similarity_search_by_vector_with_relevance_scores() — langchain Function Reference

Source Code

libs/partners/chroma/langchain_chroma/vectorstores.py lines 786–815

    def similarity_search_by_vector_with_relevance_scores(
        self,
        embedding: list[float],
        k: int = DEFAULT_K,
        filter: dict[str, str] | None = None,  # noqa: A002
        where_document: dict[str, str] | None = None,
        **kwargs: Any,
    ) -> list[tuple[Document, float]]:
        """Return docs most similar to embedding vector and similarity score.

        Args:
            embedding (List[float]): Embedding to look up documents similar to.
            k: Number of Documents to return.
            filter: Filter by metadata.
            where_document: dict used to filter by the documents.
                    E.g. {"$contains": "hello"}.
            kwargs: Additional keyword arguments to pass to Chroma collection query.

        Returns:
            List of documents most similar to the query text and relevance score
            in float for each. Lower score represents more similarity.
        """
        results = self.__query_collection(
            query_embeddings=[embedding],
            n_results=k,
            where=filter,
            where_document=where_document,
            **kwargs,
        )
        return _results_to_docs_and_scores(results)