diff --git a/README.md b/README.md index 8c38f6c..13c88b3 100644 --- a/README.md +++ b/README.md @@ -103,24 +103,23 @@ Organizing the data as a knowledge graph allows a chatbot to access accurate, fa ### Quick Start #### Use TigerGraph Docker-Based Instance -Set your LLM Provider (supported `openai` or `gemini`) api key as environment varabiel LLM_API_KEY and use the following command for a one-step quick deployment with TigerGraph Community Edition and default configurations: +Set your OpenAI api key as environment varabiel OPENAI_API_KEY and use the following command for a one-step quick deployment with TigerGraph Community Edition and default configurations: ``` -curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag.sh | bash +curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag.sh | sh ``` The GraphRAG instances will be deployed at `./graphrag` folder and TigerGraph instance will be available at `http://localhost:14240`. -To change installation folder, use `bash -s -- ` instead of `bash` at the end of the above command. - -> Note: for other LLM providers, manually update `configs/server_config.json` accordingly and re-run `docker compose up -d` +To change installation folder, use `sh -s -- ` instead of `sh` at the end of the above command. #### Use Pre-Installed TigerGraph Instance -Similar to the above setup, and use the following command for a one-step quick deployment connecting to a pre-installed TigerGraph with default configurations: + +Using the following command for a one-step quick deployment with TigerGraph Community Edition and default configurations: ``` -curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag_tg.sh | bash +curl -k https://raw.githubusercontent.com/tigergraph/graphrag/refs/heads/main/docs/tutorials/setup_graphrag_tg.sh | sh ``` The GraphRAG instances will be deployed at `./graphrag` folder and connect to TigerGraph instance at `http://localhost:14240` by default. -To change installation folder, TigerGraph instance location or username/password, use `bash -s -- ` instead of `bash` at the end of the above command. +To change installation folder, TigerGraph instance location or username/password, use `sh -s -- ` instead of `sh` at the end of the above command. [Go back to top](#top) @@ -152,7 +151,7 @@ Here’s what the folder structure looks like: ##### Step 3: Adjust configurations -Edit `llm_config` section of `configs/server_config.json` and replace `` to your own LLM_API_KEY for the LLM provider. +Edit `llm_config` section of `configs/server_config.json` and replace `` to your own OPENAI_API_KEY. > If desired, you can also change the model to be used for the embedding service and completion service to your preferred models to adjust the output from the LLM service. @@ -470,23 +469,27 @@ In addition to the `OPENAI_API_KEY`, `llm_model` and `model_name` can be edited ```json { "llm_config": { + "authentication_configuration": { + "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE" + }, "embedding_service": { - "embedding_model_service": "openai", "model_name": "text-embedding-3-small", - "authentication_configuration": { - "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE" - } + "embedding_model_service": "openai" }, "completion_service": { "llm_service": "openai", "llm_model": "gpt-4.1-mini", - "authentication_configuration": { - "OPENAI_API_KEY": "YOUR_OPENAI_API_KEY_HERE" - }, "model_kwargs": { "temperature": 0 }, "prompt_path": "./common/prompts/openai_gpt4/" + }, + "multimodal_service": { + "llm_service": "openai", + "llm_model": "gpt-4o-mini", + "model_kwargs": { + "temperature": 0 + } } } } @@ -546,7 +549,7 @@ And your JSON config should follow as: "model_kwargs": { "temperature": 0 }, - "prompt_path": "./common/prompts/gcp_vertexai_palm/" + "prompt_path": "./app/prompts/gcp_vertexai_palm/" } } } @@ -583,7 +586,7 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d "model_kwargs": { "temperature": 0 }, - "prompt_path": "./common/prompts/azure_open_ai_gpt35_turbo_instruct/" + "prompt_path": "./app/prompts/azure_open_ai_gpt35_turbo_instruct/" } } } @@ -594,27 +597,32 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d ```json { "llm_config": { + "authentication_configuration": { + "AWS_ACCESS_KEY_ID": "YOUR_AWS_ACCESS_KEY", + "AWS_SECRET_ACCESS_KEY": "YOUR_AWS_SECRET_KEY", + "AWS_REGION_NAME": "us-west-2" + }, "embedding_service": { + "model_name": "amazon.titan-embed-text-v1", "embedding_model_service": "bedrock", - "model_name":"amazon.titan-embed-text-v2", - "region_name":"us-west-2", - "authentication_configuration": { - "AWS_ACCESS_KEY_ID": "ACCESS_KEY", - "AWS_SECRET_ACCESS_KEY": "SECRET" - } + "dimensions": 1536 }, "completion_service": { "llm_service": "bedrock", - "llm_model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0", - "region_name":"us-west-2", - "authentication_configuration": { - "AWS_ACCESS_KEY_ID": "ACCESS_KEY", - "AWS_SECRET_ACCESS_KEY": "SECRET" - }, + "llm_model": "anthropic.claude-3-5-sonnet-20240620-v1:0", "model_kwargs": { "temperature": 0, + "max_tokens": 4096 }, - "prompt_path": "./common/prompts/aws_bedrock_claude3haiku/" + "prompt_path": "./common/prompts/openai_gpt4/" + }, + "multimodal_service": { + "llm_service": "bedrock", + "llm_model": "anthropic.claude-3-5-sonnet-20240620-v1:0", + "model_kwargs": { + "temperature": 0, + "max_tokens": 4096 + } } } } @@ -640,7 +648,7 @@ In addition to the `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, and `azure_d "model_kwargs": { "temperature": 0.0000001 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/openai_gpt4/" } } } @@ -670,7 +678,7 @@ Example configuration for a model on Hugging Face with a dedicated endpoint is s "model_kwargs": { "temperature": 0.1 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/openai_gpt4/" } } } @@ -697,7 +705,7 @@ Example configuration for a model on Hugging Face with a serverless endpoint is "model_kwargs": { "temperature": 0.1 }, - "prompt_path": "./common/prompts/llama_70b/" + "prompt_path": "./app/prompts/llama_70b/" } } } @@ -724,7 +732,7 @@ Example configuration for a model on Hugging Face with a serverless endpoint is "model_kwargs": { "temperature": 0.1 }, - "prompt_path": "./common/prompts/openai_gpt4/" + "prompt_path": "./app/prompts/openai_gpt4/" } } } diff --git a/common/requirements.txt b/common/requirements.txt index 562c2f6..3bbd096 100644 --- a/common/requirements.txt +++ b/common/requirements.txt @@ -108,9 +108,10 @@ ordered-set==4.1.0 orjson==3.10.18 packaging==24.2 pandas==2.2.3 -#pathtools==0.1.2 +pathtools==0.1.2 pillow==11.2.1 -PyMuPDF==1.26.4 +#PyMuPDF==1.26.4 +pymupdf4llm==0.2.0 platformdirs==4.3.8 pluggy==1.6.0 prometheus_client==0.22.1 diff --git a/common/utils/image_data_extractor.py b/common/utils/image_data_extractor.py index bde9c97..74e8d2f 100644 --- a/common/utils/image_data_extractor.py +++ b/common/utils/image_data_extractor.py @@ -11,155 +11,54 @@ logger = logging.getLogger(__name__) - - -def describe_image_with_llm(image_input): +def describe_image_with_llm(file_path): """ - Send image (pixmap or PIL image) to LLM vision model and return description. - Uses multimodal_service from config if available, otherwise falls back to completion_service. - Currently supports: OpenAI, Azure OpenAI, Google GenAI, and Google VertexAI + Read image file and convert to base64 to send to LLM. """ try: + from PIL import Image as PILImage + client = get_multimodal_service() if not client: return "[Image: Failed to create multimodal LLM client]" - + + # Read image and convert to base64 + pil_image = PILImage.open(file_path) buffer = io.BytesIO() - # Convert to RGB if needed for better compatibility - if image_input.mode != 'RGB': - image_input = image_input.convert('RGB') - image_input.save(buffer, format="JPEG", quality=95) - b64_img = base64.b64encode(buffer.getvalue()).decode("utf-8") + if pil_image.mode != 'RGB': + pil_image = pil_image.convert('RGB') + pil_image.save(buffer, format="JPEG", quality=95) + image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8') - # Build messages (system + human) messages = [ - SystemMessage( - content="You are a helpful assistant that describes images concisely for document analysis." - ), - HumanMessage( - content=[ - { - "type": "text", - "text": ( - "Please describe what you see in this image and " - "if the image has scanned text then extract all the text. " - "if the image has any logo, icon, or branding element, try to describe it with text. " - "Focus on any text, diagrams, charts, or other visual elements." - "If the image is purely a logo, icon, or branding element, start your response with 'LOGO:' or 'ICON:'." - ), - }, - { - "type": "image_url", - "image_url": {"url": f"data:image/jpeg;base64,{b64_img}"}, - }, - ] - ), + SystemMessage( + content="You are a helpful assistant that describes images concisely for document analysis." + ), + HumanMessage( + content=[ + { + "type": "text", + "text": ( + "Please describe what you see in this image and " + "if the image has scanned text then extract all the text. " + "If the image has any graph, chart, table, or other diagram, describe it. " + ), + }, + { + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}, + }, + ], + ), ] - # Get response from LangChain LLM client - # Access the underlying LangChain client langchain_client = client.llm response = langchain_client.invoke(messages) - return response.content if hasattr(response, 'content') else str(response) + return response.content if hasattr(response, "content") else str(response) except Exception as e: logger.error(f"Failed to describe image with LLM: {str(e)}") return "[Image: Error processing image description]" -def save_image_and_get_markdown(image_input, context_info="", graphname=None): - """ - Save image locally to static/images/ folder and return markdown reference with description. - - LEGACY/OLD APPROACH: Used for backward compatibility with JSONL-based loading. - Images are saved as files and served via /ui/images/ endpoint with img:// protocol. - - For NEW direct loading approach, images are stored in Image vertex as base64 - and served via /ui/image_vertex/ endpoint with image:// protocol. - - Args: - image_input: PIL Image object - context_info: Optional context (e.g., "page 3 of invoice.pdf") - graphname: Graph name to organize images by graph (optional) - - Returns: - dict with: - - 'markdown': Markdown string with img:// reference - - 'image_id': Unique identifier for the saved image - - 'image_path': Path where image was saved to static/images/ - """ - try: - # FIRST: Get description from LLM to check if it's a logo - description = describe_image_with_llm(image_input) - - # Check if the image is a logo, icon, or decorative element BEFORE saving - # These should be filtered out as they're not content-relevant - description_lower = description.lower() - logo_indicators = ['logo', 'icon', 'branding', 'watermark', 'trademark', 'company logo', 'brand logo'] - - if any(indicator in description_lower for indicator in logo_indicators): - logger.info(f"Detected logo/icon in image, skipping: {description[:100]}") - return None - - # If not a logo, proceed with saving the image - # Generate unique image ID using hash of image content - buffer = io.BytesIO() - if image_input.mode != 'RGB': - image_input = image_input.convert('RGB') - image_input.save(buffer, format="JPEG", quality=95) - image_bytes = buffer.getvalue() - - # Create hash-based ID (deterministic for same image) - image_hash = hashlib.sha256(image_bytes).hexdigest()[:16] - image_id = f"{image_hash}.jpg" - - # Save image to local storage directory organized by graphname - project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - - # If graphname is provided, organize images by graph - if graphname: - images_dir = os.path.join(project_root, "static", "images", graphname) - # Include graphname in the image reference for URL construction - image_reference = f"{graphname}/{image_id}" - else: - images_dir = os.path.join(project_root, "static", "images") - image_reference = image_id - - os.makedirs(images_dir, exist_ok=True) - - image_path = os.path.join(images_dir, image_id) - - # Save image file (skip if already exists with same hash) - if not os.path.exists(image_path): - with open(image_path, 'wb') as f: - f.write(image_bytes) - logger.info(f"Saved content image to: {image_path}") - else: - logger.debug(f"Image already exists: {image_path}") - - # Generate markdown with custom img:// protocol (will be replaced later) - # Format: ![description](img://graphname/image_id) or ![description](img://image_id) - markdown = f"![{description}](img://{image_reference})" - - logger.info(f"Created image reference: {image_reference} with description") - - return { - 'markdown': markdown, - 'image_id': image_reference, - 'image_path': image_path, - 'description': description - } - - except Exception as e: - logger.error(f"Failed to save image and generate markdown: {str(e)}") - # Fallback to text description only - fallback_desc = f"[Image: {context_info} - processing failed]" - return { - 'markdown': fallback_desc, - 'image_id': None, - 'image_path': None, - 'description': fallback_desc - } - - diff --git a/common/utils/markdown_parsing.py b/common/utils/markdown_parsing.py new file mode 100644 index 0000000..7c8c476 --- /dev/null +++ b/common/utils/markdown_parsing.py @@ -0,0 +1,63 @@ +import re +import os +import pymupdf4llm + +class MarkdownProcessor: + """ + A helper class to extract markdown image entries and + update descriptions based on image_id. + """ + + # regex for markdown images: ![alt](path) + _pattern = re.compile(r'!\[([^\]]*)\]\(([^)\s]+)\)') + + @classmethod + def extract_images(cls, md_text): + """ + Returns list of {"path": path, "image_id": image_id} + image_id = basename without extension + """ + images = [] + for m in cls._pattern.finditer(md_text): + path = m.group(2) + basename = os.path.basename(path) + image_id = os.path.splitext(basename)[0] + images.append({"path": path, "image_id": image_id}) + return images + + @classmethod + def insert_description_by_id(cls, md_text, image_id, description): + """ + Replace the description for an image whose basename == image_id. + """ + + def repl(m): + old_path = m.group(2) + candidate_id = os.path.splitext(os.path.basename(old_path))[0] + + if candidate_id == image_id: + # Insert new description + return f'![{description}]({old_path})' + + return m.group(0) + + return cls._pattern.sub(repl, md_text) + + @classmethod + def replace_path_with_tg_protocol(cls, md_text, image_id, tg_reference): + """ + Replace the file path for an image whose basename == image_id with tg:// protocol reference. + tg_reference should be like 'Graphs_image_1' + """ + def repl(m): + old_path = m.group(2) + candidate_id = os.path.splitext(os.path.basename(old_path))[0] + + if candidate_id == image_id: + # Replace path with tg:// protocol reference + alt_text = m.group(1) + return f'![{alt_text}](tg://{tg_reference})' + + return m.group(0) + + return cls._pattern.sub(repl, md_text) \ No newline at end of file diff --git a/common/utils/text_extractors.py b/common/utils/text_extractors.py index da3e22d..9b5b652 100644 --- a/common/utils/text_extractors.py +++ b/common/utils/text_extractors.py @@ -8,6 +8,7 @@ import uuid import base64 import io +import threading from pathlib import Path import shutil import asyncio @@ -15,6 +16,9 @@ logger = logging.getLogger(__name__) +# Global lock for pymupdf4llm calls (not thread-safe) +_pymupdf4llm_lock = threading.Lock() + class TextExtractor: """Class for handling text extraction from various file formats and cleanup.""" @@ -38,10 +42,11 @@ def __init__(self): '.jpg': 'image/jpeg' } - async def _process_file_async(self, file_path, folder_path_obj, graphname): + async def _process_file_async(self, file_path, folder_path_obj, graphname, temp_folder, jsonl_file, jsonl_lock): """ Async helper to process a single file. Runs in thread pool to avoid blocking on I/O operations. + Appends documents immediately to JSONL file. """ try: loop = asyncio.get_event_loop() @@ -53,10 +58,21 @@ async def _process_file_async(self, file_path, folder_path_obj, graphname): graphname ) + # Append each document to JSONL file immediately + if doc_entries: + # Use lock to ensure thread-safe writing to JSONL file + async with jsonl_lock: + await loop.run_in_executor( + None, + self._append_to_jsonl, + jsonl_file, + doc_entries + ) + + # Return metadata only, documents already saved to JSONL return { 'success': True, 'file_path': str(file_path), - 'documents': doc_entries, 'num_documents': len(doc_entries) } @@ -67,11 +83,21 @@ async def _process_file_async(self, file_path, folder_path_obj, graphname): except Exception as e: logger.warning(f"Failed to process file {file_path}: {e}") return {'success': False, 'file_path': str(file_path), 'error': str(e)} + + def _append_to_jsonl(self, jsonl_file, doc_entries): + """ + Append document entries to JSONL file. + Each document is written as a separate line. + """ + with open(jsonl_file, 'a', encoding='utf-8') as f: + for doc_data in doc_entries: + json_line = json.dumps(doc_data, ensure_ascii=False) + f.write(json_line + '\n') - async def _process_folder_async(self, folder_path, graphname=None, max_concurrent=10): + async def _process_folder_async(self, folder_path, graphname, temp_folder, max_concurrent=10): """ Async version of process_folder for parallel file processing. - This prevents conflicts when multiple users process folders simultaneously. + Saves all documents immediately to a single JSONL file as they are processed. """ logger.info(f"Processing local folder ASYNC: {folder_path} for graph: {graphname} (max_concurrent={max_concurrent})") @@ -83,6 +109,13 @@ async def _process_folder_async(self, folder_path, graphname=None, max_concurren if not folder_path_obj.is_dir(): raise Exception(f"Path is not a directory: {folder_path}") + # Create temp folder and JSONL file + os.makedirs(temp_folder, exist_ok=True) + jsonl_file = os.path.join(temp_folder, "processed_documents.jsonl") + # Create async lock for thread-safe JSONL writing + jsonl_lock = asyncio.Lock() + logger.info(f"Saving processed documents to: {jsonl_file}") + def safe_walk(path): try: for item in path.iterdir(): @@ -110,13 +143,13 @@ def safe_walk(path): async def process_with_semaphore(file_path): async with semaphore: - return await self._process_file_async(file_path, folder_path_obj, graphname) + return await self._process_file_async(file_path, folder_path_obj, graphname, temp_folder, jsonl_file, jsonl_lock) tasks = [process_with_semaphore(fp) for fp in files_to_process] results = await asyncio.gather(*tasks, return_exceptions=True) - all_documents = [] processed_files_info = [] + total_docs = 0 for result in results: if isinstance(result, Exception): @@ -124,10 +157,12 @@ async def process_with_semaphore(file_path): continue if result.get('success'): - all_documents.extend(result.get('documents', [])) + num_docs = result.get('num_documents', 0) + total_docs += num_docs + processed_files_info.append({ 'file_path': result['file_path'], - 'num_documents': result.get('num_documents', len(result.get('documents', []))), + 'num_documents': num_docs, 'status': 'success' }) else: @@ -137,23 +172,118 @@ async def process_with_semaphore(file_path): 'error': result.get('error', 'Unknown error') }) - logger.info(f"Processed {len(processed_files_info)} files, extracted {len(all_documents)} total documents") + logger.info(f"Processed {len(processed_files_info)} files, extracted {total_docs} total documents") return { 'statusCode': 200, - 'message': f'Processed {len(processed_files_info)} files, {len(all_documents)} documents', - 'documents': all_documents, + 'message': f'Processed {len(processed_files_info)} files, {total_docs} documents', 'files': processed_files_info, - 'num_documents': len(all_documents) + 'num_documents': total_docs, + 'temp_folder': temp_folder, + 'jsonl_file': jsonl_file } - def process_folder(self, folder_path, graphname=None): + def process_folder(self, folder_path, graphname, temp_folder): """ Process local folder with multiple file formats and extract text content. Uses async processing internally for parallel file handling. + Saves all documents to JSONL file immediately as they are processed. + + Args: + folder_path: Path to the folder containing files to process + graphname: Name of the graph (for context) + temp_folder: Path to save processed documents as JSONL file """ logger.info(f"Processing local folder: {folder_path} for graph: {graphname}") - return asyncio.run(self._process_folder_async(folder_path, graphname)) + return asyncio.run(self._process_folder_async(folder_path, graphname, temp_folder)) + + def delete_file_from_jsonl(self, temp_folder, filename): + """ + Delete all documents related to a specific file from the JSONL file. + + Args: + temp_folder: Path to the temp folder containing processed_documents.jsonl + filename: Original filename (e.g., "report.pdf", "stock_gs200.jpg") + + Returns: + dict with status and number of documents removed + """ + jsonl_file = os.path.join(temp_folder, "processed_documents.jsonl") + + if not os.path.exists(jsonl_file): + logger.warning(f"JSONL file not found: {jsonl_file}") + return {'success': False, 'error': 'JSONL file not found'} + + # Get base name without extension to match doc_id + base_name = Path(filename).stem + logger.info(f"Deleting documents for file: {filename} (base_name: '{base_name}')") + + # Read all lines and filter out ones matching this file + remaining_lines = [] + removed_count = 0 + removed_doc_ids = [] + + try: + with open(jsonl_file, 'r', encoding='utf-8') as f: + for line_num, line in enumerate(f, 1): + line = line.strip() + if not line: + continue + + try: + doc_data = json.loads(line) + doc_id = doc_data.get('doc_id', '') + + # Check if doc_id matches the base_name or starts with base_name_ + # Handles: "stock_gs200" == "stock_gs200" or "stock_gs200_image_1".startswith("stock_gs200_") + if doc_id == base_name or doc_id.startswith(f"{base_name}_"): + removed_count += 1 + removed_doc_ids.append(doc_id) + logger.info(f"Removing document: {doc_id}") + else: + remaining_lines.append(line) + except json.JSONDecodeError as e: + logger.warning(f"Skipping invalid JSON at line {line_num}: {e}") + # Keep invalid lines in case they're important + remaining_lines.append(line) + + if removed_count == 0: + logger.warning(f"No documents found matching base_name: '{base_name}'") + return { + 'success': False, + 'error': f'No documents found for {filename}', + 'removed_count': 0 + } + + # If no lines remain, delete the entire temp folder + if not remaining_lines: + logger.info(f"No documents remaining, deleting temp folder: {temp_folder}") + import shutil + shutil.rmtree(temp_folder, ignore_errors=True) + return { + 'success': True, + 'removed_count': removed_count, + 'removed_doc_ids': removed_doc_ids, + 'temp_folder_deleted': True + } + + # Write remaining lines back to JSONL + with open(jsonl_file, 'w', encoding='utf-8') as f: + for line in remaining_lines: + f.write(line + '\n') + + logger.info(f"Removed {removed_count} documents ({', '.join(removed_doc_ids)}), {len(remaining_lines)} remaining") + return { + 'success': True, + 'removed_count': removed_count, + 'removed_doc_ids': removed_doc_ids, + 'remaining_count': len(remaining_lines), + 'temp_folder_deleted': False + } + + except Exception as e: + logger.error(f"Error deleting from JSONL: {e}") + return {'success': False, 'error': str(e)} def extract_text_from_file_with_images_as_docs(file_path, graphname=None): @@ -183,137 +313,167 @@ def extract_text_from_file_with_images_as_docs(file_path, graphname=None): def _extract_pdf_with_images_as_docs(file_path, base_doc_id, graphname=None): """ - Extract PDF as ONE markdown document with inline image references. + Extract PDF as ONE markdown document with inline image references using pymupdf4llm. + Uses unique temporary folder per PDF to allow parallel processing. + After processing, delete the extracted image folder. """ + # Use unique folder per PDF to allow parallel processing without conflicts + unique_folder_id = uuid.uuid4().hex[:12] + image_output_folder = Path(f"tg_temp_{unique_folder_id}") + try: - import fitz # PyMuPDF + import pymupdf4llm from PIL import Image as PILImage + from common.utils.image_data_extractor import describe_image_with_llm + from common.utils.markdown_parsing import MarkdownProcessor - doc = fitz.open(file_path) - markdown_parts = [] - image_entries = [] - image_counter = 0 + # Ensure clean slate - remove folder if it exists from failed previous run + if image_output_folder.exists(): + shutil.rmtree(image_output_folder, ignore_errors=True) - for page_num, page in enumerate(doc, start=1): - if page_num > 1: - markdown_parts.append("\n\n") - markdown_parts.append(f"--- Page {page_num} ---\n") #Avoid to be splitted as a single chunk + # Convert PDF to markdown with extracted image files + # Use lock because pymupdf4llm's table extraction is not thread-safe + # See: https://github.com/pymupdf/PyMuPDF/issues/3241 + with _pymupdf4llm_lock: + try: + markdown_content = pymupdf4llm.to_markdown( + file_path, + write_images=True, + image_path=str(image_output_folder), # unique folder per PDF + margins=0, + image_size_limit=0.08, + ) + except Exception: + # Retry with table_strategy="lines" if first attempt fails + try: + markdown_content = pymupdf4llm.to_markdown( + file_path, + write_images=True, + image_path=str(image_output_folder), # unique folder per PDF + margins=0, + image_size_limit=0.08, + table_strategy="lines", + ) + except Exception as e: + logger.error(f"pymupdf4llm failed for {file_path}: {e}") + # Cleanup folder if it was created + if image_output_folder.exists(): + shutil.rmtree(image_output_folder, ignore_errors=True) + return [{ + "doc_id": base_doc_id, + "doc_type": "markdown", + "content": f"[PDF extraction failed: {e}]", + "position": 0 + }] + + if not markdown_content or not markdown_content.strip(): + logger.warning(f"No content extracted from PDF: {file_path}") + + # Extract image references from markdown + image_refs = MarkdownProcessor.extract_images(markdown_content) + + if not image_refs: + # cleanup folder anyway + if image_output_folder.exists(): + shutil.rmtree(image_output_folder, ignore_errors=True) + + return [{ + "doc_id": base_doc_id, + "doc_type": "markdown", + "content": markdown_content, + "position": 0 + }] - blocks = page.get_text("blocks", sort=True) - text_blocks_with_pos = [] + image_entries = [] + image_counter = 0 - for block in blocks: - block_type = block[6] if len(block) > 6 else 0 - if block_type == 0: - text = block[4].strip() - if text: - y_pos = block[1] - text_blocks_with_pos.append({'type': 'text', 'content': text, 'y_pos': y_pos}) + for img_ref in image_refs: + try: + img_path = Path(img_ref["path"]) # convert to Path + image_id = img_ref["image_id"] + + # Image description + description = describe_image_with_llm(str(img_path)) + + markdown_content = MarkdownProcessor.insert_description_by_id( + markdown_content, + image_id, + description + ) + + # Convert image to base64 + pil_image = PILImage.open(img_path) + buffer = io.BytesIO() + + if pil_image.mode != "RGB": + pil_image = pil_image.convert("RGB") + + pil_image.save(buffer, format="JPEG", quality=95) + image_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8") + + image_counter += 1 + image_doc_id = f"{base_doc_id}_image_{image_counter}" + + # Replace file path with tg:// protocol reference in markdown + markdown_content = MarkdownProcessor.replace_path_with_tg_protocol( + markdown_content, + image_id, + image_doc_id + ) + + image_entries.append({ + "doc_id": image_doc_id, + "doc_type": "image", + "image_description": description, + "image_data": image_base64, + "image_format": "jpg", + "parent_doc": base_doc_id, + "page_number": 0, + "width": pil_image.width, + "height": pil_image.height, + "position": image_counter + }) - image_list = page.get_images(full=True) - images_with_pos = [] + except Exception as img_error: + logger.warning(f"Failed to process image {img_ref.get('path')}: {img_error}") - if image_list: - for img_index, img_info in enumerate(image_list): - try: - xref = img_info[0] - base_image = doc.extract_image(xref) - image_bytes = base_image["image"] - image_ext = base_image["ext"] - - img_rects = page.get_image_rects(xref) - y_pos = img_rects[0].y0 if img_rects else 999999 - - pil_image = PILImage.open(io.BytesIO(image_bytes)) - if pil_image.width < 100 or pil_image.height < 100: - continue - - from common.utils.image_data_extractor import describe_image_with_llm - description = describe_image_with_llm(pil_image) - description_lower = description.lower() - logo_indicators = [ - 'logo:', 'icon:', 'logo', 'icon', 'branding', - 'watermark', 'trademark', 'stylized letter', - 'stylized text', 'word "', "word '" - ] - if any(indicator in description_lower for indicator in logo_indicators): - continue - - buffer = io.BytesIO() - if pil_image.mode != 'RGB': - pil_image = pil_image.convert('RGB') - pil_image.save(buffer, format="JPEG", quality=95) - image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8') - - image_counter += 1 - image_doc_id = f"{base_doc_id}_image_{image_counter}" - - images_with_pos.append({ - 'type': 'image', - 'image_doc_id': image_doc_id, - 'description': description, - 'y_pos': y_pos, - 'image_data': image_base64, - 'image_format': image_ext, - 'width': pil_image.width, - 'height': pil_image.height - }) - except Exception as img_error: - logger.warning(f"Failed to extract image on page {page_num}: {img_error}") - - all_elements = text_blocks_with_pos + images_with_pos - all_elements.sort(key=lambda x: x['y_pos']) - - for element in all_elements: - if element['type'] == 'text': - markdown_parts.append(element['content']) - markdown_parts.append("\n\n") - else: - # Add image description as text, then markdown image reference - # Use short alt text in markdown, full description as regular text - markdown_parts.append(f"![{element['description']}](tg://{element['image_doc_id']})\n\n") - - image_entries.append({ - "doc_id": element['image_doc_id'], - "doc_type": "image", - "image_description": element['description'], - "image_data": element['image_data'], - "image_format": element['image_format'], - "parent_doc": base_doc_id, - "page_number": page_num, - "width": element['width'], - "height": element['height'], - "position": int(element['image_doc_id'].split('_')[-1]) - }) - - doc.close() - - markdown_content = "".join(markdown_parts) if markdown_parts else "" #No content extracted from PDF - if not markdown_content: - return [] + # FINAL CLEANUP — delete folder after processing everything + if image_output_folder.exists() and image_output_folder.is_dir(): + try: + shutil.rmtree(image_output_folder) + logger.debug(f"Deleted image folder: {image_output_folder}") + except Exception as delete_err: + logger.warning(f"Failed to delete folder {image_output_folder}: {delete_err}") + # Build final result result = [{ "doc_id": base_doc_id, - "doc_type": "", + "doc_type": "markdown", "content": markdown_content, "position": 0 }] result.extend(image_entries) + return result - except ImportError: - logger.error("PyMuPDF not available") + except ImportError as import_err: + logger.error(f"Required library missing: {import_err}") + # Cleanup on import error + if image_output_folder.exists(): + shutil.rmtree(image_output_folder, ignore_errors=True) return [{ "doc_id": base_doc_id, - "doc_type": "", - "content": "[PDF extraction requires PyMuPDF]", + "doc_type": "markdown", + "content": "[PDF extraction requires pymupdf4llm and PyMuPDF]", "position": 0 }] except Exception as e: logger.error(f"Error extracting PDF: {e}") + # Cleanup on any other error + if image_output_folder.exists(): + shutil.rmtree(image_output_folder, ignore_errors=True) raise - def _extract_standalone_image_as_doc(file_path, base_doc_id, graphname=None): """ Extract standalone image file as ONE markdown document with inline image reference. @@ -324,25 +484,15 @@ def _extract_standalone_image_as_doc(file_path, base_doc_id, graphname=None): pil_image = PILImage.open(file_path) if pil_image.width < 100 or pil_image.height < 100: - return [{ - "doc_id": base_doc_id, - "doc_type": "", - "content": f"[Skipped small image: {file_path.name}]", - "position": 0 - }] + pass - description = describe_image_with_llm(pil_image) + description = describe_image_with_llm(str(Path(file_path).absolute())) description_lower = description.lower() logo_indicators = ['logo:', 'icon:', 'logo', 'icon', 'branding', 'watermark', 'trademark', 'stylized letter', 'stylized text', 'word "', "word '"] if any(indicator in description_lower for indicator in logo_indicators): - return [{ - "doc_id": base_doc_id, - "doc_type": "", - "content": f"[Skipped logo/icon: {file_path.name}]", - "position": 0 - }] + return [] buffer = io.BytesIO() if pil_image.mode != 'RGB': @@ -353,7 +503,6 @@ def _extract_standalone_image_as_doc(file_path, base_doc_id, graphname=None): image_id = f"{base_doc_id}_image_1" # Put description as text, then markdown image reference with short alt text content = f"![{description}](tg://{image_id})" - return [ { "doc_id": base_doc_id, @@ -379,7 +528,7 @@ def _extract_standalone_image_as_doc(file_path, base_doc_id, graphname=None): logger.error(f"Error extracting image: {e}") return [{ "doc_id": base_doc_id, - "doc_type": "", + "doc_type": "markdown", "content": f"[Image extraction failed: {str(e)}]", "position": 0 }] @@ -441,12 +590,10 @@ def get_doc_type_from_extension(extension): if extension in ['.html', '.htm']: return 'html' - elif extension in ['.md']: - return 'markdown' elif extension in ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp']: return 'image' else: - return '' + return 'markdown' def get_supported_extensions(): @@ -457,4 +604,4 @@ def get_supported_extensions(): def is_supported_file(file_path): """Check if a file is supported for text extraction.""" extension = Path(file_path).suffix.lower() - return extension in get_supported_extensions() + return extension in get_supported_extensions() \ No newline at end of file diff --git a/graphrag-ui/src/pages/Setup.tsx b/graphrag-ui/src/pages/Setup.tsx index b7d357d..0e15939 100644 --- a/graphrag-ui/src/pages/Setup.tsx +++ b/graphrag-ui/src/pages/Setup.tsx @@ -2,7 +2,7 @@ import React, { useState, useEffect } from "react"; import { useNavigate } from "react-router-dom"; import { Button } from "@/components/ui/button"; import { Input } from "@/components/ui/input"; -import { Database, Upload, RefreshCw, Loader2, Trash2, FolderUp, Cloud, ArrowLeft, CloudDownload, CloudLightning } from "lucide-react"; +import { Database, Upload, RefreshCw, Loader2, Trash2, FolderUp, Cloud, ArrowLeft, CloudDownload, CloudCog } from "lucide-react"; import { Dialog, DialogContent, @@ -40,7 +40,7 @@ const Setup = () => { const navigate = useNavigate(); const [confirm, confirmDialog, isConfirmDialogOpen] = useConfirm(); const [availableGraphs, setAvailableGraphs] = useState([]); - + const [initializeGraphOpen, setInitializeGraphOpen] = useState(false); const [graphName, setGraphName] = useState(""); const [isInitializing, setIsInitializing] = useState(false); @@ -56,7 +56,13 @@ const Setup = () => { const [uploadMessage, setUploadMessage] = useState(""); const [isIngesting, setIsIngesting] = useState(false); const [ingestMessage, setIngestMessage] = useState(""); - const [activeTab, setActiveTab] = useState("upload"); + + // Ingestion temp files state + const [tempSessionId, setTempSessionId] = useState(null); + const [tempFiles, setTempFiles] = useState([]); + const [showTempFiles, setShowTempFiles] = useState(false); + const [ingestJobData, setIngestJobData] = useState(null); + const [directIngestion, setDirectIngestion] = useState(false); // Refresh state const [refreshOpen, setRefreshOpen] = useState(false); @@ -65,14 +71,15 @@ const Setup = () => { const [refreshGraphName, setRefreshGraphName] = useState(""); const [isRebuildRunning, setIsRebuildRunning] = useState(false); const [isCheckingStatus, setIsCheckingStatus] = useState(false); - + // S3 state + const [fileFormat, setFileFormat] = useState<"json" | "multi">("json"); const [awsAccessKey, setAwsAccessKey] = useState(""); const [awsSecretKey, setAwsSecretKey] = useState(""); + const [dataPath, setDataPath] = useState(""); const [inputBucket, setInputBucket] = useState(""); const [outputBucket, setOutputBucket] = useState(""); const [regionName, setRegionName] = useState(""); - const [skipBDAProcessing, setSkipBDAProcessing] = useState(false); // Cloud Download state const [cloudProvider, setCloudProvider] = useState<"s3" | "gcs" | "azure">("s3"); @@ -122,7 +129,7 @@ const Setup = () => { } const filesArray = Array.from(selectedFiles); - + // Check if any single file exceeds the server limit const oversizedFiles = filesArray.filter((file) => file.size > MAX_UPLOAD_SIZE_BYTES); if (oversizedFiles.length > 0) { @@ -159,13 +166,19 @@ const Setup = () => { const data = await response.json(); if (data.status === "success") { - setUploadMessage(`✅ ${data.message}`); + setUploadMessage(`✅ ${data.message} Processing...`); setSelectedFiles(null); await fetchUploadedFiles(); + + // Step 2: Call create_ingest to process uploaded files + console.log("Calling handleCreateIngestAfterUpload from main upload..."); + await handleCreateIngestAfterUpload("uploaded"); + console.log("handleCreateIngestAfterUpload completed"); } else { setUploadMessage(`⚠️ ${data.message}`); } } catch (error: any) { + console.error("Upload error:", error); setUploadMessage(`❌ Error: ${error.message}`); } finally { setIsUploading(false); @@ -187,9 +200,9 @@ const Setup = () => { for (let i = 0; i < filesArray.length; i++) { const file = filesArray[i]; const fileNumber = i + 1; - + setUploadMessage(`Uploading file ${fileNumber}/${totalFiles}: ${file.name} (${formatBytes(file.size)})...`); - + const formData = new FormData(); formData.append("files", file); @@ -219,14 +232,20 @@ const Setup = () => { // Show final result if (failedCount === 0) { - setUploadMessage(`✅ Successfully uploaded all ${uploadedCount} files (uploaded individually).`); + setUploadMessage(`✅ Successfully uploaded all ${uploadedCount} files. Processing...`); } else { - setUploadMessage(`⚠️ Uploaded ${uploadedCount} files successfully, ${failedCount} failed.`); + setUploadMessage(`⚠️ Uploaded ${uploadedCount} files successfully, ${failedCount} failed. Processing...`); } - + setSelectedFiles(null); await fetchUploadedFiles(); + + // Step 2: Call create_ingest to process uploaded files + console.log("Calling handleCreateIngestAfterUpload..."); + await handleCreateIngestAfterUpload("uploaded"); + console.log("handleCreateIngestAfterUpload completed"); } catch (error: any) { + console.error("Upload error:", error); setUploadMessage(`❌ Batch upload error: ${error.message}`); } finally { setIsUploading(false); @@ -237,19 +256,32 @@ const Setup = () => { const handleDeleteFile = async (filename: string) => { if (!ingestGraphName) return; + console.log("Deleting file:", filename); + console.log("tempSessionId:", tempSessionId); + try { const creds = localStorage.getItem("creds"); - const response = await fetch( - `/ui/${ingestGraphName}/uploads?filename=${encodeURIComponent(filename)}`, - { - method: "DELETE", - headers: { Authorization: `Basic ${creds}` }, - } - ); + + // Delete original file (backend will also delete processed content from JSONL if session_id is provided) + const url = tempSessionId + ? `/ui/${ingestGraphName}/uploads?filename=${encodeURIComponent(filename)}&session_id=${tempSessionId}` + : `/ui/${ingestGraphName}/uploads?filename=${encodeURIComponent(filename)}`; + + const response = await fetch(url, { + method: "DELETE", + headers: { Authorization: `Basic ${creds}` }, + }); const data = await response.json(); + setUploadMessage(`✅ ${data.message}`); await fetchUploadedFiles(); + + // Refresh temp files list if session exists + if (tempSessionId) { + await fetchTempFiles(tempSessionId); + } } catch (error: any) { + console.error("Delete error:", error); setUploadMessage(`❌ Error: ${error.message}`); } }; @@ -268,6 +300,12 @@ const Setup = () => { headers: { Authorization: `Basic ${creds}` }, }); const data = await response.json(); + + // Also clear temp session + if (tempSessionId) { + await handleDeleteAllTempFiles(); + } + setUploadMessage(`✅ ${data.message}`); await fetchUploadedFiles(); } catch (error: any) { @@ -303,7 +341,7 @@ const Setup = () => { try { const creds = localStorage.getItem("creds"); - + // Prepare request body based on provider let requestBody: any = { provider: cloudProvider }; @@ -360,8 +398,11 @@ const Setup = () => { const data = await response.json(); if (data.status === "success") { - setDownloadMessage(`✅ ${data.message}`); + setDownloadMessage(`✅ ${data.message}. Processing...`); await fetchDownloadedFiles(); + + // Step 2: Call create_ingest to process downloaded files + await handleCreateIngestAfterUpload("downloaded"); } else if (data.status === "warning") { setDownloadMessage(`⚠️ ${data.message}`); } else { @@ -380,16 +421,25 @@ const Setup = () => { try { const creds = localStorage.getItem("creds"); - const response = await fetch( - `/ui/${ingestGraphName}/cloud/delete?filename=${encodeURIComponent(filename)}`, - { - method: "DELETE", - headers: { Authorization: `Basic ${creds}` }, - } - ); + + // Delete original file (backend will also delete processed content from JSONL if session_id is provided) + const url = tempSessionId + ? `/ui/${ingestGraphName}/cloud/delete?filename=${encodeURIComponent(filename)}&session_id=${tempSessionId}` + : `/ui/${ingestGraphName}/cloud/delete?filename=${encodeURIComponent(filename)}`; + + const response = await fetch(url, { + method: "DELETE", + headers: { Authorization: `Basic ${creds}` }, + }); const data = await response.json(); + setDownloadMessage(`✅ ${data.message}`); await fetchDownloadedFiles(); + + // Refresh temp files list if session exists + if (tempSessionId) { + await fetchTempFiles(tempSessionId); + } } catch (error: any) { setDownloadMessage(`❌ Error: ${error.message}`); } @@ -416,6 +466,179 @@ const Setup = () => { } }; + // Fetch temp processed files + const fetchTempFiles = async (sessionId: string) => { + if (!ingestGraphName || !sessionId) return; + + try { + const creds = localStorage.getItem("creds"); + const response = await fetch(`/ui/${ingestGraphName}/ingestion_temp/list?session_id=${sessionId}`, { + headers: { Authorization: `Basic ${creds}` }, + }); + const data = await response.json(); + if (data.status === "success" && data.sessions.length > 0) { + setTempFiles(data.sessions[0].files || []); + setShowTempFiles(true); + } + } catch (error) { + console.error("Error fetching temp files:", error); + } + }; + + // Delete a specific temp file + const handleDeleteTempFile = async (filename: string) => { + if (!ingestGraphName || !tempSessionId) return; + + try { + const creds = localStorage.getItem("creds"); + const response = await fetch( + `/ui/${ingestGraphName}/ingestion_temp/delete?session_id=${tempSessionId}&filename=${encodeURIComponent(filename)}`, + { + method: "DELETE", + headers: { Authorization: `Basic ${creds}` }, + } + ); + const data = await response.json(); + if (data.status === "success") { + setIngestMessage(`✅ ${data.message}`); + // Refresh the temp files list + await fetchTempFiles(tempSessionId); + } + } catch (error: any) { + setIngestMessage(`❌ Error: ${error.message}`); + } + }; + + // Delete all temp files for session + const handleDeleteAllTempFiles = async () => { + if (!ingestGraphName || !tempSessionId) return; + + try { + const creds = localStorage.getItem("creds"); + const response = await fetch( + `/ui/${ingestGraphName}/ingestion_temp/delete?session_id=${tempSessionId}`, + { + method: "DELETE", + headers: { Authorization: `Basic ${creds}` }, + } + ); + const data = await response.json(); + if (data.status === "success") { + setIngestMessage(`✅ ${data.message}`); + setTempFiles([]); + setShowTempFiles(false); + setTempSessionId(null); + } + } catch (error: any) { + setIngestMessage(`❌ Error: ${error.message}`); + } + }; + + // Delete temp files matching original filename + const handleDeleteTempFilesForOriginal = async (originalFilename: string) => { + console.log("handleDeleteTempFilesForOriginal called with:", originalFilename); + + if (!ingestGraphName || !tempSessionId) { + console.log("No graph name or session ID, returning"); + return; + } + + try { + // Extract base name without extension (e.g., "document.pdf" -> "document") + const baseName = originalFilename.replace(/\.[^/.]+$/, ""); + console.log("Base name:", baseName); + + const creds = localStorage.getItem("creds"); + + // Fetch temp files to find matches + const response = await fetch(`/ui/${ingestGraphName}/ingestion_temp/list?session_id=${tempSessionId}`, { + headers: { Authorization: `Basic ${creds}` }, + }); + const data = await response.json(); + console.log("Temp files list response:", data); + + if (data.status === "success" && data.sessions.length > 0) { + const files = data.sessions[0].files || []; + console.log("All temp files:", files.map((f: any) => f.filename)); + + // Find temp files matching pattern: doc_{idx}_{baseName}*.json + const matchingFiles = files.filter((f: any) => f.filename.includes(`_${baseName}`)); + console.log("Matching files to delete:", matchingFiles.map((f: any) => f.filename)); + + // Delete each matching file + for (const file of matchingFiles) { + console.log("Deleting temp file:", file.filename); + const deleteResponse = await fetch( + `/ui/${ingestGraphName}/ingestion_temp/delete?session_id=${tempSessionId}&filename=${encodeURIComponent(file.filename)}`, + { + method: "DELETE", + headers: { Authorization: `Basic ${creds}` }, + } + ); + const deleteData = await deleteResponse.json(); + console.log("Delete response:", deleteData); + } + + console.log(`Successfully deleted ${matchingFiles.length} temp file(s)`); + } else { + console.log("No temp files found or empty sessions"); + } + } catch (error: any) { + console.error("Error deleting temp files:", error); + } + }; + + // Run final ingest after user reviews temp files + const handleRunIngest = async () => { + if (!ingestJobData) { + setIngestMessage("❌ No ingest job data available"); + return; + } + + setIsIngesting(true); + setIngestMessage("Running final document ingest..."); + + try { + const creds = localStorage.getItem("creds"); + + const loadingInfo = { + load_job_id: ingestJobData.load_job_id, + data_source_id: ingestJobData.data_source_id, + file_path: ingestJobData.data_path, + }; + + const ingestResponse = await fetch(`/ui/${ingestGraphName}/ingest`, { + method: "POST", + headers: { + "Content-Type": "application/json", + Authorization: `Basic ${creds}`, + }, + body: JSON.stringify(loadingInfo), + }); + + if (!ingestResponse.ok) { + const errorData = await ingestResponse.json(); + throw new Error(errorData.detail || `Failed to run ingest: ${ingestResponse.statusText}`); + } + + const ingestData = await ingestResponse.json(); + console.log("Ingest response:", ingestData); + + setIngestMessage(`✅ Data ingested successfully! Processed ${tempFiles.length} documents.`); + + // Clear temp state + setTempFiles([]); + setShowTempFiles(false); + setTempSessionId(null); + setIngestJobData(null); + } catch (error: any) { + console.error("Error running ingest:", error); + setIngestMessage(`❌ Error: ${error.message}`); + } finally { + setIsIngesting(false); + } + }; + // Ingest files into knowledge graph (uploaded or downloaded) const handleIngestDocuments = async (sourceType: "uploaded" | "downloaded" = "uploaded") => { if (!ingestGraphName) { @@ -423,7 +646,7 @@ const Setup = () => { return; } - const folderPath = sourceType === "uploaded" + const folderPath = sourceType === "uploaded" ? `uploads/${ingestGraphName}` : `downloaded_files_cloud/${ingestGraphName}`; @@ -458,45 +681,143 @@ const Setup = () => { } const createData = await createResponse.json(); - //console.log("Create ingest response:", createData); + console.log("Create ingest response:", createData); - // Step 2: Run ingest - setIngestMessage("Step 2/2: Running document ingest..."); + // Check if temp files were created (for server data source) + const sessionId = createData.data_source_id?.temp_session_id; - const loadingInfo = { - load_job_id: createData.load_job_id, - data_source_id: createData.data_source_id, - file_path: createData.data_path || createData.file_path, // Handle both field names + if (sessionId && !directIngestion) { + // Files are saved to temp storage - show them for review (only if not direct ingestion) + setTempSessionId(sessionId); + setIngestJobData({ + load_job_id: createData.load_job_id, + data_source_id: createData.data_source_id, + data_path: createData.data_path || createData.file_path, + }); + setIngestMessage(`✅ Processed ${createData.data_source_id.file_count} files. Review them below before ingesting.`); + await fetchTempFiles(sessionId); + setIsIngesting(false); + } else { + // No temp files (e.g., S3 Bedrock) OR direct ingestion enabled - proceed directly to ingest + setIngestMessage("Step 2/2: Running document ingest..."); + + const loadingInfo = { + load_job_id: createData.load_job_id, + data_source_id: createData.data_source_id, + file_path: createData.data_path || createData.file_path, + }; + + const ingestResponse = await fetch(`/ui/${ingestGraphName}/ingest`, { + method: "POST", + headers: { + "Content-Type": "application/json", + Authorization: `Basic ${creds}`, + }, + body: JSON.stringify(loadingInfo), + }); + + if (!ingestResponse.ok) { + const errorData = await ingestResponse.json(); + throw new Error(errorData.detail || `Failed to run ingest: ${ingestResponse.statusText}`); + } + + const ingestData = await ingestResponse.json(); + console.log("Ingest response:", ingestData); + + setIngestMessage(`✅ Data ingested successfully! Processed documents from ${folderPath}/`); + setIsIngesting(false); + } + } catch (error: any) { + console.error("Error ingesting data:", error); + setIngestMessage(`❌ Error: ${error.message}`); + setIsIngesting(false); + } + }; + + // Create ingest after upload/download (called automatically after files are uploaded/downloaded) + const handleCreateIngestAfterUpload = async (sourceType: "uploaded" | "downloaded" = "uploaded") => { + console.log("handleCreateIngestAfterUpload called with sourceType:", sourceType); + console.log("ingestGraphName:", ingestGraphName); + + if (!ingestGraphName) { + console.log("No graph name, returning early"); + return; + } + + const folderPath = sourceType === "uploaded" + ? `uploads/${ingestGraphName}` + : `downloaded_files_cloud/${ingestGraphName}`; + + console.log("folderPath:", folderPath); + + try { + const creds = localStorage.getItem("creds"); + + // Call create_ingest to process files + const createIngestConfig = { + data_source: "server", + data_source_config: { + folder_path: folderPath + }, + loader_config: {}, + file_format: "multi" }; - const ingestResponse = await fetch(`/ui/${ingestGraphName}/ingest`, { + console.log("Calling create_ingest with config:", createIngestConfig); + + const createResponse = await fetch(`/ui/${ingestGraphName}/create_ingest`, { method: "POST", headers: { "Content-Type": "application/json", Authorization: `Basic ${creds}`, }, - body: JSON.stringify(loadingInfo), + body: JSON.stringify(createIngestConfig), }); - if (!ingestResponse.ok) { - const errorData = await ingestResponse.json(); - throw new Error(errorData.detail || `Failed to run ingest: ${ingestResponse.statusText}`); + console.log("create_ingest response status:", createResponse.status); + + if (!createResponse.ok) { + const errorData = await createResponse.json(); + console.error("create_ingest error:", errorData); + throw new Error(errorData.detail || `Failed to create ingest job: ${createResponse.statusText}`); } - const ingestData = await ingestResponse.json(); - //console.log("Ingest response:", ingestData); + const createData = await createResponse.json(); + console.log("create_ingest response data:", createData); + + const sessionId = createData.data_source_id?.temp_session_id; + console.log("Session ID:", sessionId); - setIngestMessage(`✅ Data ingested successfully! Processed documents from ${folderPath}/`); + if (sessionId) { + // Save session ID for later ingest + setTempSessionId(sessionId); + setIngestJobData({ + load_job_id: createData.load_job_id, + data_source_id: createData.data_source_id, + data_path: createData.data_path || createData.file_path, + }); + + console.log("Direct ingestion enabled:", directIngestion); + + if (directIngestion) { + // Direct ingestion - proceed to ingest immediately + setUploadMessage("Running direct ingestion..."); + await handleRunIngest(); + } else { + // Save for later - files ready for ingestion + setUploadMessage(`✅ Successfully processed ${createData.data_source_id.file_count} files. Ready for ingestion.`); + } + } else { + console.warn("No session ID returned from create_ingest"); + } } catch (error: any) { - console.error("Error ingesting data:", error); - setIngestMessage(`❌ Error: ${error.message}`); - } finally { - setIsIngesting(false); + console.error("Error in create_ingest:", error); + setUploadMessage(`❌ Processing error: ${error.message}`); } }; - // Ingest files from S3 with Amazon BDA - const handleAmazonBDAIngest = async () => { + // Ingest files from S3 with Bedrock BDA + const handleS3BedrockIngest = async () => { if (!ingestGraphName) { setIngestMessage("Please select a graph"); return; @@ -508,112 +829,92 @@ const Setup = () => { return; } - if (skipBDAProcessing) { - // When skipping BDA, only output bucket and region are required - if (!outputBucket || !regionName) { - setIngestMessage("❌ Please provide Output Bucket and Region Name"); - return; - } - } else { - // When using BDA, all fields are required + if (fileFormat === "multi") { if (!inputBucket || !outputBucket || !regionName) { setIngestMessage("❌ Please provide Input Bucket, Output Bucket, and Region Name"); return; } - } - // Ask for confirmation - const confirmMessage = skipBDAProcessing - ? `You're skipping Amazon BDA processing and will ingest directly from the output bucket (${outputBucket}). Please confirm to proceed.` - : `You're using Amazon BDA for multimodal document processing. This will trigger Amazon BDA to process your documents from the input bucket (${inputBucket}) and store the results in the output bucket (${outputBucket}) and then ingest them into your knowledge graph. Please confirm to proceed.`; - - const shouldProceed = await confirm(confirmMessage); - if (!shouldProceed) { - setIngestMessage("Operation cancelled by user."); - return; + // Ask for confirmation if using Bedrock (multi format) + const shouldProceed = await confirm( + `Are you using AWS Bedrock for multimodal document processing? This will trigger AWS Bedrock BDA to process your documents from the input bucket (${inputBucket}) and store the results in the output bucket (${outputBucket}).` + ); + if (!shouldProceed) { + setIngestMessage("Operation cancelled by user."); + return; + } + } else if (fileFormat === "json") { + if (!dataPath) { + setIngestMessage("❌ Please provide Data Path (e.g., s3://bucket-name/path/to/data)"); + return; + } } setIsIngesting(true); + setIngestMessage("Step 1/2: Creating ingest job..."); try { const creds = localStorage.getItem("creds"); - let loadingInfo: any = {}; - if (skipBDAProcessing) { - // Skip BDA processing - create ingest job that reads directly from output bucket - const runIngestConfig: any = { - data_source: "bda", + // Step 1: Create ingest job + const createIngestConfig: any = { + data_source: "s3", + data_source_config: { aws_access_key: awsAccessKey, aws_secret_key: awsSecretKey, - output_bucket: outputBucket, - region_name: regionName, - bda_jobs:[], - loader_config: { - doc_id_field: "doc_id", - content_field: "content", - doc_type: "markdown", - }, - file_format: "multi" - }; - - setIngestMessage("Step 1/2: Creating ingest job from output bucket..."); - - // Run ingest directly - loadingInfo = { - load_job_id: "load_documents_content_json", - data_source_id: runIngestConfig, - file_path: outputBucket, - }; - setIngestMessage(`Step 2/2: Running document ingestion for all files in ${outputBucket}...`); - } else { - // Step 1: Create ingest job with BDA processing - const createIngestConfig: any = { - data_source: "bda", - data_source_config: { - aws_access_key: awsAccessKey, - aws_secret_key: awsSecretKey, - input_bucket: inputBucket, - output_bucket: outputBucket, - region_name: regionName, - }, - loader_config: { - doc_id_field: "doc_id", - content_field: "content", - doc_type: "markdown", - }, - file_format: "multi" - }; + }, + loader_config: { + doc_id_field: "doc_id", + content_field: "content", + doc_type: fileFormat === "multi" ? "markdown" : "", + }, + file_format: fileFormat + }; - setIngestMessage("Step 1/2: Triggering Amazon BDA processing and creating ingest job..."); + // Add format-specific configuration + if (fileFormat === "multi") { + createIngestConfig.data_source_config.input_bucket = inputBucket; + createIngestConfig.data_source_config.output_bucket = outputBucket; + createIngestConfig.data_source_config.region_name = regionName; + setIngestMessage("Step 1/2: Creating ingest job and triggering AWS Bedrock BDA processing..."); + } else if (fileFormat === "json") { + createIngestConfig.loader_config.doc_id_field = "url"; + } - const createResponse = await fetch(`/ui/${ingestGraphName}/create_ingest`, { - method: "POST", - headers: { - "Content-Type": "application/json", - Authorization: `Basic ${creds}`, - }, - body: JSON.stringify(createIngestConfig), - }); + const createResponse = await fetch(`/ui/${ingestGraphName}/create_ingest`, { + method: "POST", + headers: { + "Content-Type": "application/json", + Authorization: `Basic ${creds}`, + }, + body: JSON.stringify(createIngestConfig), + }); - if (!createResponse.ok) { - const errorData = await createResponse.json(); - throw new Error(errorData.detail || `Failed to create ingest job: ${createResponse.statusText}`); - } + if (!createResponse.ok) { + const errorData = await createResponse.json(); + throw new Error(errorData.detail || `Failed to create ingest job: ${createResponse.statusText}`); + } - const createData = await createResponse.json(); - //console.log("Create ingest response:", createData); + const createData = await createResponse.json(); + console.log("Create ingest response:", createData); - // Step 2: Run ingest - loadingInfo = { - load_job_id: createData.load_job_id, - data_source_id: createData.data_source_id, - file_path: outputBucket, - }; + // Step 2: Run ingest + setIngestMessage("Step 2/2: Running document ingest..."); - const filesToIngest = createData.data_source_id.bda_jobs.map((job: any) => job.jobId.split("/")[-1]); - setIngestMessage(`Step 2/2: Running document ingest for ${filesToIngest.length} files in ${outputBucket}...`); + // Determine file path based on format + let filePath = ""; + if (fileFormat === "multi") { + filePath = outputBucket; // For multi format, use output bucket + } else if (fileFormat === "json") { + filePath = dataPath; // For json format, use the provided data path } + const loadingInfo = { + load_job_id: createData.load_job_id, + data_source_id: createData.data_source_id, + file_path: filePath, + }; + const ingestResponse = await fetch(`/ui/${ingestGraphName}/ingest`, { method: "POST", headers: { @@ -629,13 +930,15 @@ const Setup = () => { } const ingestData = await ingestResponse.json(); - //console.log("Ingest response:", ingestData); - const filesIngested = ingestData.summary.map((file: any) => file.file_path); - - setIngestMessage(`✅ Document ingestion completed successfully! Ingested ${filesIngested.length} into your knowledge graph.`); + console.log("Ingest response:", ingestData); + if (fileFormat === "multi") { + setIngestMessage(`✅ Data ingested successfully! AWS Bedrock BDA processed documents from ${inputBucket} and loaded results from ${outputBucket}.`); + } else { + setIngestMessage(`✅ Data ingested successfully! Processed documents from ${dataPath}.`); + } } catch (error: any) { - console.error("Error ingesting files:", error); + console.error("Error ingesting S3 data:", error); setIngestMessage(`❌ Error: ${error.message}`); } finally { setIsIngesting(false); @@ -664,9 +967,9 @@ const Setup = () => { const statusData = await statusResponse.json(); const wasRunning = isRebuildRunning; const isCurrentlyRunning = statusData.is_running || false; - + setIsRebuildRunning(isCurrentlyRunning); - + if (isCurrentlyRunning) { const startTime = statusData.started_at ? new Date(statusData.started_at * 1000).toLocaleString() : "unknown time"; setRefreshMessage(`⚠️ A rebuild is already in progress for "${graphName}" (started at ${startTime}). Please wait for it to complete.`); @@ -722,7 +1025,7 @@ const Setup = () => { try { const creds = localStorage.getItem("creds"); - + const response = await fetch(`/ui/${refreshGraphName}/rebuild_graph`, { method: "POST", headers: { @@ -753,12 +1056,12 @@ const Setup = () => { if (refreshOpen && refreshGraphName) { // Check status immediately when dialog opens checkRebuildStatus(refreshGraphName, true); - + // Set up polling to check status every 5 seconds while dialog is open const intervalId = setInterval(() => { checkRebuildStatus(refreshGraphName, false); }, 5000); - + return () => clearInterval(intervalId); } }, [refreshOpen, refreshGraphName]); @@ -858,10 +1161,10 @@ const Setup = () => { setIsInitializing(false); return; } - + setStatusMessage(`✅ Graph "${graphName}" created and initialized successfully! You can now close this dialog.`); setStatusType("success"); - + // Add the new graph to the available graphs list const newGraph = graphName; setAvailableGraphs(prev => { @@ -875,7 +1178,7 @@ const Setup = () => { } return prev; }); - + // Set the newly created graph as selected for ingestion setIngestGraphName(graphName); setRefreshGraphName(graphName); @@ -911,7 +1214,7 @@ const Setup = () => { {/* Three cards displayed horizontally */}
- + {/* Section 1: Initialize Knowledge Graph */}
@@ -926,7 +1229,7 @@ const Setup = () => {

-
-
-
{/* Initialize Graph Dialog */} - { // Prevent closing if confirm dialog is open @@ -997,7 +1300,7 @@ const Setup = () => { setInitializeGraphOpen(open); }} > - e.preventDefault()} > @@ -1007,7 +1310,7 @@ const Setup = () => { Enter the name of your knowledge graph. The system will create it if necessary and initialize it with the GraphRAG schema. - +
{/* Data Ingest Dialog */} - { // Prevent closing if confirm dialog is open if (!open && isConfirmDialogOpen) { @@ -1105,7 +1407,7 @@ const Setup = () => { setIngestOpen(open); }} > - e.preventDefault()} > @@ -1121,8 +1423,8 @@ const Setup = () => { - + @@ -1139,35 +1441,32 @@ const Setup = () => { )} + {ingestGraphName && ( +

+ Files will be uploaded to: uploads/{ingestGraphName}/ +

+ )}
- { - // Block tab switching when ingesting - if (!isIngesting) { - setActiveTab(value); - } - }} className="w-full"> + - + Upload Files - + Download from Cloud - - - Use Amazon BDA + + + Amazon BDA Configuration {/* Upload Data Tab */}
-

- Upload local files to the server and ingest them into your knowledge graph. -

@@ -1230,9 +1543,10 @@ const Setup = () => {

Process uploaded files and add them to the knowledge graph

+ {ingestMessage && ( -
+ }`}> {ingestMessage}
)} @@ -1295,9 +1608,6 @@ const Setup = () => { {/* Download from Cloud Storage Tab */}
-

- Download files from cloud storage and ingest them into your knowledge graph. -

)} - {ingestGraphName && ( -

- Download destination: downloaded_files_cloud/{ingestGraphName}/ -

- )}
-
{downloadMessage && ( -
+ }`}> {downloadMessage}
)} @@ -1574,9 +1881,10 @@ const Setup = () => {

Process downloaded files and add them to the knowledge graph

+ {ingestMessage && ( -
+ }`}> {ingestMessage}
)} @@ -1607,12 +1914,23 @@ const Setup = () => {
- {/* Amazon BDA Configuration Tab */} - -
-

- Process multimodal documents stored in S3 with Amazon Bedrock Data Automation and ingest them into your knowledge graph. -

+ {/* S3 Bedrock Configuration Tab */} + +
+
+ + +
{/* Common fields */}
@@ -1625,7 +1943,6 @@ const Setup = () => { onChange={(e) => setAwsAccessKey(e.target.value)} placeholder="Enter AWS access key" className="dark:border-[#3D3D3D] dark:bg-shadeA" - disabled={isIngesting} />
@@ -1639,74 +1956,76 @@ const Setup = () => { onChange={(e) => setAwsSecretKey(e.target.value)} placeholder="Enter AWS secret key" className="dark:border-[#3D3D3D] dark:bg-shadeA" - disabled={isIngesting} />
-
-
- -
- -
- - setOutputBucket(e.target.value)} - placeholder="Enter output bucket name" - className="dark:border-[#3D3D3D] dark:bg-shadeA" - disabled={isIngesting} - /> -
+ ) : ( + <> +
+ + setInputBucket(e.target.value)} + placeholder="Enter input bucket name" + className="dark:border-[#3D3D3D] dark:bg-shadeA" + /> +
-
- - setRegionName(e.target.value)} - placeholder="e.g., us-east-1" - className="dark:border-[#3D3D3D] dark:bg-shadeA" - disabled={isIngesting} - /> -
+
+ + setOutputBucket(e.target.value)} + placeholder="Enter output bucket name" + className="dark:border-[#3D3D3D] dark:bg-shadeA" + /> +
- {ingestGraphName && ( -

- Processing destination: Input bucket ({inputBucket || "not specified"}) → Output bucket ({outputBucket || "not specified"}) → Knowledge graph ({ingestGraphName}) -

+
+ + setRegionName(e.target.value)} + placeholder="e.g., us-east-1" + className="dark:border-[#3D3D3D] dark:bg-shadeA" + /> +
+ )} - {/* Ingest S3 Files with Amazon BDA Section */} + {/* Ingest S3 Bedrock Data Section */}
+

+ Ingest S3 Data into Knowledge Graph +

+

+ Process S3 data and add it to the knowledge graph using AWS Bedrock BDA for multimodal documents +

{ingestMessage && ( -
+ }`}> {ingestMessage}
)} @@ -1754,8 +2072,8 @@ const Setup = () => { {/* Refresh Graph Dialog */} - { // Prevent closing if confirm dialog is open if (!open && isConfirmDialogOpen) { @@ -1764,14 +2082,14 @@ const Setup = () => { setRefreshOpen(open); }} > - e.preventDefault()} > Refresh Knowledge Graph - Rebuild the graph content and rerun community detection for your knowledge graph + Rebuild the graph content of your knowledge graph @@ -1780,8 +2098,8 @@ const Setup = () => { - + @@ -1805,19 +2123,18 @@ const Setup = () => { ⚠️ Warning

- This operation will process new documents and rerun community detection that will interrupt related queries. + This operation will rebuild the graph content that will interrupt related queries. Please confirm to proceed.

{refreshMessage && ( -
+ }`}> {refreshMessage}
)} diff --git a/graphrag/app/routers/ui.py b/graphrag/app/routers/ui.py index 9637347..b69afae 100644 --- a/graphrag/app/routers/ui.py +++ b/graphrag/app/routers/ui.py @@ -52,6 +52,7 @@ from common.logs.logwriter import LogWriter from common.metrics.prometheus_metrics import metrics as pmetrics from supportai import supportai +from common.utils.text_extractors import TextExtractor from common.py_schemas.schemas import ( AgentProgess, CreateIngestConfig, @@ -395,6 +396,7 @@ async def serve_image_from_vertex( LogWriter.info(f"Serving image {image_id} from graph {graphname}") # Fetch the Image vertex by ID + # TigerGraph loading job uses gsql_lower() so all IDs are stored in lowercase image_vertices = conn.getVerticesById('Image', [image_id.lower()]) if not image_vertices: @@ -989,6 +991,7 @@ async def clear_uploaded_files( graphname: str, creds: Annotated[tuple[list[str], HTTPBasicCredentials], Depends(ui_basic_auth)], filename: str | None = None, + session_id: str | None = None, ): """ Clear uploaded files for a specific graphname. @@ -996,6 +999,7 @@ async def clear_uploaded_files( Parameters: - graphname: The graph name whose files to clear - filename: If provided, only delete this specific file. Otherwise, delete all files. + - session_id: Optional session ID to delete processed content from temp folder """ try: upload_dir = os.path.join("uploads", graphname) @@ -1008,9 +1012,21 @@ async def clear_uploaded_files( } deleted_files = [] + text_extractor = TextExtractor() if filename: - # Delete specific file + # Delete processed content from JSONL FIRST if session_id provided + if session_id: + temp_folder = os.path.join("uploads", "ingestion_temp", graphname, session_id) + if os.path.exists(temp_folder): + logger.info(f"Deleting processed content for {filename} from temp folder") + result = text_extractor.delete_file_from_jsonl(temp_folder, filename) + if result.get('success'): + logger.info(f"Removed {result.get('removed_count', 0)} processed documents for {filename}") + else: + logger.warning(f"Failed to remove processed content: {result.get('error', 'Unknown error')}") + + # Then delete the original file file_path = os.path.join(upload_dir, filename) if os.path.exists(file_path) and os.path.isfile(file_path): os.remove(file_path) @@ -1048,6 +1064,7 @@ async def clear_uploaded_files( raise HTTPException(status_code=500, detail=f"Error deleting files: {str(e)}") + # Cloud Storage Download Endpoints @router.post(route_prefix + "/{graphname}/cloud/download") @@ -1321,6 +1338,7 @@ async def delete_cloud_downloads( graphname: str, credentials: Annotated[HTTPBase, Depends(security)], filename: str = None, + session_id: str = None, ): """ Delete downloaded cloud files for a specific graph. @@ -1328,6 +1346,7 @@ async def delete_cloud_downloads( Parameters: - graphname: The graph name whose downloaded files to clear - filename: If provided, only delete this specific file. Otherwise, delete all files. + - session_id: Optional session ID to delete processed content from temp folder """ try: download_dir = os.path.join("downloaded_files_cloud", graphname) @@ -1340,9 +1359,21 @@ async def delete_cloud_downloads( } deleted_files = [] + text_extractor = TextExtractor() if filename: - # Delete specific file + # Delete processed content from JSONL FIRST if session_id provided + if session_id: + temp_folder = os.path.join("uploads", "ingestion_temp", graphname, session_id) + if os.path.exists(temp_folder): + logger.info(f"Deleting processed content for {filename} from temp folder") + result = text_extractor.delete_file_from_jsonl(temp_folder, filename) + if result.get('success'): + logger.info(f"Removed {result.get('removed_count', 0)} processed documents for {filename}") + else: + logger.warning(f"Failed to remove processed content: {result.get('error', 'Unknown error')}") + + # Then delete the original file file_path = os.path.join(download_dir, filename) if os.path.exists(file_path) and os.path.isfile(file_path): os.remove(file_path) @@ -1379,3 +1410,139 @@ async def delete_cloud_downloads( logger.debug_pii(f"Delete error trace:\n{exc}") raise HTTPException(status_code=500, detail=f"Error deleting files: {str(e)}") + +# Ingestion Temp Files Endpoints + +@router.get(route_prefix + "/{graphname}/ingestion_temp/list") +async def list_ingestion_temp_files( + graphname: str, + credentials: Annotated[HTTPBase, Depends(security)], + session_id: str = None, +): + """ + List processed files in the ingestion temp folder for a specific graph. + """ + try: + base_temp_dir = os.path.join("uploads", "ingestion_temp", graphname) + + if not os.path.exists(base_temp_dir): + return { + "status": "success", + "graphname": graphname, + "sessions": [], + "total_files": 0, + } + + sessions = [] + total_files = 0 + + # If session_id provided, list only that session + if session_id: + session_dir = os.path.join(base_temp_dir, session_id) + if os.path.exists(session_dir) and os.path.isdir(session_dir): + files = [] + for filename in os.listdir(session_dir): + filepath = os.path.join(session_dir, filename) + if os.path.isfile(filepath) and filename.endswith('.json'): + file_stat = os.stat(filepath) + # Read doc_id from file + try: + with open(filepath, 'r', encoding='utf-8') as f: + doc_data = json.load(f) + doc_id = doc_data.get('doc_id', 'unknown') + except: + doc_id = 'unknown' + + files.append({ + "filename": filename, + "doc_id": doc_id, + "size": file_stat.st_size, + "modified": file_stat.st_mtime, + }) + sessions.append({ + "session_id": session_id, + "files": files, + "file_count": len(files), + }) + total_files = len(files) + + return { + "status": "success", + "graphname": graphname, + "sessions": sessions, + "total_files": total_files, + } + + except Exception as e: + exc = traceback.format_exc() + logger.error(f"Error listing ingestion temp files for graph {graphname}: {e}") + logger.debug_pii(f"List error trace:\n{exc}") + raise HTTPException(status_code=500, detail=f"Error listing temp files: {str(e)}") + + +@router.delete(route_prefix + "/{graphname}/ingestion_temp/delete") +async def delete_ingestion_temp_files( + graphname: str, + credentials: Annotated[HTTPBase, Depends(security)], + session_id: str = None, + filename: str = None, +): + """ + Delete files from ingestion temp folder. + """ + try: + base_temp_dir = os.path.join("uploads", "ingestion_temp", graphname) + + if not session_id: + raise HTTPException(status_code=400, detail="session_id is required") + + session_dir = os.path.join(base_temp_dir, session_id) + + if not os.path.exists(session_dir): + return { + "status": "success", + "message": f"No temp files found for session {session_id}", + "deleted_files": [], + } + + deleted_files = [] + + if filename: + # Delete specific file + file_path = os.path.join(session_dir, filename) + if os.path.exists(file_path) and os.path.isfile(file_path): + os.remove(file_path) + deleted_files.append(filename) + logger.info(f"Deleted temp file {filename} from session {session_id}") + + # If session folder is now empty, remove it + if not os.listdir(session_dir): + os.rmdir(session_dir) + logger.info(f"Removed empty session folder {session_id}") + else: + raise HTTPException(status_code=404, detail=f"File {filename} not found") + else: + # Delete entire session folder + import shutil + for filename in os.listdir(session_dir): + if os.path.isfile(os.path.join(session_dir, filename)): + deleted_files.append(filename) + + shutil.rmtree(session_dir) + logger.info(f"Deleted session folder {session_id} for graph {graphname}") + + return { + "status": "success", + "message": f"Successfully deleted {len(deleted_files)} file(s)", + "deleted_files": deleted_files, + "session_id": session_id, + } + + except HTTPException: + raise + except Exception as e: + exc = traceback.format_exc() + logger.error(f"Error deleting ingestion temp files for graph {graphname}: {e}") + logger.debug_pii(f"Delete error trace:\n{exc}") + raise HTTPException(status_code=500, detail=f"Error deleting temp files: {str(e)}") + diff --git a/graphrag/app/supportai/supportai.py b/graphrag/app/supportai/supportai.py index d2efe8a..a9dbbe0 100644 --- a/graphrag/app/supportai/supportai.py +++ b/graphrag/app/supportai/supportai.py @@ -337,9 +337,9 @@ def create_ingest( conn: TigerGraphConnection, ): # Check for invalid combination of multi format and non-s3 data source - if ingest_config.data_source.lower() in ["bda", "server"] and ingest_config.get("file_format", "").lower() != "multi": - logger.warning(f"File format {ingest_config.get('file_format', '').lower()} is not supported for data source {ingest_config.data_source.lower()}") - ingest_config["file_format"] = "multi" + if ingest_config.data_source.lower() in ["bda", "server"] and ingest_config.file_format.lower() != "multi": + logger.warning(f"File format {ingest_config.file_format.lower()} is not supported for data source {ingest_config.data_source.lower()}") + ingest_config.file_format = "multi" res_ingest_config = {"data_source": ingest_config.data_source.lower()} res_ingest_config["file_format"] = ingest_config.file_format.lower() @@ -481,22 +481,34 @@ def create_ingest( except Exception as e: raise Exception(f"Error during Amazon BDA preprocessing: {e}") elif ingest_config.data_source.lower() == "server": - data_path = ingest_config.data_source_config.get("data_path", None) + data_path = ingest_config.data_source_config.get("folder_path", None) if data_path is None: - raise Exception("Data path not provided for server processing") + raise Exception("Folder path not provided for server processing") try: + # Create temp folder BEFORE processing so extractor can save directly + temp_session_id = str(uuid.uuid4()) + temp_folder = os.path.join("uploads", "ingestion_temp", graphname, temp_session_id) + + # Process files and save immediately to temp folder (memory efficient) extractor = TextExtractor() - server_processing_result = extractor.process_folder(data_path, graphname=graphname) + server_processing_result = extractor.process_folder( + data_path, + graphname=graphname, + temp_folder=temp_folder # Extractor saves files as it processes + ) + if server_processing_result.get("statusCode") != 200: raise Exception(f"Server folder processing failed: {server_processing_result}") - else: - logger.info(f"Server folder processing completed successfully: {server_processing_result}") - - res_ingest_config["server_jobs"] = server_processing_result.get("documents", []) + + doc_count = server_processing_result.get("num_documents", 0) + logger.info(f"Server folder processing completed: {server_processing_result.get('message')}") + + res_ingest_config["temp_session_id"] = temp_session_id + res_ingest_config["temp_folder"] = temp_folder + res_ingest_config["file_count"] = doc_count res_ingest_config["data_source_id"] = "DocumentContent" - # Use a placeholder path that doesn't start with "/" to avoid pyTigerGraph treating it as a file - # The actual folder path is stored in server_jobs, this is just for the API call - res["data_path"] = "in_response" + # Use a placeholder path to indicate temp storage + res["data_path"] = "in_temp_storage" res["data_source_id"] = res_ingest_config except Exception as e: raise Exception(f"Error during server folder processing: {e}") @@ -648,41 +660,45 @@ def ingest( } elif ingest_config.get("data_source") == "server": try: - processed_files = [] data_source_id = ingest_config.get("data_source_id", "DocumentContent") - if ingest_config.get("server_jobs"): - for doc_data in ingest_config.get("server_jobs"): - if not doc_data.get("doc_id") or not doc_data.get("content"): - continue - if doc_data.get("image_data"): - payload = { - "doc_id": doc_data.get("doc_id", ""), - "doc_type": "image", - "image_data": doc_data.get("image_data", ""), - "image_format": doc_data.get("image_format", "jpg"), - "parent_doc": doc_data.get("parent_doc", ""), - "page_number": doc_data.get("page_number", 0), - "position": doc_data.get("position", 0), - "content": "" - } - else: - payload = { - "doc_id": doc_data.get("doc_id", ""), - "doc_type": doc_data.get("doc_type", "markdown"), - "content": doc_data.get("content", "") - } - payload_json = json.dumps(payload) - conn.runLoadingJobWithData(payload_json, data_source_id, loader_info.load_job_id) - processed_files.append({ - 'file_path': doc_data.get("doc_id", ""), - 'parent_doc': doc_data.get("parent_doc", ""), - }) - logger.info(f"Data uploading done for doc_id: {doc_data.get('doc_id', 'unknown')}") + + # Read from temporary folder's JSONL file + temp_folder = ingest_config.get("temp_folder") + if not temp_folder or not os.path.exists(temp_folder): + raise Exception(f"Temporary folder not found: {temp_folder}") + + # Read the entire JSONL file as a string + jsonl_file = os.path.join(temp_folder, "processed_documents.jsonl") + if not os.path.exists(jsonl_file): + raise Exception(f"JSONL file not found: {jsonl_file}") + + logger.info(f"Reading JSONL file: {jsonl_file}") + + # Read entire JSONL content + with open(jsonl_file, 'r', encoding='utf-8') as f: + jsonl_content = f.read() + + # Load all documents in one call - runLoadingJobWithData supports JSONL format + conn.runLoadingJobWithData(jsonl_content, data_source_id, loader_info.load_job_id) + + # Count documents for reporting + doc_count = sum(1 for line in jsonl_content.strip().split('\n') if line.strip()) + logger.info(f"Successfully ingested {doc_count} documents from JSONL") + + # Clean up temp folder after successful ingestion + try: + import shutil + shutil.rmtree(temp_folder) + logger.info(f"Cleaned up temporary folder: {temp_folder}") + except Exception as cleanup_error: + logger.warning(f"Failed to cleanup temp folder {temp_folder}: {cleanup_error}") + except Exception as e: raise Exception(f"Error during server markdown extraction and TigerGraph loading: {e}") return { "job_name": loader_info.load_job_id, - "summary": processed_files + "summary": f"Successfully ingested {doc_count} documents from JSONL", + "document_count": doc_count } else: raise Exception("Data source and file format combination not implemented") diff --git a/licenses/pymupdf4llm-AGPL-3.0.txt b/licenses/pymupdf4llm-AGPL-3.0.txt new file mode 100644 index 0000000..0ad25db --- /dev/null +++ b/licenses/pymupdf4llm-AGPL-3.0.txt @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Remote Network Interaction; Use with the GNU General Public License. + + Notwithstanding any other provision of this License, if you modify the +Program, your modified version must prominently offer all users +interacting with it remotely through a computer network (if your version +supports such interaction) an opportunity to receive the Corresponding +Source of your version by providing access to the Corresponding Source +from a network server at no charge, through some standard or customary +means of facilitating copying of software. This Corresponding Source +shall include the Corresponding Source for any work covered by version 3 +of the GNU General Public License that is incorporated pursuant to the +following paragraph. + + Notwithstanding any other provision of this License, you have +permission to link or combine any covered work with a work licensed +under version 3 of the GNU General Public License into a single +combined work, and to convey the resulting work. 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If the Program does not specify a version number of the +GNU Affero General Public License, you may choose any version ever published +by the Free Software Foundation. + + If the Program specifies that a proxy can decide which future +versions of the GNU Affero General Public License can be used, that proxy's +public statement of acceptance of a version permanently authorizes you +to choose that version for the Program. + + Later license versions may give you additional or different +permissions. However, no additional obligations are imposed on any +author or copyright holder as a result of your choosing to follow a +later version. + + 15. Disclaimer of Warranty. + + THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY +APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT +HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY +OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, +THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM +IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF +ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + + 16. Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS +THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE +USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF +DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD +PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), +EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF +SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU Affero General Public License as published + by the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Affero General Public License for more details. + + You should have received a copy of the GNU Affero General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +.