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* add sparse vector embedding * hybrid search add sparse vector search * add checking version logic * add qwen sparse vector * adjust weight * update sparse vector function * update sparse vector function * fix bug * fix bug * optimise function * optimise function * optimise function * optimise function * optimise function * optimise function * optimise function * fix bug * add migrate function * update alembic function * update alembic function * update alembic function * adjust file struct * update alembic * update version * optimise * fix bug * update * update schema update method * update schema update method * update schema update method * update schema update method * update schema update method * update migrate method * update migrate method * update env.example * update env.example * update migrate sparse vector * update migrate sparse vector * adjust threshold score logic * update remark * add guides and examples * add benchmark param * fix bug * fulltext parsers support * adjust enable sparse vector setting * adjust env.example * adjust docs * update version * fix bug * optimise check * adjust file construct * adjust file construct * add native search * add file * remove log * remove log * fix bug * update pyobvector * add rerank * adjust * add limit * adjust config
* support native language * support native language * add docs
* feat(llm): enhance configuration management with pydantic-settings - Introduced a unified configuration system for LLM providers using pydantic-settings. - Added provider-specific settings for Anthropic, Azure, DeepSeek, Ollama, OpenAI, Qwen, Vllm, and Zai. - Improved environment variable handling and validation through Field and AliasChoices. - Removed legacy initialization methods in favor of a cleaner, more maintainable structure. - Updated LLMFactory to utilize the new provider registration mechanism. * chore: Update LLM configuration management and improve environment variable handling - Refactor LLM configuration imports to use BaseLLMConfig. - Replace direct attribute access with getattr for safer environment variable retrieval. - Remove deprecated LLMConfig and streamline related code for better maintainability.
* feat(llm): enhance configuration management with pydantic-settings - Introduced a unified configuration system for LLM providers using pydantic-settings. - Added provider-specific settings for Anthropic, Azure, DeepSeek, Ollama, OpenAI, Qwen, Vllm, and Zai. - Improved environment variable handling and validation through Field and AliasChoices. - Removed legacy initialization methods in favor of a cleaner, more maintainable structure. - Updated LLMFactory to utilize the new provider registration mechanism. * chore: Update LLM configuration management and improve environment variable handling - Refactor LLM configuration imports to use BaseLLMConfig. - Replace direct attribute access with getattr for safer environment variable retrieval. - Remove deprecated LLMConfig and streamline related code for better maintainability. * feat: Enhance rerank configuration and integration - Introduced BaseRerankConfig for improved configuration management across rerank providers. - Updated rerank integration files to utilize the new base configuration structure. - Added support for additional configuration fields such as api_base_url and top_n. - Refactored rerank factory to accommodate new configuration handling and provider registration. - Removed deprecated RerankConfig and streamlined related code for better maintainability. - Updated API request handling in rerank classes to support custom HTTP clients. * * refactor(powermem): remove unused storage configuration management module - Removed `VectorStoreConfig` and `GraphStoreConfig` classes - Deleted associated validation logic and import statements - Streamlined codebase by eliminating unused components * feat(powermem): enhance sparse embedder configuration management - Introduced BaseSparseEmbedderConfig for unified sparse embedding configuration. - Updated MemoryConfig to utilize BaseSparseEmbedderConfig. - Refactored SparseEmbedderFactory to support new configuration handling. - Improved handling of sparse embedder settings across various components. * feat(powermem): enhance user profile storage with provider registration - Added a registry mechanism to UserProfileStoreBase for automatic provider registration. - Implemented class paths for OceanBase and SQLite user profile storage implementations. - Updated UserProfileStoreFactory to utilize the new registry for provider class retrieval. - Refactored imports to trigger auto-registration of user profile storage classes. - Improved handling of provider names in the factory for better compatibility. * feat(powermem): synchronize embedding model dimensions across configurations - Added logic to sync `embedding_model_dims` from the embedder to both `vector_store` and `graph_store` if not already set. - Updated `config_loader.py` and `configs.py` to ensure consistent embedding dimensions across components. * feat(powermem): enhance OceanBase configuration and query handling - Added `enable_native_hybrid` field to `OceanBaseConfig` for native hybrid search support. - Updated query handling in `OceanBaseVectorStore` to use a safe query format, preventing SQL injection risks.
- Introduced new RERANKER_* environment variables for improved configuration management across rerank providers. - Updated .env.example to include new rerank settings and reorganized sections for clarity. - Refactored rerank integration files to utilize the new base configuration structure, ensuring consistency in API key and base URL handling. - Updated error messages in rerank classes to reflect the new environment variable naming convention.
- Remove local .env and use project root config - Replace custom settings with auto_config from powermem - Update documentation to reflect new setup
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Summary
merge benchmark code
Summary
merge benchmark code