* [changes]refactor locomo_test
* [fix]Fix the circular import of ModelParameters
* [changes]The benchmark test can run stably.
* [fix]Complete end-to-end LoCoMo repair
* [fix]Complete the end-to-end longmemeval and memsciqa fixes
* [changes]Complete the benchmark test description document to ensure that the configuration parameters take effect.
* [changes]refactor locomo_test
* [fix]Fix the circular import of ModelParameters
* [changes]The benchmark test can run stably.
* [fix]Complete end-to-end LoCoMo repair
* [fix]Complete the end-to-end longmemeval and memsciqa fixes
* [changes]Complete the benchmark test description document to ensure that the configuration parameters take effect.
* [changes]Benchmark test adaptation for end_user_id
* [changes]refactor locomo_test
* [fix]Fix the circular import of ModelParameters
* [changes]The benchmark test can run stably.
* [fix]Complete end-to-end LoCoMo repair
* [fix]Complete the end-to-end longmemeval and memsciqa fixes
* [changes]Complete the benchmark test description document to ensure that the configuration parameters take effect.
* [fix]Complete the end-to-end longmemeval and memsciqa fixes
* [changes]Complete the benchmark test description document to ensure that the configuration parameters take effect.
* [changes]Benchmark test adaptation for end_user_id
* [feature]Emotional memory cache
* [feature]Implicit memory cache
* [changes]Modify the expiration time of implicit memory to 24 hours.
* [feature]Emotional memory cache
* [feature]Implicit memory cache
* [changes]Modify the expiration time of implicit memory to 24 hours.
* [changes]Modify the code based on the AI review
* [feature]Emotional memory cache
* [feature]Implicit memory cache
* [changes]Modify the expiration time of implicit memory to 24 hours.
* [feature]Implicit memory cache
* [changes]Modify the code based on the AI review
* [feature]Generate emotions, implicit cache
* [feature]Generate emotions, implicit cache
* [changes]Improve the code based on AI review
* [changes]Improve the code based on AI review
* [changes]Improve the code
* [feature]Generate emotions, implicit cache
* [changes]Improve the code based on AI review
* [changes]Improve the code
* 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
Add functions related to knowledge base graph:
1. Entity type generation,
2. Knowledge base graph acquisition,
3. Hard deletion of knowledge base graph,
4. Knowledge base graph reconstruction (asynchronous)
[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
- 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
- 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
