wanyaokun
83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
import os
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from llama_index.vector_stores.chroma import ChromaVectorStore
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from llama_index.vector_stores.qdrant import QdrantVectorStore
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from qdrant_client import qdrant_client
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from llama_index.graph_stores.neo4j import Neo4jPropertyGraphStore
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qclient = None
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def get_qdrant_vector_store(docType:str):
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collection_name = docType
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vector_store_path = os.getenv("VECTOR_STORE_PATH")
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host=os.getenv("VECTOR_STORE_HOST", "127.0.0.1"),
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port=int(os.getenv("VECTOR_STORE_PORT", "6333")),
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vector_store_path =os.path.join(vector_store_path,docType)
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if not vector_store_path or not host:
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raise ValueError(
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"Please provide either VECTOR_STORE_PATH or VECTOR_STORE_HOST and VECTOR_STORE_PORT"
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)
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# if VECTOR_STORE_PATH is set, use a local QdrantVectorStore from the path
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# otherwise, use a remote QdrantVectorStore
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global qclient
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if qclient == None:
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if vector_store_path:
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qclient = qdrant_client.QdrantClient(
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path=vector_store_path,
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)
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else:
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qclient = qdrant_client.QdrantClient(
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host=host,
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port=port,
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)
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vector_store = QdrantVectorStore(client=qclient, collection_name=collection_name)
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return vector_store
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def get_chroma_vector_store(docType:str):
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collection_name = docType
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vector_store_path =os.path.join(os.getenv("VECTOR_STORE_PATH"),docType)
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# if VECTOR_STORE_PATH is set, use a local ChromaVectorStore from the path
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# otherwise, use a remote ChromaVectorStore (ChromaDB Cloud is not supported yet)
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if vector_store_path:
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store = ChromaVectorStore.from_params(
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persist_dir=vector_store_path, collection_name=collection_name,
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collection_kwargs={"metadata":{"hnsw:space":"cosine"}},
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)
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else:
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if not os.getenv("VECTOR_STORE_HOST") or not os.getenv("VECTOR_STORE_PORT"):
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raise ValueError(
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"Please provide either VECTOR_STORE_PATH or VECTOR_STORE_HOST and VECTOR_STORE_PORT"
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)
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store = ChromaVectorStore.from_params(
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host=os.getenv("VECTOR_STORE_HOST"),
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port=int(os.getenv("VECTOR_STORE_PORT")),
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collection_name=collection_name,
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collection_kwargs={"metadata":{"hnsw:space":"cosine"}},
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)
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return store
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def get_vector_store(docType:str):
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store_type=os.getenv("VECTOR_STORE_TYPE")
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store = None
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match store_type:
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case "chroma":
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store = get_chroma_vector_store(docType)
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case "qdrant":
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store = get_qdrant_vector_store(docType)
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case _:
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raise ValueError(f"Invalid vector store type: {store_type}")
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return store
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def get_Neo4j_Graph_Store(docType:str):
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neo4jStore = Neo4jPropertyGraphStore(
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username= os.getenv('NEO4J_USERNAME'),
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password= os.getenv('NEO4J_PASSWORD'),
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url=os.getenv('NEO4J_URL'),
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database= docType
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)
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return neo4jStore |