* [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
* [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
- Add force parameter to delete_config endpoint for controlled deletion of in-use configs
- Implement MemoryConfigService.delete_config with protection against deleting default configs
- Add validation to prevent deletion of configs with connected end-users unless force=True
- Reorganize controller imports to remove duplicates and improve maintainability
- Clean up unused database connection management code from memory_storage_controller
- Add detailed docstring to delete_config endpoint explaining protection mechanisms
- Update error handling with specific BizCode.RESOURCE_IN_USE for configs in active use
- Add comprehensive logging for deletion attempts, warnings, and affected users
- Refactor ConfigParamsDelete schema usage to use MemoryConfigService directly
- Improve API response structure with affected_users count and force_required flag
* [changes]Optimize the time consumption of the "/end_users" interface
* [fix]Optimize the time consumption of the "/hot_memory_tags" interface
* [changes]Optimize the time consumption of the "/end_users" interface
* [fix]Optimize the time consumption of the "/hot_memory_tags" interface
* [changes]Improve the code based on AI review
* [changes]《Modify the interface》
1.Remove the "/search/entity_graph" interface
2.Reconstruct the "/updated_end_user/profile" interface
3.Remove the "Update Username" interface
4.Fix the batch query of user association memory configuration
* [changes]《Modify the interface》
1.Remove the "/search/entity_graph" interface
2.Reconstruct the "/updated_end_user/profile" interface
3.Remove the "Update Username" interface
4.Fix the batch query of user association memory configuration
* [fix]Fix the error response type
[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
- Move MemoryClientFactory from app.core.memory.client_factory to app.core.memory.utils.llm.llm_utils
- Update all evaluation modules to import MemoryClientFactory from new location (locomo, longmemeval, memsciqa)
- Move GenerateCacheRequest from memory_storage_service to memory_storage_schema
- Update memory_storage_controller imports to reflect schema reorganization
- Add analytics_user_summary import to memory_storage_controller from user_memory_service
- Consolidate utility imports across evaluation test files for consistency
- Improve code organization by centralizing LLM utilities in dedicated utils module
- Reorganize imports and remove unused dependencies across memory agent controllers
- Extract config validation logic into dedicated validators module
- Create new memory_config_model and memory_config_schema for configuration management
- Implement memory_config_service for centralized config handling
- Add embedder_utils module for embedding model utilities
- Refactor memory agent service to use new config validation framework
- Clean up configuration files (remove config.json, testdata.json, dbrun.json)
- Remove deprecated hybrid_chatbot.py and config overrides
- Update logging configuration and error handling across memory modules
- Consolidate LLM and embedding model validation into validators
- Improve code organization and reduce duplication in memory storage services
- Enhance type classification and verification tools with better error handling
