feat(workflow): support cycle nodes in workflow config validation and enhance node logging
* feature/20251219_myh: (11 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.
- feat(workflow): support cycle nodes in workflow config validation and enhance node logging
- feat(workflow): support cycle nodes in workflow config validation and enhance node logging
- fix(workflow): fix compatibility with some legacy node configurations
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/80
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
Add new predicates for learning/education domain to support educational content extraction. Also add field validator to filter empty statements in extraction response to handle malformed LLM outputs.
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)
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
- Implemented ParameterExtractorNode to extract structured parameters from input text using LLM.
- Supports dynamic Jinja2 prompt rendering with field descriptions and types.
- Integrates with RedBearLLM and ModelConfigService for model retrieval.
- Handles JSON repair and raises clear BusinessException on parsing errors.
- 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
- Simplified FastMCP initialization by removing unnecessary allowed_hosts parameter.
- Added logging for MCP server startup details.
- Implemented DNS rebinding protection configuration to support Docker container hostnames.
- 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
- 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
