- Updated `ready.py` to include a health check for the new `products_nav_history` endpoint, ensuring system readiness.
- Enhanced the `ProductNavChartPanel` component to visualize historical NAV data with various charting options, including line and column charts.
- Introduced new utility functions for filtering and aggregating NAV data, improving data handling in the frontend.
- Updated tests to verify the inclusion of the new `products_nav_history` check in the API response.
- Improved documentation to reflect recent changes and the new testing baseline of 833 passed tests, indicating enhanced stability.
This update significantly improves the product API by providing access to historical NAV data and enhancing user insights through visualizations.
- Added `list_nav_history` method in `CoreReadOnlyRepository` to retrieve historical NAV data for products over a specified number of days.
- Introduced `get_nav_history` endpoint in `product_service` to return historical NAV sequences, improving product data accessibility.
- Updated `products.py` to include new API endpoint for fetching NAV history, ensuring compliance with access control.
- Enhanced `ready.py` to include a health check for the new functionality, ensuring system readiness.
- Updated documentation to reflect the new testing baseline of 833 passed tests, indicating improved stability and functionality across the application.
This update significantly enhances the product API by providing access to historical NAV data, improving user insights into product performance.
- Updated `RiskListAccess` and `ThresholdWriteAccess` to enforce access control in the risk repository and threshold repository, ensuring only authorized roles can perform sensitive operations.
- Introduced new methods in `RiskRepository` for counting pending alerts and listing alerts with access checks, improving data security and compliance.
- Enhanced the `chat.py` and `deps.py` files to integrate compliance roles into the risk management matrix, allowing for more granular access control.
- Updated documentation to reflect the new testing baseline of 825 passed tests, indicating improved stability and functionality across the application.
This update significantly strengthens the risk management capabilities, ensuring robust access control and compliance with organizational policies.
- 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.
- Added `status` query parameter to `list_sessions_api` for filtering sessions by their status (active/closed).
- Introduced `close_all_sessions_api` endpoint to allow users to close all active sessions for the current actor.
- Updated `SessionRepository` to support status filtering in session listing and implemented logic for closing active sessions.
- Improved Redis connection settings for better performance and reliability.
This update enhances the chat functionality by providing more control over session management, improving user experience and system efficiency.
- 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 `battery_report.json` for local data analysis, excluding it from the database.
- Enhanced `AGENTS.md` to reflect updated test baseline with 795 passed tests.
- Added new metrics for trade flow in `dict_service.py`, improving transaction data analysis.
- Updated regex patterns in `guardrail.py` to better handle numeric extraction and prevent misinterpretation of tokens.
- Expanded course modules with new content on FR-8/9/10 capabilities and L3 role management.
- Improved concurrency handling in transaction processing to ensure accurate alert generation.
This update enhances data analysis capabilities and improves the overall structure and clarity of course materials.
- Added `ThresholdRepository` for managing customer loss threshold configurations and notifications.
- Introduced `threshold_service` to handle loss threshold alerts based on customer portfolio performance.
- Enhanced `customer_prompts` to include new intent for querying product net values.
- Updated `customer_service` to integrate new threshold alert functionality into existing workflows.
- Implemented `sanitize_postprocess` for improved compliance handling in customer interactions.
- Enhanced course documentation to reflect updates in advisor training modules and interactive elements.
This update significantly improves the customer experience by providing proactive loss threshold notifications and enhancing the overall service framework.
- Updated the `dashboard` function in `analyst.py` to include additional metrics for different user roles, improving data visibility for analysts, customers, advisors, and risk officers.
- Introduced a new `prepare_customer_stream` function in `customer_service.py` to facilitate streaming responses for customer interactions, enhancing the chat experience.
- Added new API endpoints in `analyst.ts` for fetching dashboard metrics and managing analyst assets, streamlining data handling and user interactions.
- Updated frontend components to support new dashboard features and asset management, ensuring a cohesive user experience across the application.
This update significantly improves the functionality and usability of the analyst and customer service features, providing users with enhanced tools for data analysis and interaction.
- Replaced `get_auth_context` with `get_platform_auth_context` in `analyst.py` to enhance authentication handling.
- Added a new smoke test script `smoke_analyst.py` for testing the data analysis agent with both fake and live LLM configurations.
- Updated TODO documentation to reflect the completion of Scope B smoke tests, ensuring clarity on testing status.
This update improves the authentication mechanism and introduces a comprehensive testing approach for the data analysis agent.
- 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.
- Changed Redis port mapping in `docker-compose.yml` from `6379:6379` to `6380:6379` to avoid conflicts with Windows Redis.
- Updated `.env.example` to reflect the new Redis URL (`redis://127.0.0.1:6380/0`), ensuring proper configuration for Docker users.
- Enhanced Redis client initialization in `database.py` and `redis_gateway.py` to utilize a new `_redis_kwargs` function for improved compatibility with Windows Redis 3.x and Docker Redis 7.
- Added a new PowerShell script `start-redis.ps1` to facilitate starting Redis in Docker, providing a seamless setup experience for developers.
This update significantly improves the Redis integration, ensuring a smoother development process and better compatibility across environments.
- Added new configuration for knowledge base root directory in `.env.example` and `settings.py`.
- Implemented `find_products` method in `CoreReadOnlyRepository` for fuzzy product search based on user queries.
- Introduced `search_cs_knowledge` function in `rag_service.py` to facilitate semantic search across new `fin_*` collections.
- Updated document parsing to support Markdown and YAML front-matter for knowledge base entries.
- Created multiple new FAQ and policy documents in the `data/kb_collections` directory to enrich the knowledge base.
This update significantly improves the knowledge retrieval capabilities for customer service interactions, ensuring more relevant and accurate responses.
- Added a new visitor chat API endpoint (`/api/chat/visitor`) to allow unauthenticated users to engage in conversations without requiring customer data.
- Introduced a visitor context dependency to manage visitor interactions seamlessly.
- Enhanced the chat API to support explicit session termination and improved response handling for customer service interactions.
- Updated the database configuration to include Redis client support for caching visitor data.
- Added a new customer note repository to persist user notes independently of the L1 profile slots.
This update significantly improves the customer service experience by enabling visitor interactions and ensuring efficient data handling for both registered and unregistered users.
- Introduced a new function `_assistant_content_for_persist` to append disclaimers to assistant responses, ensuring consistency in stored content.
- Updated `chat_api` and `chat_stream_api` to utilize the new function for managing assistant replies, improving data integrity.
- Added tests to verify the correct handling of disclaimers in various scenarios, ensuring compliance with expected output.
This update enhances the chat system's reliability by ensuring that disclaimers are consistently applied to assistant responses, improving user experience and data accuracy.
- Modified `chat_stream_api` to use `new_trace()` instead of an empty string for `trace_id`, enhancing traceability.
- Added tests to verify the correct generation and propagation of `trace_id` in responses, ensuring no empty trace IDs are sent to clients.
This update improves the tracking of chat sessions and ensures compliance with traceability standards.
- Updated the login endpoint to utilize shared JWT issuer/audience settings, improving consistency across modules.
- Introduced error handling for unknown accounts during token issuance, raising an UnauthorizedError when necessary.
- Enhanced traceability by adding trace and request IDs to responses, ensuring better tracking of requests.
- Refactored exception handling in middleware to properly bubble up application-specific errors, preventing them from being swallowed.
- Added new utility functions for generating trace headers to improve debugging capabilities.
This update strengthens the authentication process and enhances error visibility, contributing to a more robust and maintainable codebase.
- Added `auth.py` for mock login and JWT issuance.
- Introduced `chat.py` for handling chat requests with role-based access control.
- Enhanced `main.py` to include new routers and middleware for tracing.
- Implemented input validation in `input_guard.py` to prevent SQL injection.
- Created repositories for managing agent sessions and audit logs.
- Added exception handling for authorization errors.
- Updated settings to include JWT configuration.
- Introduced tests for authentication and input validation.
- Implemented `check_suitability` method in `CoreReadOnlyRepository` for suitability determination based on customer and product risk levels.
- Added `build_suitability_log_row` function in `suitability.py` for mapping suitability check results to `risk_suitability_log`.
- Updated `AGENTS.md`, `ENVIRONMENT.md`, and `FLOW.md` to reflect changes in suitability assessment processes and documentation.
- Revised `FRAMEWORK.md` and `MEMORY.md` to clarify project structure and data flow related to suitability checks.
- Expanded `TODO.md` with tasks related to logging and auditing suitability assessments.
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