- Introduced new endpoints `/api/analyst/query/{trace_id}/sample` and `/api/analyst/escalate` for sampling query results and escalating issues to human analysts, respectively.
- Enhanced `AnalystAgent` to support sampling of SQL results based on trace ID and to handle escalation requests, improving user experience in error scenarios.
- Updated `analyst_schemas.py` to include `EscalateRequest` for structured escalation requests.
- Added corresponding frontend API calls and UI components to facilitate user interactions with the new features.
- Implemented unit tests to ensure the reliability of the new functionalities.
This update significantly enhances the analytical capabilities of the application, allowing users to retrieve detailed query samples and escalate issues effectively.
- Enhanced the `AnalystAgent` class to include an `_audit_terminal` method for logging query denials, clarifications, and errors, ensuring compliance and traceability.
- Updated error handling paths to call the new audit method, capturing relevant details such as question, user authentication, and SQL context.
- Introduced new validation checks in `sql_guard.py` to enforce ownership filters for sensitive queries, improving security measures.
- Added unit tests to verify the correct logging behavior and ownership filter enforcement, ensuring robust functionality.
This update significantly strengthens the auditing capabilities of the analyst agent, enhancing security and compliance in query handling.
- Added `interpret` flag to `AnalystChatRequest` for optional immediate interpretation of queries.
- Implemented new `POST /api/analyst/interpret` endpoint for on-demand data interpretation based on the latest query snapshot.
- Updated `AnalystAgent` to handle interpretation logic, including error handling and response formatting.
- Enhanced `AnalystQueryPage` to include a button for triggering interpretations, improving user interaction.
- Updated frontend API calls to support the new interpret functionality, ensuring seamless integration with existing workflows.
This update significantly enhances the analytical capabilities of the application, allowing users to request interpretations of their queries directly.
- Introduced `TemplateService` for managing SQL templates, allowing for parameterized queries based on user input.
- Added functionality to automatically reload templates upon asset creation in `analyst.py`.
- Enhanced `CacheService` to support table generation bumping, ensuring cache invalidation on data changes.
- Updated `RiskRepository` and `GatewayRepository` to trigger cache invalidation for relevant operations.
- Expanded `analyst_schemas.py` to include new fields for template tracking in response metadata.
- Created seed SQL script for populating initial templates and added unit tests for template rendering logic.
This update significantly improves the efficiency of query handling by leveraging SQL templates, reducing reliance on LLM for common queries.
- Introduced `analyst_auth_adapter.py` for managing authentication context and access control for the data analysis agent.
- Added new API endpoints in `analyst.py` for chat, dashboard, asset management, and metrics, utilizing the new authentication context.
- Created Pydantic models in `analyst_schemas.py` for request and response structures, ensuring consistent data handling.
- Updated SQL guard logic in `sql_guard.py` to enforce access restrictions based on user roles and contexts.
- Implemented migration scripts for new database tables related to the data analysis agent, enhancing data management capabilities.
- Removed legacy authentication code from `auth.py`, streamlining the authentication process.
This update significantly enhances the data analysis capabilities, providing a robust framework for querying and managing data securely.
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