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39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
import logging
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from pathlib import Path
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from langchain.retrievers.multi_vector import MultiVectorRetriever
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from langchain.storage import LocalFileStore
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from langchain_chroma import Chroma
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from langchain_community.embeddings import OpenAIEmbeddings
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def get_multi_vector_retriever(docstore_id_key: str):
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"""Create the composed retriever object."""
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vectorstore = get_vectorstore()
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store = get_docstore()
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return MultiVectorRetriever(
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vectorstore=vectorstore,
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byte_store=store,
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id_key=docstore_id_key,
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)
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def get_vectorstore(collection_name: str = "proposals"):
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"""Get the vectorstore used for this example."""
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return Chroma(
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collection_name=collection_name,
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persist_directory=str(Path(__file__).parent.parent / "chroma_db_proposals"),
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embedding_function=OpenAIEmbeddings(),
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)
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def get_docstore():
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"""Get the metadata store used for this example."""
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return LocalFileStore(
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str(Path(__file__).parent.parent / "multi_vector_retriever_metadata")
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)
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