* [add]Integration of the core engineering and memory extraction
* [add]The import and export function of the main body engineering files
* [add]Improve the import interface
* [add]Introducing generic types helps with entity extraction
* [add]Modify the references of the main repository to the sub-repositories
* [add]The extraction trial run introduces the ontology type.
* [add]Integration of the core engineering and memory extraction
* [add]The import and export function of the main body engineering files
* [add]Improve the import interface
* [add]Introducing generic types helps with entity extraction
* [add]Modify the references of the main repository to the sub-repositories
* [add]The extraction trial run introduces the ontology type.
* [add]Complete the second phase of the main project content
* [add]The dependencies and configurations of the main body project
* [add]Modify the code based on the AI review
- Add support for both memory_config_id (new) and memory_content (legacy) field names
- Implement detection and handling of legacy int format memory configurations
- Add validation for numeric string formats with appropriate warning logs
- Support case-insensitive memory node type matching (MemoryRead/MemoryWrite and memory-read/memory-write)
- Improve error handling with more descriptive logging for invalid UUID strings
- Fix config_id field reference in memory config resolution
- Ensure backward compatibility with existing agent configurations while supporting new format
- Add workspace_id fallback parameter to memory config loading across all services
- Update hot_memory_tags.py to pass workspace_id when resolving memory configuration
- Enhance emotion_analytics_service.py to support workspace_id as fallback for config resolution
- Improve implicit_memory_service.py with workspace_id fallback in config loading
- Update memory_agent_service.py to handle workspace_id resolution and add refactoring TODO
- Enhance preference_analysis.jinja2 prompt with critical guidance on supporting_evidence extraction
- Add validation to check both config_id and workspace_id before raising configuration errors
- Improve error handling and logging for memory configuration resolution across services
- This enables more flexible memory configuration resolution when config_id is unavailable
- Add null check for actual_config_id before calling term_memory_save in langchain_agent.py to prevent errors when memory config is unavailable
- Add warning log when skipping term_memory_save due to missing memory config
- Fix incorrect attribute reference from memory_config.id to memory_config.config_id in memory_agent_service.py
- Fix method call from private _get_workspace_default_config to public get_workspace_default_config in memory_config_service.py
- Ensures graceful handling of missing memory configurations and prevents runtime errors
1. Add the "Skills" module;
2. The loading of the model square has been modified to be controlled through environment variables;
3. Dynamic scheduling of the skill binding tool;
4. Agent Integration Skills
* [changes]Add 'id' as the secondary sorting key, and 'scene_id' now returns a UUID object
* [fix]Fix the "end_user" return to be sorted by update time.
* [fix]Set the default values of the memory configuration model based on the spatial model.
* [fix]Remove the entity extraction check combination model, read the configuration list, and add the return of scene_id
* [fix]Fix the "end_user" return to be sorted by update time.
* [fix]
* [changes]add user_summary language unification
* [add]Entity extraction, user memory, emotion suggestions, unified language type for writing
* [add]Complete the switch between Chinese and English for the emotion labels and emotion suggestions fields.
* [changes]add user_summary language unification
* [add]Entity extraction, user memory, emotion suggestions, unified language type for writing
* [add]Complete the switch between Chinese and English for the emotion labels and emotion suggestions fields.
* [changes]Modify the code based on the AI review
