- 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.
- Introduced `build_all.py` script to automate the assembly of course modules into a single `index.html` file.
- Created `index.html` for the main course overview, featuring a structured layout and navigation for various modules.
- Developed `_base.html` and `_footer.html` templates for the advisor module, ensuring consistent styling and structure.
- Added `build.sh` script for individual module assembly, enhancing modularity and ease of updates.
- Implemented multiple module HTML files detailing specific training scenarios and functionalities for advisors, including interactive elements and quizzes.
This update significantly enhances the course delivery framework, providing a comprehensive and interactive learning experience for advisors.
- 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.
- Introduced new chat components including `ChatPanel` and `VisitorChatWidget` to facilitate real-time conversations for customers and visitors.
- Developed a robust chat API with endpoints for synchronous and streaming interactions, enhancing user engagement.
- Added new pages for customer interactions, including `CustomerChatPage`, `CustomerProfilePage`, `CustomerHoldingsPage`, and `CustomerTradesPage`, integrating chat capabilities seamlessly.
- Enhanced the advisor experience with the `AdvisorCustomersPage` and `RiskAlertsPage`, allowing for efficient management of customer interactions and risk alerts.
- Updated routing and state management to support new chat functionalities, ensuring a cohesive user experience across the application.
This update significantly improves the chat capabilities within the application, providing users with enhanced tools for communication and support.
- 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.
- 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.
- 新增《接口契约-代销平台API-v0.1.md》，定义代销平台 REST API 的路由风格、命名规范及端点清单。
- 更新 AGENTS.md，包含代销平台 API 契约的路径信息。
- 修改 ITERATION.md 和 MEMORY.md，反映代销平台 API 的实施进度及相关文档的状态。
- 更新 TODO.md，明确代销平台 API 的实现优先级。
此更新为代销平台 API 的开发提供了清晰的指导，确保各模块间的接口一致性与可维护性。
zhanghongyu_0626
zyi
GaoYiYuan_0626