mirror of
https://github.com/csunny/DB-GPT.git
synced 2025-07-24 04:36:23 +00:00
feat:add storage client cache (#2810)
This commit is contained in:
Aries-ckt
GitHub
parent
d27fdb7928
commit
1eea9c8eec
@ -1,5 +1,6 @@
|
||||
"""RAG STORAGE MANAGER manager."""
|
||||
|
||||
import threading
|
||||
from typing import List, Optional, Type
|
||||
|
||||
from dbgpt import BaseComponent
|
||||
@ -22,6 +23,8 @@ class StorageManager(BaseComponent):
|
||||
def __init__(self, system_app: SystemApp):
|
||||
"""Create a new ConnectorManager."""
|
||||
self.system_app = system_app
|
||||
self._store_cache = {}
|
||||
self._cache_lock = threading.Lock()
|
||||
super().__init__(system_app)
|
||||
|
||||
def init_app(self, system_app: SystemApp):
|
||||
@ -62,17 +65,22 @@ class StorageManager(BaseComponent):
|
||||
"""Create vector store."""
|
||||
app_config = self.system_app.config.configs.get("app_config")
|
||||
storage_config = app_config.rag.storage
|
||||
embedding_factory = self.system_app.get_component(
|
||||
"embedding_factory", EmbeddingFactory
|
||||
)
|
||||
embedding_fn = embedding_factory.create()
|
||||
vector_store_config: VectorStoreConfig = storage_config.vector
|
||||
return vector_store_config.create_store(
|
||||
name=index_name,
|
||||
embedding_fn=embedding_fn,
|
||||
max_chunks_once_load=vector_store_config.max_chunks_once_load,
|
||||
max_threads=vector_store_config.max_threads,
|
||||
)
|
||||
if index_name in self._store_cache:
|
||||
return self._store_cache[index_name]
|
||||
with self._cache_lock:
|
||||
embedding_factory = self.system_app.get_component(
|
||||
"embedding_factory", EmbeddingFactory
|
||||
)
|
||||
embedding_fn = embedding_factory.create()
|
||||
vector_store_config: VectorStoreConfig = storage_config.vector
|
||||
new_store = vector_store_config.create_store(
|
||||
name=index_name,
|
||||
embedding_fn=embedding_fn,
|
||||
max_chunks_once_load=vector_store_config.max_chunks_once_load,
|
||||
max_threads=vector_store_config.max_threads,
|
||||
)
|
||||
self._store_cache[index_name] = new_store
|
||||
return new_store
|
||||
|
||||
def create_kg_store(
|
||||
self, index_name, llm_model: Optional[str] = None
|
||||
@ -81,63 +89,72 @@ class StorageManager(BaseComponent):
|
||||
app_config = self.system_app.config.configs.get("app_config")
|
||||
rag_config = app_config.rag
|
||||
storage_config = app_config.rag.storage
|
||||
worker_manager = self.system_app.get_component(
|
||||
ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
|
||||
).create()
|
||||
llm_client = DefaultLLMClient(worker_manager=worker_manager)
|
||||
embedding_factory = self.system_app.get_component(
|
||||
"embedding_factory", EmbeddingFactory
|
||||
)
|
||||
embedding_fn = embedding_factory.create()
|
||||
if storage_config.graph:
|
||||
graph_config = storage_config.graph
|
||||
graph_config.llm_model = llm_model
|
||||
if hasattr(graph_config, "enable_summary") and graph_config.enable_summary:
|
||||
from dbgpt_ext.storage.knowledge_graph.community_summary import (
|
||||
CommunitySummaryKnowledgeGraph,
|
||||
)
|
||||
if index_name in self._store_cache:
|
||||
return self._store_cache[index_name]
|
||||
with self._cache_lock:
|
||||
worker_manager = self.system_app.get_component(
|
||||
ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
|
||||
).create()
|
||||
llm_client = DefaultLLMClient(worker_manager=worker_manager)
|
||||
embedding_factory = self.system_app.get_component(
|
||||
"embedding_factory", EmbeddingFactory
|
||||
)
|
||||
embedding_fn = embedding_factory.create()
|
||||
if storage_config.graph:
|
||||
graph_config = storage_config.graph
|
||||
graph_config.llm_model = llm_model
|
||||
if (
|
||||
hasattr(graph_config, "enable_summary")
|
||||
and graph_config.enable_summary
|
||||
):
|
||||
from dbgpt_ext.storage.knowledge_graph.community_summary import (
|
||||
CommunitySummaryKnowledgeGraph,
|
||||
)
|
||||
|
||||
return CommunitySummaryKnowledgeGraph(
|
||||
config=storage_config.graph,
|
||||
name=index_name,
|
||||
llm_client=llm_client,
|
||||
vector_store_config=storage_config.vector,
|
||||
kg_extract_top_k=rag_config.kg_extract_top_k,
|
||||
kg_extract_score_threshold=rag_config.kg_extract_score_threshold,
|
||||
kg_community_top_k=rag_config.kg_community_top_k,
|
||||
kg_community_score_threshold=rag_config.kg_community_score_threshold,
|
||||
kg_triplet_graph_enabled=rag_config.kg_triplet_graph_enabled,
|
||||
kg_document_graph_enabled=rag_config.kg_document_graph_enabled,
|
||||
kg_chunk_search_top_k=rag_config.kg_chunk_search_top_k,
|
||||
kg_extraction_batch_size=rag_config.kg_extraction_batch_size,
|
||||
kg_community_summary_batch_size=rag_config.kg_community_summary_batch_size,
|
||||
kg_embedding_batch_size=rag_config.kg_embedding_batch_size,
|
||||
kg_similarity_top_k=rag_config.kg_similarity_top_k,
|
||||
kg_similarity_score_threshold=rag_config.kg_similarity_score_threshold,
|
||||
kg_enable_text_search=rag_config.kg_enable_text_search,
|
||||
kg_text2gql_model_enabled=rag_config.kg_text2gql_model_enabled,
|
||||
kg_text2gql_model_name=rag_config.kg_text2gql_model_name,
|
||||
embedding_fn=embedding_fn,
|
||||
kg_max_chunks_once_load=rag_config.max_chunks_once_load,
|
||||
kg_max_threads=rag_config.max_threads,
|
||||
)
|
||||
return BuiltinKnowledgeGraph(
|
||||
config=storage_config.graph,
|
||||
name=index_name,
|
||||
llm_client=llm_client,
|
||||
)
|
||||
return CommunitySummaryKnowledgeGraph(
|
||||
config=storage_config.graph,
|
||||
name=index_name,
|
||||
llm_client=llm_client,
|
||||
vector_store_config=storage_config.vector,
|
||||
kg_extract_top_k=rag_config.kg_extract_top_k,
|
||||
kg_extract_score_threshold=rag_config.kg_extract_score_threshold,
|
||||
kg_community_top_k=rag_config.kg_community_top_k,
|
||||
kg_community_score_threshold=rag_config.kg_community_score_threshold,
|
||||
kg_triplet_graph_enabled=rag_config.kg_triplet_graph_enabled,
|
||||
kg_document_graph_enabled=rag_config.kg_document_graph_enabled,
|
||||
kg_chunk_search_top_k=rag_config.kg_chunk_search_top_k,
|
||||
kg_extraction_batch_size=rag_config.kg_extraction_batch_size,
|
||||
kg_community_summary_batch_size=rag_config.kg_community_summary_batch_size,
|
||||
kg_embedding_batch_size=rag_config.kg_embedding_batch_size,
|
||||
kg_similarity_top_k=rag_config.kg_similarity_top_k,
|
||||
kg_similarity_score_threshold=rag_config.kg_similarity_score_threshold,
|
||||
kg_enable_text_search=rag_config.kg_enable_text_search,
|
||||
kg_text2gql_model_enabled=rag_config.kg_text2gql_model_enabled,
|
||||
kg_text2gql_model_name=rag_config.kg_text2gql_model_name,
|
||||
embedding_fn=embedding_fn,
|
||||
kg_max_chunks_once_load=rag_config.max_chunks_once_load,
|
||||
kg_max_threads=rag_config.max_threads,
|
||||
)
|
||||
return BuiltinKnowledgeGraph(
|
||||
config=storage_config.graph,
|
||||
name=index_name,
|
||||
llm_client=llm_client,
|
||||
)
|
||||
|
||||
def create_full_text_store(self, index_name) -> FullTextStoreBase:
|
||||
"""Create Full Text store."""
|
||||
app_config = self.system_app.config.configs.get("app_config")
|
||||
rag_config = app_config.rag
|
||||
storage_config = app_config.rag.storage
|
||||
return ElasticDocumentStore(
|
||||
es_config=storage_config.full_text,
|
||||
name=index_name,
|
||||
k1=rag_config.bm25_k1,
|
||||
b=rag_config.bm25_b,
|
||||
)
|
||||
if index_name in self._store_cache:
|
||||
return self._store_cache[index_name]
|
||||
with self._cache_lock:
|
||||
return ElasticDocumentStore(
|
||||
es_config=storage_config.full_text,
|
||||
name=index_name,
|
||||
k1=rag_config.bm25_k1,
|
||||
b=rag_config.bm25_b,
|
||||
)
|
||||
|
||||
@property
|
||||
def get_vector_supported_types(self) -> List[str]:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user