* [refactor]Reconstruct episodic memory
* [refactor]Reconstructing explicit memory
* [refactor]Reconstruct episodic memory
* [refactor]Reconstructing explicit memory
* [changes]Based on the improvement of AI review
* [changes]Modify the routing
* [changes]Uniform routing format
* [fix]Fix the failure in parsing the timestamp.
* [refactor]Reconstruct episodic memory
* [refactor]Reconstructing explicit memory
* [changes]Based on the improvement of AI review
* [changes]Modify the routing
* [changes]Uniform routing format
* [fix]Fix the failure in parsing the timestamp.
* [deleted]Delete migration files
* [refactor]Reconstruct episodic memory
* [refactor]Reconstructing explicit memory
* [changes]Based on the improvement of AI review
* [changes]Modify the routing
* [changes]Uniform routing format
* [fix]Fix the failure in parsing the timestamp.
* [deleted]Delete migration files
* feat: add database migration 9ab9b6393f32_20261511
* feat(memory): add conversation title to conversation list response for frontend display
* feat(memory): optimize conversation retrieval, enable working memory to return conversation question summaries
* fix(memory): fix conversation re-generation logic
* style(desc): improve description of get_conversation function
* 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
* [changes]Request to remove 'config_id' has been received.
* [add]Add the access history record table
* [changes]Request to remove 'config_id' has been received.
* [add]Add the access history record table
* [add]Obtain the record of the forgetting trend
* [changes]Based on the AI's suggestion, make the necessary modifications.
* 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
[feature]actr-记忆遗忘需求开发
* feature/actr-forget: (12 commits squashed)
- [feature]
1.Extended fields of the date_config table;
2.New activation value calculation has been added, and the ACTR parameter has been introduced in Neo4j.
- [feature]1.Create a forgetting strategy executor;2.Create the forgetting scheduler
- [feature]Introduce activation values for retrieval, and develop a two-stage retrieval reordering process
- [feature]
1.Extended fields of the date_config table;
2.New activation value calculation has been added, and the ACTR parameter has been introduced in Neo4j.
- [feature]1.Create a forgetting strategy executor;2.Create the forgetting scheduler
- [feature]Introduce activation values for retrieval, and develop a two-stage retrieval reordering process
- Merge branch 'feature/actr-forget' of codeup.aliyun.com:redbearai/python/redbear-mem-open into feature/actr-forget
- [fix]Eliminate the interference caused by redundant code
- [feature]
1.Extended fields of the date_config table;
2.New activation value calculation has been added, and the ACTR parameter has been introduced in Neo4j.
- [feature]1.Create a forgetting strategy executor;2.Create the forgetting scheduler
- [feature]Introduce activation values for retrieval, and develop a two-stage retrieval reordering process
- Merge branch 'feature/actr-forget' of codeup.aliyun.com:redbearai/python/redbear-mem-open into feature/actr-forget
Signed-off-by: 乐力齐 <accounts_690c7b0af9007d7e338af636@mail.teambition.com>
Reviewed-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com>
Merged-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com>
CR-link: https://codeup.aliyun.com/redbearai/python/redbear-mem-open/change/85
Merge branch 'develop' into feature/20251219_myh
* feature/20251219_myh: (8 commits squashed)
- feat(workflow): update reranker model configuration for knowledge base retrieval
- fix(workflow): fix output issue in parameter extraction node
- fix(workflow): fix output issue in parameter extraction node
- feat(workflow): add user prompt to parameter extraction node
- perf(workflow): change grouped variable input to key-value format in variable aggregator
- feat(workflow): Add new cycle node for iterative workflow execution
- Introduce a new Loop/Iteration node in the workflow engine.
- Supports both conditional loops and iteration over lists.
- Allows parallel execution and flattening of iteration outputs.
- Maintains runtime state, node outputs, and loop variables for downstream nodes.
- Enhances workflow flexibility for complex, repeated operations.
- Merge branch 'develop' into feature/20251219_myh
# Conflicts:
#	api/app/core/workflow/nodes/configs.py
#	api/app/core/workflow/nodes/node_factory.py
- feat(workflow): Add new cycle node for iterative workflow execution
- Introduce a new Loop/Iteration node in the workflow engine.
- Supports both conditional loops and iteration over lists.
- Allows parallel execution and flattening of iteration outputs.
- Maintains runtime state, node outputs, and loop variables for downstream nodes.
- Enhances workflow flexibility for complex, repeated operations.
Signed-off-by: Eternity <1533512157@qq.com>
Reviewed-by: zhuwenhui5566@163.com <zhuwenhui5566@163.com>
Reviewed-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com>
Merged-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com>
CR-link: https://codeup.aliyun.com/redbearai/python/redbear-mem-open/change/78
1. Optimization of the JSON tool, add insert, replace, delete, parse
2. Optimization of the mcp test_connection
3. tool list desc
4. datetime_tool default timezone set Asia/Shanghai
- Add MemoryAPIService with read_memory and write_memory methods for managing user memories
- Create memory_api_schema.py with request/response schemas for read and write operations
- Implement write_memory_api_service endpoint for storing memory content with configurable storage backends
- Implement read_memory_api_service endpoint for querying memories with context-aware responses
- Add memory-specific error codes (MEMORY_WRITE_FAILED, MEMORY_READ_FAILED, MEMORY_CONFIG_NOT_FOUND) to error_codes.py
- Add PUT endpoint to update workspace LLM, embedding, and rerank model configurations
- Create WorkspaceModelsUpdate schema for model configuration update requests
- Create WorkspaceModelsConfig schema for model configuration responses with proper validation
- Implement update_workspace_models_configs service method to persist model configuration changes
- Update workspace_models_configs GET endpoint to return validated WorkspaceModelsConfig response
- Reorganize imports across controller, schema, and service files for consistency and readability
- Add proper logging for model configuration updates with user and workspace context
[fix]Fix the bug that affects user memory.
* fix/othername-name: (11 commits squashed)
- [fix]Fix the issue with the display of the user's memory list
- [fix]Ensure the six dimensions of emotional expression
- [fix]Fix the issue with the display of the user's memory list
- [fix]Ensure the six dimensions of emotional expression
- Merge branch 'fix/othername-name' of codeup.aliyun.com:redbearai/python/redbear-mem-open into fix/othername-name
- [fix]Restore the display of memory types
- [fix]Fix the issue with the display of the user's memory list
- [fix]Ensure the six dimensions of emotional expression
- [fix]Restore the display of memory types
- Merge branch 'fix/othername-name' of codeup.aliyun.com:redbearai/python/redbear-mem-open into fix/othername-name
- [updated]Update the title of the "analytics/node_statistics" log
Signed-off-by: 乐力齐 <accounts_690c7b0af9007d7e338af636@mail.teambition.com>
Reviewed-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com>
Merged-by: aliyun6762716068 <accounts_68cb7c6b61f5dcc4200d6251@mail.teambition.com>
CR-link: https://codeup.aliyun.com/redbearai/python/redbear-mem-open/change/47
- Make config_id optional in UserInput and Write_UserInput schemas
- Update write_memory and read_memory method signatures to accept Optional[str] for config_id
- Implement automatic config retrieval from end_user connection when config_id is not provided
- Add explicit error handling for missing memory configurations with descriptive error messages
- Improve emotion_controller to validate config_id using MemoryConfigService when provided
- Fallback to get_end_user_connected_config when config_id is None
- Distinguish between specific "no configuration found" errors and other exceptions for better debugging
- Ensures users can operate without explicitly providing config_id if they have a connected configuration
- Remove deprecated main.py entry point from memory module
- Reorganize imports across controllers and services for consistency
- Update emotion controller to pass db session instead of config_id to services
- Enhance memory agent controller with db session parameter for status_type and user_profile endpoints
- Refactor memory agent service to accept db parameter in classify_message_type method
- Improve configuration handling in celery_app by removing automatic database reload
- Update all memory-related services to use centralized config management
- Standardize import ordering and remove unused imports across 50+ files
- Add pilot_run_service for new pilot execution workflow
- Refactor extraction engine, reflection engine, and search services for better modularity
- Update LLM utilities and embedder configuration for improved flexibility
- Enhance type classifier and verification tools with better error handling
- Improve memory evaluation modules (LOCOMO, LongMemEval, MemSciQA) with consistent patterns
