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https://github.com/csunny/DB-GPT.git
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feat:add storage client cache (#2810)
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Aries-ckt
GitHub
parent
d27fdb7928
commit
1eea9c8eec
@ -1,5 +1,6 @@
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"""RAG STORAGE MANAGER manager."""
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import threading
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from typing import List, Optional, Type
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from dbgpt import BaseComponent
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@ -22,6 +23,8 @@ class StorageManager(BaseComponent):
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def __init__(self, system_app: SystemApp):
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"""Create a new ConnectorManager."""
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self.system_app = system_app
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self._store_cache = {}
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self._cache_lock = threading.Lock()
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super().__init__(system_app)
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def init_app(self, system_app: SystemApp):
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@ -62,17 +65,22 @@ class StorageManager(BaseComponent):
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"""Create vector store."""
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app_config = self.system_app.config.configs.get("app_config")
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storage_config = app_config.rag.storage
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embedding_factory = self.system_app.get_component(
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"embedding_factory", EmbeddingFactory
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)
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embedding_fn = embedding_factory.create()
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vector_store_config: VectorStoreConfig = storage_config.vector
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return vector_store_config.create_store(
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name=index_name,
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embedding_fn=embedding_fn,
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max_chunks_once_load=vector_store_config.max_chunks_once_load,
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max_threads=vector_store_config.max_threads,
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)
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if index_name in self._store_cache:
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return self._store_cache[index_name]
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with self._cache_lock:
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embedding_factory = self.system_app.get_component(
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"embedding_factory", EmbeddingFactory
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)
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embedding_fn = embedding_factory.create()
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vector_store_config: VectorStoreConfig = storage_config.vector
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new_store = vector_store_config.create_store(
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name=index_name,
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embedding_fn=embedding_fn,
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max_chunks_once_load=vector_store_config.max_chunks_once_load,
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max_threads=vector_store_config.max_threads,
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)
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self._store_cache[index_name] = new_store
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return new_store
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def create_kg_store(
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self, index_name, llm_model: Optional[str] = None
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@ -81,63 +89,72 @@ class StorageManager(BaseComponent):
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app_config = self.system_app.config.configs.get("app_config")
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rag_config = app_config.rag
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storage_config = app_config.rag.storage
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worker_manager = self.system_app.get_component(
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ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
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).create()
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llm_client = DefaultLLMClient(worker_manager=worker_manager)
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embedding_factory = self.system_app.get_component(
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"embedding_factory", EmbeddingFactory
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)
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embedding_fn = embedding_factory.create()
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if storage_config.graph:
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graph_config = storage_config.graph
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graph_config.llm_model = llm_model
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if hasattr(graph_config, "enable_summary") and graph_config.enable_summary:
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from dbgpt_ext.storage.knowledge_graph.community_summary import (
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CommunitySummaryKnowledgeGraph,
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)
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if index_name in self._store_cache:
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return self._store_cache[index_name]
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with self._cache_lock:
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worker_manager = self.system_app.get_component(
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ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
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).create()
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llm_client = DefaultLLMClient(worker_manager=worker_manager)
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embedding_factory = self.system_app.get_component(
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"embedding_factory", EmbeddingFactory
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)
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embedding_fn = embedding_factory.create()
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if storage_config.graph:
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graph_config = storage_config.graph
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graph_config.llm_model = llm_model
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if (
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hasattr(graph_config, "enable_summary")
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and graph_config.enable_summary
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):
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from dbgpt_ext.storage.knowledge_graph.community_summary import (
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CommunitySummaryKnowledgeGraph,
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)
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return CommunitySummaryKnowledgeGraph(
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config=storage_config.graph,
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name=index_name,
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llm_client=llm_client,
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vector_store_config=storage_config.vector,
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kg_extract_top_k=rag_config.kg_extract_top_k,
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kg_extract_score_threshold=rag_config.kg_extract_score_threshold,
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kg_community_top_k=rag_config.kg_community_top_k,
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kg_community_score_threshold=rag_config.kg_community_score_threshold,
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kg_triplet_graph_enabled=rag_config.kg_triplet_graph_enabled,
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kg_document_graph_enabled=rag_config.kg_document_graph_enabled,
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kg_chunk_search_top_k=rag_config.kg_chunk_search_top_k,
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kg_extraction_batch_size=rag_config.kg_extraction_batch_size,
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kg_community_summary_batch_size=rag_config.kg_community_summary_batch_size,
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kg_embedding_batch_size=rag_config.kg_embedding_batch_size,
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kg_similarity_top_k=rag_config.kg_similarity_top_k,
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kg_similarity_score_threshold=rag_config.kg_similarity_score_threshold,
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kg_enable_text_search=rag_config.kg_enable_text_search,
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kg_text2gql_model_enabled=rag_config.kg_text2gql_model_enabled,
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kg_text2gql_model_name=rag_config.kg_text2gql_model_name,
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embedding_fn=embedding_fn,
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kg_max_chunks_once_load=rag_config.max_chunks_once_load,
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kg_max_threads=rag_config.max_threads,
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)
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return BuiltinKnowledgeGraph(
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config=storage_config.graph,
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name=index_name,
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llm_client=llm_client,
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)
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return CommunitySummaryKnowledgeGraph(
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config=storage_config.graph,
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name=index_name,
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llm_client=llm_client,
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vector_store_config=storage_config.vector,
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kg_extract_top_k=rag_config.kg_extract_top_k,
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kg_extract_score_threshold=rag_config.kg_extract_score_threshold,
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kg_community_top_k=rag_config.kg_community_top_k,
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kg_community_score_threshold=rag_config.kg_community_score_threshold,
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kg_triplet_graph_enabled=rag_config.kg_triplet_graph_enabled,
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kg_document_graph_enabled=rag_config.kg_document_graph_enabled,
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kg_chunk_search_top_k=rag_config.kg_chunk_search_top_k,
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kg_extraction_batch_size=rag_config.kg_extraction_batch_size,
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kg_community_summary_batch_size=rag_config.kg_community_summary_batch_size,
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kg_embedding_batch_size=rag_config.kg_embedding_batch_size,
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kg_similarity_top_k=rag_config.kg_similarity_top_k,
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kg_similarity_score_threshold=rag_config.kg_similarity_score_threshold,
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kg_enable_text_search=rag_config.kg_enable_text_search,
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kg_text2gql_model_enabled=rag_config.kg_text2gql_model_enabled,
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kg_text2gql_model_name=rag_config.kg_text2gql_model_name,
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embedding_fn=embedding_fn,
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kg_max_chunks_once_load=rag_config.max_chunks_once_load,
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kg_max_threads=rag_config.max_threads,
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)
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return BuiltinKnowledgeGraph(
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config=storage_config.graph,
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name=index_name,
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llm_client=llm_client,
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)
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def create_full_text_store(self, index_name) -> FullTextStoreBase:
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"""Create Full Text store."""
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app_config = self.system_app.config.configs.get("app_config")
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rag_config = app_config.rag
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storage_config = app_config.rag.storage
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return ElasticDocumentStore(
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es_config=storage_config.full_text,
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name=index_name,
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k1=rag_config.bm25_k1,
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b=rag_config.bm25_b,
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)
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if index_name in self._store_cache:
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return self._store_cache[index_name]
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with self._cache_lock:
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return ElasticDocumentStore(
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es_config=storage_config.full_text,
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name=index_name,
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k1=rag_config.bm25_k1,
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b=rag_config.bm25_b,
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)
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@property
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def get_vector_supported_types(self) -> List[str]:
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