- Implemented `_merged_items` and `_merged_memory_text` functions to consolidate consult and chitchat memories, improving context awareness in intent classification and response generation.
- Updated intent prompts to include recent dialogue history, aiding in the resolution of ambiguous user queries.
- Enhanced `search_knowledge` tool to utilize context window for better query understanding, addressing issues with omitted references in user inputs.
- Fixed existing test cases to reflect changes in intent constants and ensure accurate context handling during tests.
This update significantly improves the handling of multi-turn dialogues, ensuring a more coherent and contextually aware interaction for users.
- 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 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.
- 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.
- 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.
- 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.
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