- 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.
Dev login no longer honors injected roles; self-service simulate/convert
blocks R4 disclosure grades like the UI and chat already do. Reuse of a
convert idempotency key with a different body returns 409. CT6 redeem qty
is derived from T+2 lots instead of a fixed 35000; CT7-05 and CT10-07
assertions follow. Update project memory and v1.1 post-fix test artifacts.
- 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.
- Updated KYC session endpoints to remove the ApiResponse response model, simplifying the API structure.
- Enhanced clarity in the KYC session creation, retrieval, chat, and completion methods by focusing on the payload and response data directly.
- Improved code readability and maintainability by streamlining the endpoint definitions.
- Added new modules for advisor compliance, KYC sessions, and script templates, enhancing the advisor agent's capabilities.
- Implemented a comprehensive API structure under the `/api/advisor-agent` prefix, ensuring clear organization and access to new features.
- Established database models and repositories for compliance rules and KYC sessions, facilitating robust data management.
- Integrated exception handling and response models to improve error management and user feedback.
- Updated settings to include new configurations for compliance and KYC features, ensuring flexibility and adaptability.
This update significantly expands the advisor agent's functionality, providing essential tools for compliance and customer interaction while maintaining a structured API design.
- Added new endpoints to the analyst API for managing assets, including `GET /assets` to list assets and `POST /assets/{kind}/{asset_id}/publish` to publish assets.
- Introduced `DictAmbiguityCheckRequest` schema for checking metric ambiguities, enhancing the analyst's ability to clarify definitions and aliases.
- Implemented `detect_dict_ambiguity` function to analyze potential ambiguities in metrics, providing structured feedback for users.
- Updated `AnalystAgent` to support the new asset management functionalities and ambiguity detection logic, improving overall user experience.
- Enhanced existing schemas and services to accommodate new features, ensuring robust data handling and validation.
This update significantly improves the analyst API's capabilities, allowing for better asset management and clarity in metric definitions.
- 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.
- 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.
- 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.
zhanghongyu_0626
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