1. Three party web website data access - Web site synchronization
Building a knowledge base by crawling web page data in batches through web crawlers
Web site synchronization utilizes crawler technology, which can automatically capture all websites under the same domain name through a single entry website. Currently, it supports up to 200 subpages. For compliance and security reasons, only static site crawling is supported, mainly used for quickly building knowledge bases on various document sites.
2. Feishu Knowledge Base
By configuring Feishu document permissions, a knowledge base can be built using Feishu documents, and the documents will not undergo secondary storage
3. Language Bird Knowledge Base
You can configure the permissions of the language bird document to build a knowledge base using the language bird document, and the document will not undergo secondary storage
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
* [fix]Fix the issue of inconsistent language in explicit and episodic memory.
* [fix]Fix the issue of inconsistent language in explicit and episodic memory.
* [add]Add scene_id
* [fix]Based on the AI review to fix the code
* [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
