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") )