1. Increase support for visual models and multimodal models;
2. The application and workflow can input various multimodal files such as images, documents, audio, and videos.
* refactor(conversation): separate service and repository layers for conversation module
- Split ConversationService and repository/UnitOfWork layers
- Service layer now only handles business logic and orchestration
- Repository layer handles all direct database operations
- UnitOfWork encapsulates transactional operations for messages
- Ensured all public methods have clear English docstrings with arguments, return values, and exceptions
* feat(memory): implement work memory endpoints and services
- Added API routes for conversation count, conversation list, messages, and detail.
- Integrated ConversationService for database queries and LLM-based summary generation.
* feat(memory): implement work memory endpoints and services
- Added API routes for conversation count, conversation list, messages, and detail.
- Integrated ConversationService for database queries and LLM-based summary generation.
* feat(workflow): fix issues causing workflow failures
if-else None value error
knowledge empty list rerank
end node output none node value
assigner input none value
* feat(memory): convert memory file creation time to timestamp and include title and first-line fields in file type
* fix(memory): fix serialization output and default value issues
* fix(workflow): fix issue with hybrid search logic in knowledge retrieval node
* perf(workflow): pass JSON data to HTTP node as a string
* perf(prompt_opt): simplify log output
* feat(memory): add perceptual memory page API and related database schema
* perf(log): clean up API exception log output
* perf(memory): simplify perceptual memory timeline response by removing metadata
- Replace the system prompt of the prompt optimization model with a built-in prompt.
- Remove system prompt entries from the database.
- Remove the API endpoint for managing system prompt configuration.
- Added API endpoints for prompt optimization:
* POST /prompt/sessions: Create a new prompt optimization session
* GET /prompt/sessions/{session_id}: Retrieve session message history
* POST /prompt/sessions/{session_id}/messages: Send message and get optimized prompt
* PUT /prompt/model: Create or update system prompt model configuration
- Added database models for prompt optimization:
* prompt_opt_session: Stores session metadata
* prompt_opt_session_history: Stores session message history
* prompt_opt_message: Stores user and assistant messages
* prompt_opt_model_config: Stores system prompt model configurations
- Updated service layer to handle message creation, prompt optimization, and variable parsing
- Added corresponding Pydantic schemas for request and response validation
