- Updated test files to import `AgentSessionLocal` from `advisor_db` instead of directly, preventing session binding to the real database during tests.
- Fixed 6 test cases to use the new login token utility, ensuring consistency across authentication methods.
- Adjusted customer risk codes in `test_convert_confirm.py` to reflect changes in customer classification (C3 to C4).
- Verified that changes resulted in zero database pollution during test runs, maintaining integrity of the testing environment.
- Documented findings and updates in the relevant test logs and memory files, ensuring clarity on the current state of tests and defects.
- Introduced `_recent_days_series_hint` to handle queries related to "最近N天" for time series data aggregation.
- Added `_cn_num_to_int` function to convert Chinese numerals to integers for better query parsing.
- Updated `_nl_sql_hints` to incorporate the new hint generation logic, ensuring accurate SQL output for recent days queries.
- Enhanced documentation to reflect these changes and improve clarity on the new functionalities.
- Completed 203 test cases with 162 passing, 6 failing, and 2 skipped, identifying 2 critical defects (P0: authentication privilege escalation, P1: suitability disclosure not enforced).
- Enhanced test coverage by verifying front-end rendering values against back-end raw responses, addressing previous gaps in validation.
- Documented findings and defects in the new test log and README files, ensuring clarity on test outcomes and areas for improvement.
- Updated the TODO list to reflect the current state of defects and testing priorities, emphasizing the need for immediate attention to identified issues.
This round of testing significantly improves the robustness of the customer trade functionality, ensuring compliance and security standards are met.
- Introduced `pending_trade` handling in the chat API to manage trade requests more effectively.
- Updated the `submit_trade_api` to allow advisors to access customer trades based on assigned roles.
- Added new methods in `GatewayRepository` for managing core holdings during trade subscriptions and redemptions.
- Implemented context-aware trade dialogue management in the customer service layer to improve user experience during multi-turn interactions.
- Enhanced the tool service to support trade actions and suitability checks, ensuring accurate processing of user requests.
This update significantly improves the trade interaction flow, providing a more robust and user-friendly experience for customers engaging in trading activities.
- Introduced `convert_meta_api` endpoint to fetch the latest NAV date for conversion processes, restricted to users with the "risk_officer" role.
- Updated `created_at` field in `AgentMessage` to use UTC timezone for consistency in timestamp handling.
- Added `get_max_product_nav_date` method in `CoreReadOnlyRepository` to support the new API functionality.
- Enhanced Milvus template loading in `MilvusTemplateVectorStore` to ensure collections are loaded when they exist.
This update improves the API's capability to handle conversion metadata and ensures accurate timestamp management across the application.
- Merged `xinghuo/advisor-agent` into the `merger` branch, establishing a new API prefix `/api/advisor-agent` to avoid conflicts with existing endpoints.
- Implemented a comprehensive implementation plan detailing the integration steps, including conflict resolution, database migrations, and service adaptations.
- Introduced new files for advisor compliance, script templates, KYC, and market alerts, ensuring a cohesive addition to the existing architecture.
- Updated documentation to reflect the new structure and integration process, enhancing clarity for future development and maintenance.
This integration significantly expands the capabilities of the merger, providing a robust framework for advisor-related functionalities while maintaining compliance and operational integrity.
- Introduced a new `/analyze` endpoint in the analyst API to process analysis requests, allowing users to receive textual interpretations and chart specifications based on provided prompts.
- Enhanced `analyst_schemas.py` with `AnalyzeRequest` and `ChartSpec` models to structure analysis requests and validate chart specifications.
- Implemented chart validation logic in a new `analyst_chart.py` service, ensuring that chart types and fields are correctly specified and conform to allowed values.
- Updated `AnalystAgent` to handle analysis requests, integrating the new logic for generating responses based on user prompts and data availability.
- Added unit tests to verify the functionality of the new endpoint and validation mechanisms, ensuring robustness and reliability.
This update significantly enhances the analytical capabilities of the application, providing users with improved tools for data interpretation and visualization.
- 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.
- Introduced a new course section on architecture governance, including modules on ADR (Architecture Decision Record) and invariants.
- Created multiple HTML modules detailing architecture principles, decision-making processes, and compliance mechanisms.
- Implemented interactive elements such as quizzes and flow animations to enhance user engagement and learning experience.
- Added supporting files including styles, scripts, and base templates to ensure a cohesive design and functionality across the course materials.
This update significantly enriches the educational content available for architecture governance, providing a structured approach to understanding key concepts and practices.
- 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 `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 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.
- Established the P0 scaffolding for the frontend, including login, layout, and routing for four roles.
- Integrated the Customer Wealth Dashboard, Advisor Clients Dashboard, Analyst Market Dashboard, and Risk Alerts Dashboard.
- Updated the API client to support fetching customer and product data, enhancing the overall functionality of the dashboard.
- Added error handling components to improve user experience during data fetching.
- Enhanced charting capabilities using Ant Design Charts for better data visualization.
This update lays the groundwork for further development of the frontend application, ensuring a robust structure for future features and integrations.
- Enhanced AGENTS.md to include new draft specifications for market data in Phase B.
- Updated MEMORY.md with details on the C-05 market data source selection and the new API contract for v0.2.
- Introduced a new document for frontend P0 design specifications, outlining the architecture and features for the initial web application.
- Added a new document for C-05 market data source comparison, detailing the requirements and options for future data integration.
- Updated existing API contracts to reflect the latest changes and ensure consistency across documentation.
This update improves the clarity and comprehensiveness of the API documentation, supporting ongoing development efforts and future integrations.
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