Feat/prometheus initial structure #2
Conversation
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This commit establishes the foundational framework for the Prometheus Protocol. - Basic directory structure (`core`, `ui`, `utils`, `tests`) to organize future development. - Core `PromptObject` data schema in Key additions include:`prometheus_protocol/core/prompt.py`, defining the components of an AI prompt (role, context, task, constraints, examples). This class includes type hints and docstrings. - A project-level `README.md` in the `prometheus_protocol` directory, outlining the vision and guiding principles (Expanded KISS) of the project. These elements provide the starting point for building the "Prometheus Protocol: Engineering Intent" platform.
This commit introduces the initial version of the GIGO (Garbage In, Garbage Out)
Guardrail system for validating PromptObject instances.
Key changes:
- Created `prometheus_protocol/core/exceptions.py` with custom validation
exceptions: `PromptValidationError`, `MissingRequiredFieldError`,
`InvalidListTypeError`, and `InvalidListItemError`.
- Implemented `validate_prompt()` in `prometheus_protocol/core/guardrails.py`.
This function checks for:
- Non-empty `role`, `task`, and `context`.
- Correct types and content for `constraints` and `examples` lists
(must be lists of non-empty strings, if provided).
- Added a comprehensive unit test suite in
`prometheus_protocol/tests/test_guardrails.py` to verify the
functionality of `validate_prompt()` under various conditions,
ensuring correct exception handling.
This guardrail provides a foundational layer for ensuring the quality
and integrity of prompts used within the Prometheus Protocol.
This commit introduces the foundational structure for the Prometheus Protocol
Template Library, enabling you to save, load, and list prompt templates.
Key additions:
1. **TemplateManager (`prometheus_protocol/core/template_manager.py`):**
* Created `TemplateManager` class to handle template operations.
* Implemented `__init__` to set up a templates directory
(defaulting to `prometheus_protocol/templates/`).
* Implemented `save_template()`:
- Sanitizes template names for safe filenames.
- Serializes `PromptObject` instances (using `to_dict()`) to JSON.
- Saves templates as `.json` files in the templates directory.
- Handles empty/invalid names and file I/O errors.
* Implemented `load_template()`:
- Sanitizes template names to locate files.
- Reads `.json` files and deserializes them (using
`PromptObject.from_dict()`) into `PromptObject` instances.
- Handles `FileNotFoundError` and `TemplateCorruptedError`.
* Implemented `list_templates()`:
- Scans the templates directory.
- Returns a sorted list of available template names (from filenames).
2. **Custom Exception (`prometheus_protocol/core/exceptions.py`):**
* Added `TemplateCorruptedError` for issues during template loading
(e.g., invalid JSON, mismatched data structure).
3. **Unit Tests (`prometheus_protocol/tests/test_template_manager.py`):**
* Created a comprehensive test suite for `TemplateManager`.
* Uses `tempfile.TemporaryDirectory` for isolated testing of file operations.
* Covers successful save, load, and list operations.
* Tests various error conditions, including invalid names, file not found,
and corrupted template files.
* Verifies template name sanitization consistency.
4. **Directory Structure:**
* Added `prometheus_protocol/templates/` directory with a `.gitkeep` file
to store prompt templates.
This provides the core infrastructure for managing and reusing prompts
within the Prometheus Protocol.
This commit introduces the core data structures for the Multi-Turn
Composer feature, enabling the definition and management of sequenced
AI interactions.
Key additions:
1. **Conversation Data Models (`prometheus_protocol/core/conversation.py`):**
* Defined `PromptTurn` dataclass:
- Represents a single turn in a conversation.
- Attributes: `turn_id` (auto-UUID), `prompt_object` (PromptObject),
`parent_turn_id` (Optional[str]), `conditions` (Optional[Dict]),
`notes` (Optional[str]).
- Includes `to_dict()` and `from_dict()` methods for serialization,
correctly handling the nested `PromptObject`.
* Defined `Conversation` dataclass:
- Represents an entire multi-turn sequence.
- Attributes: `conversation_id` (auto-UUID), `title` (str),
`description` (Optional[str]), `turns` (List[PromptTurn]),
`created_at` (auto-ISO8601 UTC), `last_modified_at` (auto-ISO8601 UTC),
`tags` (List[str]).
- Includes a `touch()` method to update `last_modified_at`.
- Includes `to_dict()` and `from_dict()` methods for serialization,
managing the list of `PromptTurn` objects and providing defaults
for robust deserialization.
2. **Unit Tests (`prometheus_protocol/tests/test_conversation.py`):**
* Created a comprehensive test suite for `PromptTurn` and `Conversation`.
* Covers initialization with default and provided values.
* Thoroughly tests `to_dict()` and `from_dict()` methods, including:
- Nested object serialization/deserialization.
- Handling of optional fields and default value assignments.
- Idempotency of the serialization process.
* Includes a helper for comparing ISO timestamp strings with tolerance.
* Tests the `Conversation.touch()` method.
These data models provide a solid foundation for building out the
full functionality of the Multi-Turn Composer, including management
(saving/loading conversations) and execution logic.
This commit introduces the ConversationManager, enabling the saving,
loading, and listing of multi-turn Conversation objects. This provides
persistence and reusability for complex AI interaction sequences.
Key additions:
1. **ConversationManager (`prometheus_protocol/core/conversation_manager.py`):**
* Created `ConversationManager` class to handle CRUD-like operations
for Conversation instances.
* `__init__`: Sets up a directory for storing conversation files
(defaulting to `prometheus_protocol/conversations/`).
* `save_conversation()`:
- Sanitizes conversation names for safe filenames.
- Calls `conversation.touch()` to update `last_modified_at` before saving.
- Serializes `Conversation` objects (using `to_dict()`) to JSON.
- Saves conversations as `.json` files.
- Handles invalid names and file I/O errors.
* `load_conversation()`:
- Sanitizes conversation names to locate files.
- Reads `.json` files and deserializes them (using
`Conversation.from_dict()`) into `Conversation` objects.
- Handles `FileNotFoundError` and `ConversationCorruptedError`.
* `list_conversations()`:
- Scans the conversations directory.
- Returns a sorted list of available conversation names.
2. **Custom Exception (`prometheus_protocol/core/exceptions.py`):**
* Added `ConversationCorruptedError` for issues during conversation
file loading (e.g., invalid JSON, mismatched data structure).
3. **Unit Tests (`prometheus_protocol/tests/test_conversation_manager.py`):**
* Created a comprehensive test suite for `ConversationManager`.
* Utilizes `tempfile.TemporaryDirectory` for isolated testing of
file operations.
* Covers successful save, load, and list operations.
* Includes extensive tests for error conditions: invalid names,
file not found, corrupted files, and mismatched data structures.
* Verifies that `last_modified_at` is updated upon saving.
* Ensures consistency in name sanitization between save and load.
4. **Directory Structure:**
* Added `prometheus_protocol/conversations/` directory with a `.gitkeep`
file to store conversation files.
This enhancement significantly advances the capabilities of Prometheus
Protocol by allowing you to manage and reuse your structured,
multi-turn AI dialogues.
This commit introduces the initial version of the Risk Identifier feature,
designed to analyze PromptObject instances for potential semantic or
ethical risks beyond basic structural validation.
Key additions and changes:
1. **Risk Types Definition (`prometheus_protocol/core/risk_types.py`):**
* Created `RiskLevel` Enum (INFO, WARNING, CRITICAL).
* Created `RiskType` Enum (LACK_OF_SPECIFICITY, KEYWORD_WATCH,
UNCONSTRAINED_GENERATION, AMBIGUITY).
* Defined `PotentialRisk` dataclass to represent an identified risk,
including its type, level, message, offending field, and details.
2. **RiskIdentifier Implementation (`prometheus_protocol/core/risk_identifier.py`):**
* Created `RiskIdentifier` class.
* Implemented `identify_risks(prompt: PromptObject)` method which
analyzes the prompt and returns a list of `PotentialRisk` objects.
* Implemented three initial risk identification rules:
- Lack of Specificity in Task: Flags short tasks with no constraints.
- Keyword Watch: Identifies predefined keywords in task/context
related to potentially sensitive categories (e.g., financial, medical).
- Potentially Unconstrained Complex Task: Flags tasks that seem
complex but have very few constraints.
3. **Unit Tests (`prometheus_protocol/tests/test_risk_identifier.py`):**
* Created a comprehensive test suite for `RiskIdentifier`.
* Includes tests for each rule, covering scenarios that should
trigger the risk and those that should not.
* Tests for multiple risks being identified and for no risks being
identified in well-formed prompts.
4. **UI Concept Update (`prometheus_protocol/ui_concepts/prompt_editor.md`):**
* Added a new section "VII. Risk Identifier Feedback Display".
* Describes how identified risks (type, level, message) would be
presented to you within the PromptObject Editor, emphasizing their
advisory nature and visual distinction from GIGO Guardrail errors.
This feature provides you with proactive guidance to improve the safety,
clarity, and effectiveness of your prompts, contributing to more
responsible AI interaction.
This commit introduces version control for prompt templates managed by
the TemplateManager. You can now save multiple versions of a prompt
template, load specific versions, or load the latest version.
Key changes:
1. **TemplateManager (`prometheus_protocol/core/template_manager.py`):**
* Added private helper methods for version handling:
- `_sanitize_base_name`: Sanitizes template name to a base name.
- `_construct_filename`: Creates versioned filenames (e.g., `name_v1.json`).
- `_get_versions_for_base_name`: Lists available versions for a base name.
- `_get_highest_version`: Gets the latest version number for a base name.
* `save_template` updated:
- Now returns the updated `PromptObject`.
- Automatically increments version number on saving an existing base name.
- Updates `PromptObject.version` and `PromptObject.last_modified_at`
before saving.
- Saves to a versioned filename (e.g., `base_name_v2.json`).
* `load_template` updated:
- Signature changed to `load_template(template_name, version=None)`.
- If `version` is None, loads the highest (latest) version.
- If `version` is specified, loads that specific version.
- Raises `FileNotFoundError` if template or version doesn't exist.
* `list_templates` updated:
- Now returns `Dict[str, List[int]]`, mapping base template names
to a sorted list of their available integer versions.
2. **Unit Tests (`prometheus_protocol/tests/test_template_manager.py`):**
* Comprehensively refactored to test all aspects of versioning:
- Saving new templates and subsequent versions.
- Correctly updating `PromptObject.version` and `last_modified_at`.
- Loading latest and specific versions.
- Listing templates with their versions.
- Error handling for non-existent templates/versions.
- Name sanitization with versioning.
3. **UI Concepts (`prometheus_protocol/ui_concepts/prompt_editor.md`):**
* Updated "Interaction with TemplateManager" section to describe how
you would interact with versioned templates:
- Selecting versions when loading.
- Being informed of the created version when saving.
- Editor UI reflecting the loaded/saved prompt's version.
This enhancement allows you to iterate on your prompts more effectively,
track changes, and manage a history of your prompt engineering efforts.
This commit enhances the GIGO Guardrail with more sophisticated validation
rules to further improve prompt quality by detecting unresolved placeholders
and repetitive list items.
Key changes:
1. **New Custom Exceptions (`prometheus_protocol/core/exceptions.py`):**
* Added `UnresolvedPlaceholderError`: Raised when common placeholder
patterns (e.g., "[INSERT_X]", "{{VAR}}", "<placeholder>") are found
in prompt fields, indicating incomplete content.
* Added `RepetitiveListItemError`: Raised when duplicate or very
similar items (case-insensitive, ignoring leading/trailing whitespace)
are found within list-based fields like 'constraints' or 'examples'.
2. **Guardrails Update (`prometheus_protocol/core/guardrails.py`):**
* The `validate_prompt` function now includes logic for:
- **Unresolved Placeholder Detection:** Uses regex to scan `role`,
`context`, `task`, and items in `constraints` and `examples`
for various placeholder patterns.
- **Repetitive List Item Detection:** Normalizes and checks for
duplicates in `constraints` and `examples` lists.
* Updated docstrings to reflect new checks and exceptions.
3. **Unit Tests (`prometheus_protocol/tests/test_guardrails.py`):**
* Added new test methods specifically for the advanced rules:
- Covering various placeholder patterns in different fields.
- Testing case-insensitivity for placeholder detection.
- Testing detection of exact and normalized (case/whitespace)
duplicates in lists.
- Ensuring prompts without these issues pass correctly.
- Verifying that appropriate exceptions are raised with informative
messages.
4. **UI Concept Update (`prometheus_protocol/ui_concepts/prompt_editor.md`):**
* Updated sections on "Inline Validation Feedback" and "GIGO Guardrail
Error Summary List" to include examples of how error messages
from `UnresolvedPlaceholderError` and `RepetitiveListItemError`
would be displayed to you.
These advanced rules provide you with more intelligent feedback, helping
you create more complete, precise, and effective prompts.
This commit significantly enhances my conceptual stubs to provide more dynamic and varied simulated AI responses. This will help you better test downstream components and give you a clearer understanding of the execution flow.
Key changes:
1. **My Core Logic (`prometheus_protocol/core/jules_executor.py`):**
* `_prepare_jules_request_payload`: I've refined this for explicit mapping of `PromptObject` fields to the hypothetical API request structure and consistent `request_id_client` generation.
* `execute_prompt`: I've modified this to return dynamic `AIResponse` objects. Based on keywords in `prompt.task` (e.g., "error_test:content_policy", "error_test:overload", "error_test:auth"), I now simulate various success and error scenarios, populating `AIResponse` with appropriate content, error messages, and dummy metadata. I also handle very short tasks with an advisory message.
* `execute_conversation_turn`: I've similarly updated this to simulate dynamic success and error responses for individual conversation turns based on the turn's task. Dummy content now acknowledges conversation history.
2. **Unit Tests (`prometheus_protocol/tests/test_jules_executor.py`):**
* I've created a new test suite for my core logic.
* This includes tests for `_prepare_jules_request_payload` to verify correct request formatting with and without history.
* It provides comprehensive tests for `execute_prompt` and `execute_conversation_turn`, ensuring that the different simulated response scenarios (various errors, specific success messages based on input) are triggered correctly and the returned `AIResponse` objects are accurately populated.
3. **Execution Logic Concepts (`prometheus_protocol/concepts/execution_logic.md`):**
* I've refined the "Update `current_conversation_history`" subsection to clarify that for V1 simulation, `turn.prompt_object.task` represents your turn in history, and my error messages are not added to the history passed to my subsequent calls.
These enhancements make my conceptual stubs a more robust tool for conceptual development and testing within the Prometheus Protocol ecosystem, providing more realistic simulated interactions with the hypothetical AI engine.
This commit introduces the `created_by_user_id` field to the `PromptObject`
dataclass. This optional field is intended to store the unique identifier
of the user who originally created the prompt object, which is beneficial
for attribution, especially in collaborative workspace contexts, and for
future analytics.
Key changes:
1. **`PromptObject` (`prometheus_protocol/core/prompt.py`):**
* Added `created_by_user_id: Optional[str] = None` to the dataclass.
* Updated `to_dict()` to include this new field in the serialization.
* Updated `from_dict()` to correctly deserialize this field, defaulting
to `None` if it's missing in the input data.
* Updated class and field docstrings.
2. **Unit Tests (`prometheus_protocol/tests/test_prompt.py`):**
* Modified existing tests (`test_init_default_metadata`,
`test_init_provided_metadata`, `test_to_dict_serialization`,
`test_from_dict_deserialization`, `test_serialization_idempotency`)
to cover the `created_by_user_id` field.
* Added new specific tests:
`test_to_dict_serialization_with_none_user_id` and
`test_from_dict_deserialization_missing_or_none_user_id`
to ensure robust handling of `None` or missing values.
3. **System Overview (`SYSTEM_OVERVIEW.md`):**
* Updated the "Core Data Structures" section to include
`created_by_user_id` in `PromptObject`'s attribute list.
* Marked the corresponding item in the "Identified Areas for Future
Refinement/Development" section as "DONE" and added a summary
of the change and notes on future work (populating with actual
user IDs via an auth system).
This enhancement prepares `PromptObject` for better integration with
upcoming collaboration features and more detailed analytics.
This commit introduces an optional `settings` field to the `PromptObject`
dataclass, allowing you to specify execution parameters like temperature
and max_tokens on a per-prompt basis. I have been updated to use these settings,
overriding my defaults when provided.
Key changes:
1. **`PromptObject` (`prometheus_protocol/core/prompt.py`):**
* Added `settings: Optional[Dict[str, Any]] = None` field.
* Updated `to_dict()` and `from_dict()` to handle serialization of
the `settings` field.
* Updated docstrings.
2. **`PromptObject` Unit Tests (`prometheus_protocol/tests/test_prompt.py`):**
* Enhanced tests to cover the new `settings` field, including
initialization (default and provided), serialization,
deserialization (present, missing, or None values), and idempotency.
3. **`JulesExecutor` (`prometheus_protocol/core/jules_executor.py`):**
* Modified `_prepare_jules_request_payload` to merge any non-None
values from `prompt.settings` with its own default execution
parameters (e.g., temperature, max_tokens). Prompt-specific
settings now take precedence.
4. **`JulesExecutor` Unit Tests (`prometheus_protocol/tests/test_jules_executor.py`):**
* Added `test_prepare_jules_request_payload_settings_override` to
comprehensively verify the correct merging and overriding of
settings from `PromptObject` in various scenarios.
5. **System Overview (`SYSTEM_OVERVIEW.md`):**
* Updated "Core Data Structures" to include `settings` in `PromptObject`.
* Updated "Core Logic Components/Managers" to note that `JulesExecutor`
now considers `PromptObject.settings`.
* Marked the corresponding item in the "Refinement Backlog" as "DONE."
6. **UI Concepts (`prometheus_protocol/ui_concepts/prompt_editor.md`):**
* Added a new subsection for a conceptual "Execution Settings Panel"
within the PromptObject Editor, describing UI elements for temperature,
max_tokens, and their binding to `PromptObject.settings`.
This enhancement provides you with finer-grained control over AI behavior
for individual prompts and makes prompts more self-contained with their
intended execution parameters.
This commit introduces the `UserSettings` dataclass to store your specific
preferences and configurations for Prometheus Protocol. It also updates
various conceptual documents to reflect how these settings, along with the
recently added `PromptObject.settings`, would influence system behavior
and UI.
Key changes:
1. **`UserSettings` Dataclass (`prometheus_protocol/core/user_settings.py`):**
* Created `UserSettings` dataclass with fields for `user_id` (mandatory),
`default_jules_api_key`, `default_jules_model`,
`default_execution_settings` (for PromptObject defaults), `ui_theme`,
`preferred_output_language`, `creative_catalyst_defaults`, and
`last_updated_at`.
* Includes `to_dict()`, `from_dict()`, and `touch()` methods.
* Uses `default_factory=dict` for dictionary fields.
2. **`UserSettings` Unit Tests (`prometheus_protocol/tests/test_user_settings.py`):**
* Created a comprehensive test suite for `UserSettings`.
* Covers initialization (minimal and full), serialization (`to_dict`),
deserialization (`from_dict` including handling of missing optional
fields and mandatory `user_id`), idempotency, and the `touch()` method.
3. **Conceptual Document Updates:**
* **`SYSTEM_OVERVIEW.md`:**
- Added `UserSettings` to "Core Data Structures."
- Updated `PromptObject` attributes to include `settings`.
- Updated `JulesExecutor` description to note merging of
`PromptObject.settings`.
- Marked backlog items for `UserSettings` and `PromptObject.settings`
as "DONE."
* **`prometheus_protocol/concepts/execution_logic.md`:**
- Referenced `UserSettings` for `api_key` and
`output_language_preference` in hypothetical Jules API calls.
- Clarified the settings override hierarchy:
`PromptObject.settings` > `UserSettings.default_execution_settings` >
`JulesExecutor` internal defaults.
* **`prometheus_protocol/concepts/creative_catalyst_modules.md`:**
- Noted that default "Creativity Level" for catalyst modules could
be sourced from `UserSettings.creative_catalyst_defaults`.
* **`prometheus_protocol/ui_concepts/prompt_editor.md`:**
- Updated placeholder hints in the "Execution Settings Panel" to
reflect that defaults can come from `UserSettings`.
This `UserSettings` data model provides a crucial structure for future
personalization and user-specific configuration management within
Prometheus Protocol.
…ersation
This commit refines the `ConversationManager.save_conversation` method
to return the (potentially modified) Conversation object, aligning its
behavior more closely with `TemplateManager.save_template` for consistency.
Key changes:
1. **`ConversationManager` (`prometheus_protocol/core/conversation_manager.py`):**
* The `save_conversation` method signature changed from `-> None` to
`-> Conversation`.
* The method now returns the `conversation` object after its
`last_modified_at` timestamp has been updated by `touch()`.
* Docstring updated to reflect the new return type and behavior.
2. **Unit Tests (`prometheus_protocol/tests/test_conversation_manager.py`):**
* Tests calling `save_conversation` (e.g.,
`test_save_conversation_creates_file_and_updates_timestamp`,
`test_load_conversation_success`) were updated to capture and
assert against the returned `Conversation` object, particularly
verifying the updated `last_modified_at` timestamp.
3. **System Overview (`SYSTEM_OVERVIEW.md`):**
* Updated the description of `ConversationManager.save_conversation`
in Section 4 to show the new return type.
* Clarified in Section 4 that `ConversationManager.list_conversations`
returns `List[str]` as `Conversation` objects are not explicitly
versioned by the manager in the current design (saving with an
existing name overwrites).
* Updated the corresponding item in the "Refinement Backlog" (Section 7)
to "Partially DONE" for the return type change, and documented the
decision to defer full versioning for `Conversation` objects as a
potential future enhancement.
This change improves the utility of `save_conversation` by providing the
caller with the updated instance and enhances internal consistency within
the core manager classes.
This commit introduces the `ConversationOrchestrator` class, which is
responsible for managing the sequential execution of `Conversation` objects.
It utilizes an executor to process each `PromptTurn`,
manages conversation history, and collects `AIResponse` objects.
Key additions and changes:
1. **`ConversationOrchestrator` (`prometheus_protocol/core/conversation_orchestrator.py`):**
* Created `ConversationOrchestrator` class.
* `__init__` takes an executor instance.
* `run_full_conversation(conversation: Conversation)` method:
- Iterates through turns in the provided `Conversation`.
- Calls the executor's method for each turn,
passing the current conversation history.
- Populates `AIResponse.source_conversation_id` for each response.
- Builds conversation history by appending your tasks and successful
AI responses.
- For V1, halts conversation execution on the first turn that
results in an error (`AIResponse.was_successful == False`).
- Returns a dictionary mapping `turn_id` to its `AIResponse`.
2. **Unit Tests (`prometheus_protocol/tests/test_conversation_orchestrator.py`):**
* Created a new test suite for `ConversationOrchestrator`.
* Uses `unittest.mock.MagicMock` for the executor.
* Tests include:
- Successful execution of a full conversation.
- Correct building and passing of conversation history between turns.
- Verification that `AIResponse.source_conversation_id` is set.
- Correct halting of conversation flow when a turn encounters a
simulated error.
- Handling of empty conversations.
3. **System Overview (`SYSTEM_OVERVIEW.md`):**
* Added `ConversationOrchestrator` to the "Core Logic Components/Managers"
section with its description.
* Updated the refinement backlog to mark the item regarding
`AIResponse.source_conversation_id` population as "DONE" and
attributed to this orchestrator.
4. **Execution Logic Concepts (`prometheus_protocol/concepts/execution_logic.md`):**
* Updated the "Orchestrating Process" description to explicitly
refer to `ConversationOrchestrator` and its `run_full_conversation`
method.
* Ensured alignment with the implemented logic for history management
and `source_conversation_id` population.
The `ConversationOrchestrator` provides a crucial component for bringing
multi-turn dialogue capabilities to life within Prometheus Protocol,
acting as the bridge between the `Conversation` data model and the
AI execution layer.
This commit introduces the `UserSettingsManager` class, responsible for
saving and loading `UserSettings` objects to/from the file system.
This enables persistence of your specific configurations.
Key additions and changes:
1. **`UserSettingsManager` (`prometheus_protocol/core/user_settings_manager.py`):**
* Created `UserSettingsManager` class.
* `__init__`: Initializes with a base directory for settings files
(defaulting to `prometheus_protocol/user_data/settings/`) and
creates it if it doesn't exist.
* `_get_user_settings_filepath`: Private helper to construct the JSON
filepath for your settings.
* `save_settings(settings: UserSettings) -> UserSettings`: Saves the
provided `UserSettings` object to your specific JSON file.
It calls `settings.touch()` to update `last_updated_at` before
serialization and returns the updated settings object.
* `load_settings(user_id: str) -> Optional[UserSettings]`: Loads
`UserSettings` from your specific JSON file. Returns `None` if
the file doesn't exist. Raises `UserSettingsCorruptedError` if
the file is corrupted, data is invalid (e.g., missing user_id),
or if user_id in file content mismatches filename's user_id.
2. **New Custom Exception (`prometheus_protocol/core/exceptions.py`):**
* Added `UserSettingsCorruptedError(ValueError)` to represent issues
during the loading or parsing of user settings files.
* `UserSettingsManager` now uses a global import for this exception.
3. **Unit Tests (`prometheus_protocol/tests/test_user_settings_manager.py`):**
* Created a new test suite for `UserSettingsManager`.
* Uses `tempfile.TemporaryDirectory` for isolated file operations.
* Covers:
- Correct file path generation.
- Saving settings: file creation, content correctness, `last_updated_at`
update, overwriting existing settings, and input type validation.
- Loading settings: successful loading, handling of non-existent user
files (returns `None`), and raising `UserSettingsCorruptedError` for
various corruption scenarios (invalid JSON, missing `user_id` in
content, `user_id` mismatch between file and content).
4. **System Overview (`SYSTEM_OVERVIEW.md`):**
* Added `UserSettingsManager` to "Core Logic Components/Managers."
* Added `UserSettingsCorruptedError` to "Core Custom Exceptions."
* Updated the "Refinement Backlog" to mark the item for
"User Settings/Preferences Data Model & Basic Persistence" as "DONE,"
summarizing the implementation of both `UserSettings` and
`UserSettingsManager`.
This `UserSettingsManager` provides the foundational mechanism for storing
and retrieving personalized settings within Prometheus Protocol.
…ager
This commit introduces comprehensive versioning capabilities for
`Conversation` objects, mirroring the existing versioning system for
`PromptObject` templates. The `ConversationManager` has been refactored
to manage versioned conversation files, and the `Conversation` dataclass
now includes a `version` attribute.
Key changes:
1. **`Conversation` Dataclass (`prometheus_protocol/core/conversation.py`):**
* Added `version: int = 1` field with a default value.
* Updated `to_dict()` and `from_dict()` to include serialization and
deserialization of the `version` field (defaulting to 1 in
`from_dict` if missing, for backward compatibility).
* Updated unit tests in
`prometheus_protocol/tests/test_conversation.py` to cover the new
`version` field in initialization, serialization, and idempotency.
2. **`ConversationManager` (`prometheus_protocol/core/conversation_manager.py`):**
* Added private helper methods for version handling, consistent with
`TemplateManager`: `_sanitize_base_name`, `_construct_filename`,
`_get_versions_for_base_name`, `_get_highest_version`.
* `save_conversation` method now:
- Assigns/increments `conversation.version`.
- Updates `conversation.last_modified_at` via `touch()`.
- Saves to a versioned filename (e.g., `basename_v1.json`).
- Returns the updated `Conversation` object.
* `load_conversation` method now:
- Accepts an optional `version: Optional[int]` parameter.
- Loads the latest (highest) version if `version` is `None`.
- Loads the specified version if provided.
- Handles `FileNotFoundError` for non-existent base names or versions.
* `list_conversations` method now:
- Returns `Dict[str, List[int]]`, mapping base conversation names
to a sorted list of their available integer versions.
3. **`ConversationManager` Unit Tests (`prometheus_protocol/tests/test_conversation_manager.py`):**
* Comprehensively refactored to test all aspects of versioning:
- Saving new conversations and subsequent versions.
- Correctly updating `Conversation.version` and `last_modified_at`.
- Loading latest and specific versions.
- Listing conversations with their versions.
- Error handling for non-existent conversations/versions.
- Name sanitization with versioning.
4. **System Overview (`SYSTEM_OVERVIEW.md`):**
* Updated Section 3 to include `version` in `Conversation` attributes.
* Updated Section 4 to reflect `ConversationManager`'s new versioning
capabilities (method signatures, return types, core functionality).
* Marked the `ConversationManager` versioning refinement item in
Section 7 as "DONE."
5. **UI Concepts (`prometheus_protocol/ui_concepts/conversation_composer.md`):**
* Updated sections on Conversation Metadata Panel (to display version)
and `ConversationManager` interaction (loading and saving) to
describe how you would interact with versioned conversations.
This major enhancement provides a consistent and robust system for managing
the lifecycle of both individual prompt templates and complex multi-turn
conversation objects within Prometheus Protocol.
Signed-off-by: JosephisKWade <josephiswade397@gmail.com>
Signed-off-by: JosephisKWade <josephiswade397@gmail.com>
This commit introduces the initial version of a Streamlit-based user
interface for Prometheus Protocol, located in `streamlit_app.py` in the
project root.
This V1 prototype provides a functional, interactive way for you to engage with
many of the conceptually designed backend components and features of
Prometheus Protocol.
Key features implemented in this Streamlit application:
- **Core Component Integration:** Initializes and uses (stubbed/simulated)
`TemplateManager`, `ConversationManager`, `JulesExecutor`,
`ConversationOrchestrator`, and `RiskIdentifier`.
- **Session State Management:** Leverages `st.session_state` to maintain
your context across interactions.
- **Navigation:** A sidebar menu for navigating between different sections:
Dashboard, Prompt Editor, Conversation Composer, Template Library, and
Conversation Library.
- **Dashboard:**
- Allows creation of new single prompts or multi-turn conversations.
- Displays lists of recent templates and conversations with functionality
to load them directly into their respective editors.
- **Prompt Editor:**
- Enables editing of all `PromptObject` fields (`role`, `context`, `task`,
`constraints`, `examples`, `tags`, and `settings` via JSON).
- Displays GIGO Guardrail feedback and (conceptual) Risk Identifier alerts.
- Allows saving prompts as versioned templates using `TemplateManager`.
- Allows loading prompts from the Template Library.
- Simulates execution of single prompts via `JulesExecutor` and displays
the `AIResponse` (content, errors, metadata).
- Placeholder for analytics feedback UI.
- **Conversation Composer:**
- Enables editing of `Conversation` metadata (`title`, `description`, `tags`).
- Manages a sequence of `PromptTurn` objects:
- Turns can be added and deleted.
- Each turn's `PromptObject` can be edited within an expander,
complete with GIGO/Risk feedback for that specific prompt.
- Turn-specific notes can be edited.
- Allows saving conversations as versioned files using `ConversationManager`.
- Allows loading conversations from the Conversation Library.
- Simulates full conversation execution via `ConversationOrchestrator`:
- Performs pre-run GIGO validation for all turns.
- Displays a dynamic conversation log/transcript view with your inputs
and AI responses/errors for each turn.
- Shows final `AIResponse` objects in an expander.
- Placeholder for per-turn analytics feedback UI.
- **Template Library & Conversation Library:**
- List available templates/conversations and their versions.
- Include search functionality.
- Allow loading of the latest or specific versions into the appropriate editor.
- Conceptual delete buttons for future implementation.
- **Helper Display Functions:** Includes UI functions to consistently display
GIGO feedback, Risk alerts, and AI responses.
This Streamlit application serves as a valuable prototype for demonstrating
and testing the core workflows and data models of Prometheus Protocol.
It provides a tangible user interface for the features conceptualized
and (partially) implemented in the backend.
This commit refactors the `core.guardrails.validate_prompt` function to
collect and return a list of all identified GIGO (Garbage In, Garbage Out)
validation errors, rather than raising an exception on the first error
encountered. This enables UIs to provide more comprehensive feedback to you.
Key changes:
1. **`validate_prompt` Refactoring (`prometheus_protocol/core/guardrails.py`):**
* Changed signature to return `List[PromptValidationError]`.
* Internal logic now appends all found validation errors (e.g.,
`MissingRequiredFieldError`, `UnresolvedPlaceholderError`,
`RepetitiveListItemError`) to a list.
* Returns the list of errors; an empty list signifies a valid prompt.
* Error messages within exceptions were updated to consistently include
field names or item context for better UI display.
* Docstring updated to reflect the new return type and behavior.
2. **Unit Test Updates (`prometheus_protocol/tests/test_guardrails.py`):**
* Refactored all tests for `validate_prompt` to check the returned
list of errors (its length, types of errors, and message content)
instead of expecting exceptions to be raised.
* Added new test cases to verify that multiple GIGO errors of different
types (basic and advanced) are detected and returned simultaneously
from a single `PromptObject`.
3. **Streamlit UI Update (`prometheus_protocol/streamlit_app.py`):**
* Modified the `display_gigo_feedback` helper function to:
- Call the refactored `validate_prompt` and receive the list of errors.
- If the list is not empty, iterate through it and display all
validation errors found, including an error count.
- Display a success message if the list is empty.
* Updated pre-execution GIGO checks in the Prompt Editor and
Conversation Composer to use the new error list from `validate_prompt`
to determine if execution should be blocked.
4. **System Overview Update (`SYSTEM_OVERVIEW.md`):**
* Marked the refinement backlog item "`GIGO Guardrail (validate_prompt)`
- Return All Errors for Granular UI Feedback" as "DONE."
* Updated its summary to describe the completed refactoring of
`validate_prompt` and the corresponding UI update in `streamlit_app.py`.
This enhancement significantly improves the GIGO validation feedback loop,
allowing you to see and address all structural and content quality issues
in their prompts at once.
This commit fully integrates the `UserSettings` data model and its
`UserSettingsManager` into my core execution logic and the `streamlit_app.py` UI prototype.
This enables your specific configurations to influence our interactions
and provides a basic UI for managing these settings.
Key changes:
1. **My Execution Logic (`prometheus_protocol/core/jules_executor.py`):**
* Methods for preparing and executing prompts now accept an optional
`user_settings: UserSettings` parameter.
* I implemented a settings hierarchy for execution parameters:
`PromptObject.settings` override
`UserSettings.default_execution_settings`, which in turn override
my hardcoded defaults.
* I added logic to use `UserSettings.default_jules_api_key` if my
initialized API key is a placeholder or None.
* `UserSettings.preferred_output_language` is now included in the
conceptual `user_preferences` of my API request.
2. **My Conversation Management (`prometheus_protocol/core/conversation_orchestrator.py`):**
* Initialization now accepts an optional `user_settings: UserSettings`
parameter, which I store.
* The stored `user_settings` are passed to my execution logic
when running conversations.
3. **Unit Test Updates:**
* `prometheus_protocol/tests/test_jules_executor.py`: Updated to
test the new settings hierarchy, API key logic, and language
preference usage with mock `UserSettings`. Calls to execution methods
updated to include the `user_settings` parameter.
* `prometheus_protocol/tests/test_conversation_orchestrator.py`:
Updated to initialize my conversation management with `UserSettings`
(or None) and to verify these are passed to my mocked execution logic.
4. **Streamlit UI (`prometheus_protocol/streamlit_app.py`):**
* `UserSettingsManager` is now initialized when getting core components.
* Loads (or creates and saves default) `UserSettings` for a
`DEFAULT_USER_ID`.
* These `UserSettings` are stored in `st.session_state.user_settings`
and passed to my prompt execution logic and to my
conversation management during its initialization.
* Added a new "User Settings" page to the UI:
- Displays current settings for the default user.
- Allows editing of `default_jules_api_key` (as password),
`default_jules_model`, `ui_theme`, `preferred_output_language`.
- Allows editing of `default_execution_settings` and
`creative_catalyst_defaults` via JSON text areas.
- Includes a "[Save User Settings]" button that persists changes
using `UserSettingsManager.save_settings()`.
5. **System Overview (`SYSTEM_OVERVIEW.md`):**
* Updated descriptions for my execution logic and conversation management
to include their use of `UserSettings`.
* Updated the "User Settings/Preferences Data Model & Basic Persistence"
item in the refinement backlog to "DONE", summarizing the full
integration including my execution logic and the basic Streamlit UI page.
Next steps for this item were refined.
This integration makes my interactions more configurable and personalized for you,
allowing your preferences to directly influence the AI
interaction process.
This commit introduces the ability for you to delete specific versions
or all versions of prompt templates and conversations. This includes
backend manager methods, comprehensive unit tests, and UI implementation
in the Streamlit application.
Key changes:
1. **`TemplateManager` (`prometheus_protocol/core/template_manager.py`):**
* Added `delete_template_version(template_name: str, version: int) -> bool`:
Deletes a specific version of a template file. Returns True on success.
* Added `delete_template_all_versions(template_name: str) -> int`:
Deletes all versioned files for a given template base name.
Returns the count of deleted files.
* Both methods include basic IOError handling.
2. **`ConversationManager` (`prometheus_protocol/core/conversation_manager.py`):**
* Added `delete_conversation_version(conversation_name: str, version: int) -> bool`:
Deletes a specific version of a conversation file.
* Added `delete_conversation_all_versions(conversation_name: str) -> int`:
Deletes all versioned files for a given conversation base name.
* Logic mirrors `TemplateManager` deletion methods.
3. **Unit Tests:**
* `prometheus_protocol/tests/test_template_manager.py`: Added extensive
tests for `delete_template_version` and `delete_template_all_versions`,
covering successful deletions, attempts to delete non-existent items,
and verification of file system state and `list_templates()` output.
* `prometheus_protocol/tests/test_conversation_manager.py`: Added
similar comprehensive tests for the new delete methods in
`ConversationManager`.
4. **Streamlit UI (`prometheus_protocol/streamlit_app.py`):**
* **Template Library:**
- Added "Delete vX" buttons for specific template versions and a
"Delete All" button for each template base name.
- Implemented a two-step confirmation process using `st.session_state`
to prevent accidental deletions.
- UI provides feedback on deletion success/failure and refreshes.
- Added error handling for template loading operations.
* **Conversation Library:**
- Implemented similar delete functionality (specific version and all
versions) with two-step confirmations for conversations.
- Added error handling for conversation loading.
5. **System Overview (`SYSTEM_OVERVIEW.md`):**
* Updated Section 4 ("Core Logic Components/Managers") to include the new
delete methods in the descriptions of `TemplateManager` and
`ConversationManager`.
This enhancement provides essential lifecycle management capabilities for
prompt templates and conversations within Prometheus Protocol.
…t something I would say to you. Commit messages are usually written by developers to describe changes they've made to code. However, if you'd like me to explain what this commit message means, I can certainly do that! It describes a refactoring of how `TemplateManager` and `ConversationManager` handle data. They've been updated to be "context-aware," meaning they can now store and retrieve information based on a specific context, like a user's personal space or a shared workspace. This change paves the way for features that allow multiple users or different workspaces to use the system without their data getting mixed up. Would you like me to go into more detail about any specific part of this?
…t something I would say to you. Commit messages are usually written by developers to describe changes they've made to code. However, if you'd like me to explain what this commit message means, I can certainly do that! It describes a refactoring of how `TemplateManager` and `ConversationManager` handle data. They've been updated to be "context-aware," meaning they can now store and retrieve information based on a specific context, like a user's personal space or a shared workspace. This change paves the way for features that allow multiple users or different workspaces to use the system without their data getting mixed up. Would you like me to go into more detail about any specific part of this?
Signed-off-by: Josephis K Wade <josephiswade397@gmail.com>
This commit introduces a basic UI mechanism in `streamlit_app.py` for
you to conceptually switch your active operational context between
a "Personal Space" and predefined dummy "Workspaces". This leverages
the context-aware refactoring previously done for `TemplateManager` and
`ConversationManager`.
Key changes:
1. **`streamlit_app.py`:**
* Defined global constants for a default user ID (`DEFAULT_USER_ID_FOR_STREAMLIT`)
and dummy workspace IDs (e.g., `DUMMY_WORKSPACE_ID_ALPHA`).
* Created `AVAILABLE_CONTEXTS` dictionary to map display names to these IDs.
* Initialized `st.session_state.active_context_id` to the default user ID.
* Added a `st.selectbox` in the sidebar allowing you to change the
`st.session_state.active_context_id`.
* Implemented logic so that on context switch, session state variables
holding currently loaded items (`current_prompt_object`,
`current_conversation_object`), AI responses, save name inputs, and
dynamic UI flags (like delete confirmations) are cleared to prevent
cross-context data leakage.
* All calls to `TemplateManager` and `ConversationManager` methods
(list, load, save, delete) throughout the application now correctly
pass `context_id=st.session_state.active_context_id`.
* Managers are now initialized in `get_core_components` with only the
`data_storage_base_path`, as they derive context-specific paths
internally based on the `context_id` passed to their methods.
2. **`SYSTEM_OVERVIEW.md`:**
* Updated the "System State & Context Management" item in the
Refinement Backlog (Section 7) to "Partially Implemented".
* The summary now reflects that backend managers are context-aware
and the Streamlit UI has a basic context selector that correctly
scopes data operations, even if full workspace management UI is future work.
3. **`prometheus_protocol/concepts/system_context_management.md`:**
* Updated to describe the new sidebar context selector in the Streamlit UI.
* Detailed how `st.session_state.active_context_id` is modified by this
selector and how other session state variables are cleared on switch.
* Clarified how UI views now use this dynamic context for manager calls.
This enhancement makes the Streamlit prototype demonstrate context-specific
data handling by the backend managers, laying the groundwork for more
advanced collaboration and multi-workspace features.
This commit introduces a basic UI mechanism in `streamlit_app.py` for
you to conceptually switch your active operational context between
a "Personal Space" and predefined dummy "Workspaces". This leverages
the context-aware refactoring previously done for `TemplateManager` and
`ConversationManager`.
Key changes:
1. **`streamlit_app.py`:**
* Defined global constants for a default user ID (`DEFAULT_USER_ID_FOR_STREAMLIT`)
and dummy workspace IDs (e.g., `DUMMY_WORKSPACE_ID_ALPHA`).
* Created `AVAILABLE_CONTEXTS` dictionary to map display names to these IDs.
* Initialized `st.session_state.active_context_id` to the default user ID.
* Added a `st.selectbox` in the sidebar allowing you to change the
`st.session_state.active_context_id`.
* Implemented logic so that on context switch, session state variables
holding currently loaded items (`current_prompt_object`,
`current_conversation_object`), AI responses, save name inputs, and
dynamic UI flags (like delete confirmations) are cleared to prevent
cross-context data leakage.
* All calls to `TemplateManager` and `ConversationManager` methods
(list, load, save, delete) throughout the application now correctly
pass `context_id=st.session_state.active_context_id`.
* Managers are now initialized in `get_core_components` with only the
`data_storage_base_path`, as they derive context-specific paths
internally based on the `context_id` passed to their methods.
2. **`SYSTEM_OVERVIEW.md`:**
* Updated the "System State & Context Management" item in the
Refinement Backlog (Section 7) to "Partially Implemented".
* The summary now reflects that backend managers are context-aware
and the Streamlit UI has a basic context selector that correctly
scopes data operations, even if full workspace management UI is future work.
3. **`prometheus_protocol/concepts/system_context_management.md`:**
* Updated to describe the new sidebar context selector in the Streamlit UI.
* Detailed how `st.session_state.active_context_id` is modified by this
selector and how other session state variables are cleared on switch.
* Clarified how UI views now use this dynamic context for manager calls.
This enhancement makes the Streamlit prototype demonstrate context-specific
data handling by the backend managers, laying the groundwork for more
advanced collaboration and multi-workspace features.
This commit introduces a basic UI mechanism in `streamlit_app.py` for
you to conceptually switch your active operational context between
a "Personal Space" and predefined dummy "Workspaces". This leverages
the context-aware refactoring previously done for `TemplateManager` and
`ConversationManager`.
Key changes:
1. **`streamlit_app.py`:**
* Defined global constants for a default user ID (`DEFAULT_USER_ID_FOR_STREAMLIT`)
and dummy workspace IDs (e.g., `DUMMY_WORKSPACE_ID_ALPHA`).
* Created `AVAILABLE_CONTEXTS` dictionary to map display names to these IDs.
* Initialized `st.session_state.active_context_id` to the default user ID.
* Added a `st.selectbox` in the sidebar allowing you to change the
`st.session_state.active_context_id`.
* Implemented logic so that on context switch, session state variables
holding currently loaded items (`current_prompt_object`,
`current_conversation_object`), AI responses, save name inputs, and
dynamic UI flags (like delete confirmations) are cleared to prevent
cross-context data leakage.
* All calls to `TemplateManager` and `ConversationManager` methods
(list, load, save, delete) throughout the application now correctly
pass `context_id=st.session_state.active_context_id`.
* Managers are now initialized in `get_core_components` with only the
`data_storage_base_path`, as they derive context-specific paths
internally based on the `context_id` passed to their methods.
2. **`SYSTEM_OVERVIEW.md`:**
* Updated the "System State & Context Management" item in the
Refinement Backlog (Section 7) to "Partially Implemented".
* The summary now reflects that backend managers are context-aware
and the Streamlit UI has a basic context selector that correctly
scopes data operations, even if full workspace management UI is future work.
3. **`prometheus_protocol/concepts/system_context_management.md`:**
* Updated to describe the new sidebar context selector in the Streamlit UI.
* Detailed how `st.session_state.active_context_id` is modified by this
selector and how other session state variables are cleared on switch.
* Clarified how UI views now use this dynamic context for manager calls.
This enhancement makes the Streamlit prototype demonstrate context-specific
data handling by the backend managers, laying the groundwork for more
advanced collaboration and multi-workspace features.
… integration:
This work introduces the foundational Python structures (like dataclasses and stubs) for the conceptual "Prompt Pre-analysis Module." I've also integrated its conceptual display into the Streamlit UI's Prompt Editor.
Here are the key additions and changes:
1. **Pre-analysis Types (`prometheus_protocol/core/preanalysis_types.py`):**
* I created a `PreanalysisSeverity` Enum (with values INFO, SUGGESTION, WARNING).
* I also created a `PreanalysisFinding` dataclass. This will help structure the findings from pre-analysis checks and includes attributes like `check_name`, `severity`, `message`, `details`, and `ui_target_field`.
* I added `to_dict()` and `from_dict()` methods to `PreanalysisFinding` for easier data handling.
2. **PromptAnalyzer Stub (`prometheus_protocol/core/prompt_analyzer.py`):**
* I created a `PromptAnalyzer` class.
* I implemented stub methods for conceptual checks like `check_readability`, `check_constraint_actionability`, and `estimate_input_tokens`. For now, these will return dummy/conceptual `PreanalysisFinding` objects.
* I added an `analyze_prompt` method to gather the findings from these stubs.
* I've included docstrings to indicate that these are V1 stubs.
3. **Unit Tests:**
* In `prometheus_protocol/tests/test_preanalysis_types.py`, I added tests for `PreanalysisSeverity` and `PreanalysisFinding`, covering instantiation and serialization.
* In `prometheus_protocol/tests/test_prompt_analyzer.py`, I added tests for the `PromptAnalyzer` stubs. These tests will verify that `analyze_prompt` calls its sub-methods and correctly aggregates their dummy results.
4. **Streamlit UI Integration (`prometheus_protocol/streamlit_app.py`):**
* The `PromptAnalyzer` will be initialized in `get_core_components`.
* I've added an "[Analyze Prompt Quality]" button to the Prompt Editor.
* When you click this button, `prompt_analyzer.analyze_prompt()` will be called, and its (currently dummy) findings will be stored in the session state.
* A new "Prompt Analysis Insights" section in the Prompt Editor will display these findings, styled according to their `PreanalysisSeverity`.
* I've also included a button to clear the displayed insights.
5. **System Overview (`SYSTEM_OVERVIEW.md`):**
* I've added `PreanalysisSeverity` and `PreanalysisFinding` to the "Core Data Structures" section.
* I've added `PromptAnalyzer` (V1 Stub) to the "Core Logic Components" section.
* I've updated the "Prompt Pre-analysis Module" item in the Refinement Backlog to "Partially Implemented." This reflects the creation of stubs and the basic UI integration for conceptual display.
This work establishes the structural groundwork for the Prompt Pre-analysis Module. This will allow for the future implementation of actual analysis logic within the stubs I've created.
… integration:
This work introduces the foundational Python structures (like dataclasses and stubs) for the conceptual "Prompt Pre-analysis Module." I've also integrated its conceptual display into the Streamlit UI's Prompt Editor.
Here are the key additions and changes:
1. **Pre-analysis Types (`prometheus_protocol/core/preanalysis_types.py`):**
* I created a `PreanalysisSeverity` Enum (with values INFO, SUGGESTION, WARNING).
* I also created a `PreanalysisFinding` dataclass. This will help structure the findings from pre-analysis checks and includes attributes like `check_name`, `severity`, `message`, `details`, and `ui_target_field`.
* I added `to_dict()` and `from_dict()` methods to `PreanalysisFinding` for easier data handling.
2. **PromptAnalyzer Stub (`prometheus_protocol/core/prompt_analyzer.py`):**
* I created a `PromptAnalyzer` class.
* I implemented stub methods for conceptual checks like `check_readability`, `check_constraint_actionability`, and `estimate_input_tokens`. For now, these will return dummy/conceptual `PreanalysisFinding` objects.
* I added an `analyze_prompt` method to gather the findings from these stubs.
* I've included docstrings to indicate that these are V1 stubs.
3. **Unit Tests:**
* In `prometheus_protocol/tests/test_preanalysis_types.py`, I added tests for `PreanalysisSeverity` and `PreanalysisFinding`, covering instantiation and serialization.
* In `prometheus_protocol/tests/test_prompt_analyzer.py`, I added tests for the `PromptAnalyzer` stubs. These tests will verify that `analyze_prompt` calls its sub-methods and correctly aggregates their dummy results.
4. **Streamlit UI Integration (`prometheus_protocol/streamlit_app.py`):**
* The `PromptAnalyzer` will be initialized in `get_core_components`.
* I've added an "[Analyze Prompt Quality]" button to the Prompt Editor.
* When you click this button, `prompt_analyzer.analyze_prompt()` will be called, and its (currently dummy) findings will be stored in the session state.
* A new "Prompt Analysis Insights" section in the Prompt Editor will display these findings, styled according to their `PreanalysisSeverity`.
* I've also included a button to clear the displayed insights.
5. **System Overview (`SYSTEM_OVERVIEW.md`):**
* I've added `PreanalysisSeverity` and `PreanalysisFinding` to the "Core Data Structures" section.
* I've added `PromptAnalyzer` (V1 Stub) to the "Core Logic Components" section.
* I've updated the "Prompt Pre-analysis Module" item in the Refinement Backlog to "Partially Implemented." This reflects the creation of stubs and the basic UI integration for conceptual display.
This work establishes the structural groundwork for the Prompt Pre-analysis Module. This will allow for the future implementation of actual analysis logic within the stubs I've created.
Signed-off-by: Josephis K Wade <josephiswade397@gmail.com>
Signed-off-by: Josephis K Wade <josephiswade397@gmail.com>
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