/archive +dummy.db .DS_Store .idea diff --git a/README.md b/README.md index 882b512..bd4697f 100644 --- a/README.md +++ b/README.md @@ -85,6 +85,10 @@ curl -X POST -H "Authorization: Bearer $KB_TOKEN" \ ## Performance & Optimization +- **Gzip Compression**: Compresses large responses (>1KB) to reduce bandwidth usage. +- **High-Performance JSON**: Uses `orjson` for fast JSON serialization. +- **Parallel Metadata Fetching**: Retrieves table metadata concurrently for fast listing. +- **Metadata Caching**: Caches object types locally to minimize KBase API calls. - **Connection Pooling**: Reuses database connections for up to 10 minutes of inactivity. - **Automatic Cleanup**: Expired caches are purged on startup. Uploaded databases automatically expire after **1 hour**. - **Query Caching**: 5-minute TTL, max 1000 entries per instance. diff --git a/app/main.py b/app/main.py index f4ffd83..d7a84cd 100644 --- a/app/main.py +++ b/app/main.py @@ -13,7 +13,9 @@ from fastapi.responses import JSONResponse from fastapi.staticfiles import StaticFiles from fastapi.middleware.cors import CORSMiddleware +from fastapi.middleware.gzip import GZipMiddleware from fastapi.security import HTTPBearer, APIKeyCookie +from fastapi.responses import JSONResponse, ORJSONResponse from app.routes import router from app.config import settings @@ -136,8 +138,12 @@ def create_app() -> FastAPI: docs_url="/docs", redoc_url="/redoc", lifespan=lifespan, + default_response_class=ORJSONResponse ) + # Enable Gzip compression for responses > 1KB + app.add_middleware(GZipMiddleware, minimum_size=1000) + # Add CORS middleware to allow cross-origin requests # Update CORS middleware to allow requests from the frontend app.add_middleware( diff --git a/app/routes.py b/app/routes.py index 290ae75..ad9440e 100644 --- a/app/routes.py +++ b/app/routes.py @@ -21,6 +21,7 @@ import shutil from uuid import uuid4 from fastapi import APIRouter, HTTPException, Header, Query, Cookie, Path, UploadFile, File +from app.exceptions import InvalidFilterError from app.models import ( TableDataRequest, @@ -61,6 +62,8 @@ from app.utils.request_utils import TableRequestProcessor from app.config import settings from app.config_constants import MAX_LIMIT, DEFAULT_LIMIT +from app.utils.cache import load_cache_metadata, save_cache_metadata + # Configure module logger logger = logging.getLogger(__name__) @@ -127,6 +130,38 @@ def get_auth_token( ) + +async def _get_table_metadata(db_path, name, schema_service): + """ + Helper to fetch metadata for a single table. + """ + try: + # Run lightweight checks in thread + columns = await run_sync_in_thread(get_table_columns, db_path, name) + row_count = await run_sync_in_thread(get_table_row_count, db_path, name) + + display_name = name.replace("_", " ").title() + + # Build schema map + try: + table_schema = await run_sync_in_thread( + schema_service.get_table_schema, db_path, name + ) + schema_map = {col["name"]: col["type"] for col in table_schema["columns"]} + except Exception: + schema_map = {col: "TEXT" for col in columns} + + return { + "name": name, + "displayName": display_name, + "row_count": row_count, + "column_count": len(columns), + "schema": schema_map + } + except Exception: + logger.warning("Error getting table info for %s", name, exc_info=True) + return {"name": name, "displayName": name, "error_fallback": True} + def get_cache_dir() -> FilePath: """Get configured cache directory.""" return FilePath(settings.CACHE_DIR) @@ -337,49 +372,71 @@ async def list_tables_by_object( schema_service = get_schema_service() # Process tables - for name in table_names: - try: - # Run lightweight checks in thread - columns = await run_sync_in_thread(get_table_columns, db_path, name) - row_count = await run_sync_in_thread(get_table_row_count, db_path, name) - - # Get display name (use table name as default) - display_name = name.replace("_", " ").title() + # Parallelize metadata fetching + tasks = [ + _get_table_metadata(db_path, name, schema_service) + for name in table_names + ] + + results = await asyncio.gather(*tasks) + + for res in results: + if "error_fallback" in res: + tables.append({"name": res["name"], "displayName": res["displayName"]}) + continue - tables.append({ - "name": name, - "displayName": display_name, - "row_count": row_count, - "column_count": len(columns) - }) - total_rows += row_count or 0 - - # Build schema map with actual types - try: - table_schema = await run_sync_in_thread( - schema_service.get_table_schema, db_path, name - ) - schemas[name] = { - col["name"]: col["type"] - for col in table_schema["columns"] - } - except Exception: - # Fallback to default type - schemas[name] = {col: "TEXT" for col in columns} - except Exception: - logger.warning("Error getting table info for %s", name, exc_info=True) - tables.append({"name": name, "displayName": name}) - - # Get object type (non-blocking) + tables.append({ + "name": res["name"], + "displayName": res["displayName"], + "row_count": res["row_count"], + "column_count": res["column_count"] + }) + total_rows += res["row_count"] or 0 + schemas[res["name"]] = res["schema"] + + # Get object type (with caching) + object_type = None + + # 1. Try to load from cache try: - # Use specific timeout for API call - object_type = await asyncio.wait_for( - run_sync_in_thread(get_object_type, berdl_table_id, token, kb_env), - timeout=settings.KBASE_API_TIMEOUT_SECONDS - ) - except (asyncio.TimeoutError, Exception) as e: - logger.warning(f"Could not get object type (non-critical): {e}") - object_type = None + # db_path is typically .../cache/sanitized_upa/tables.db + # So cache_subdir is the parent directory + cache_subdir = db_path.parent + metadata = load_cache_metadata(cache_subdir) + + if metadata and "object_type" in metadata: + object_type = metadata["object_type"] + logger.debug(f"Using cached object type for {berdl_table_id}: {object_type}") + except Exception as e: + logger.warning(f"Error reading cache metadata: {e}") + + # 2. If not cached, fetch from API + if not object_type: + try: + # Use specific timeout for API call + object_type = await asyncio.wait_for( + run_sync_in_thread(get_object_type, berdl_table_id, token, kb_env), + timeout=settings.KBASE_API_TIMEOUT_SECONDS + ) + + # 3. Save to cache + if object_type: + try: + save_cache_metadata( + db_path.parent, + { + "berdl_table_id": berdl_table_id, + "object_type": object_type, + "last_checked": datetime.utcnow().isoformat() + } + ) + logger.info(f"Cached object type for {berdl_table_id}") + except Exception as e: + logger.warning(f"Failed to cache metadata: {e}") + + except (asyncio.TimeoutError, Exception) as e: + logger.warning(f"Could not get object type (non-critical): {e}") + object_type = None # Config-related fields (deprecated, kept for backward compatibility) config_fingerprint = None @@ -645,6 +702,9 @@ async def query_table_data( except HTTPException: # Re-raise HTTP exceptions as-is (don't convert to 500) raise + except InvalidFilterError: + # Allow invalid filter errors to be handled by main app exception handler (422) + raise except Exception as e: # Log full traceback for debugging logger.error(f"Error querying data: {e}", exc_info=True) diff --git a/app/utils/cache.py b/app/utils/cache.py index 9113807..b36e097 100644 --- a/app/utils/cache.py +++ b/app/utils/cache.py @@ -173,6 +173,25 @@ def get_cache_info(cache_path: Path) -> dict[str, Any] | None: + +def save_cache_metadata(cache_subdir: Path, metadata: dict[str, Any]) -> None: + """ + Save metadata to cache directory. + + Args: + cache_subdir: Cache subdirectory + metadata: Metadata dictionary to save + """ + metadata_path = get_metadata_path(cache_subdir) + try: + ensure_cache_dir(metadata_path) + with open(metadata_path, 'w') as f: + json.dump(metadata, f, indent=2) + logger.debug(f"Saved metadata to {metadata_path}") + except Exception as e: + logger.warning(f"Failed to save metadata to {metadata_path}: {e}") + + def load_cache_metadata(cache_subdir: Path) -> dict[str, Any] | None: """ Load cache metadata. diff --git a/app/utils/request_utils.py b/app/utils/request_utils.py index ef98f64..628558c 100644 --- a/app/utils/request_utils.py +++ b/app/utils/request_utils.py @@ -129,6 +129,9 @@ def _execute(): except (TableNotFoundError, InvalidFilterError): # Allow specific exceptions to bubble up to global handlers raise + except ValueError as e: + # Handle validation errors (e.g. invalid numeric conversion) from QueryService + raise HTTPException(status_code=422, detail=str(e)) except Exception as e: logger.error(f"Query execution failed: {e}") raise HTTPException(status_code=500, detail=str(e)) diff --git a/docs/API.md b/docs/API.md index 3763bb1..8d64c75 100644 --- a/docs/API.md +++ b/docs/API.md @@ -15,6 +15,10 @@ The **TableScanner** service provides read-only access to SQLite databases store > **Note for Developers**: The `KB_SERVICE_AUTH_TOKEN` environment variable is available as a legacy fallback for local testing only. It should NOT be relied upon in production. +--- +## Performance +- **Gzip Support**: Responses >1KB are automatically compressed if the `Accept-Encoding: gzip` header is present. +- **Fast JSON**: All responses use optimized JSON serialization. --- ## 1. Service Status diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md index 80cc143..3919872 100644 --- a/docs/ARCHITECTURE.md +++ b/docs/ARCHITECTURE.md @@ -67,6 +67,9 @@ Manages SQLite database connections efficiently: - **Read-Only**: The service never modifies the source SQLite files. This simplifies concurrency control (WAL mode). - **Synchronous I/O in Async App**: We use `run_sync_in_thread` to offload blocking SQLite operations to a thread pool, keeping the FastAPI event loop responsive. - **Local Caching**: We aggressively cache database files locally to avoid the high latency of downloading multi-GB files from KBase for every request. +- **Metadata Caching**: Object types are cached locally to minimize redundant KBase API calls. +- **Concurrency**: Table listing uses parallel metadata fetching (`asyncio.gather`) to resolve "N+1" query issues. +- **Compression & High-Performance serialization**: Production-ready configuration uses Gzip and ORJSON for maximum throughput. ## Security - **Authentication**: All data access endpoints require a valid KBase Auth Token (`Authorization` header). diff --git a/requirements.txt b/requirements.txt index 7183730..d444106 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,3 +9,4 @@ requests>=2.31.0 python-multipart>=0.0.20 tqdm>=4.64.0 minio>=7.2.20 +orjson>=3.9.10