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