vector_store.cpython-313.pyc•6.06 kB
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Initialize a FAISS vector store.
Args:
dimension: Dimension of the embeddings
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Index a list of documents.
Args:
documents: List of document dictionaries with 'embedding' and other metadata
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Search for similar documents using a query embedding.
Args:
query_embedding: The embedding vector of the query
top_k: Number of results to return
Returns:
List of document dictionaries with similarity scores
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Save the index and documents to disk.
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filepath: Path to save the index
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Load the index and documents from disk.
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filepath: Path to load the index from
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