similarity_search_with_vectors() — langchain Function Reference

Source Code

libs/partners/chroma/langchain_chroma/vectorstores.py lines 859–902

    def similarity_search_with_vectors(
        self,
        query: str,
        k: int = DEFAULT_K,
        filter: dict[str, str] | None = None,  # noqa: A002
        where_document: dict[str, str] | None = None,
        **kwargs: Any,
    ) -> list[tuple[Document, np.ndarray]]:
        """Run similarity search with Chroma with vectors.

        Args:
            query: Query text to search for.
            k: Number of results to return.
            filter: Filter by metadata.
            where_document: dict used to filter by the document contents.
                    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
            embedding vectors for each.
        """
        include = ["documents", "metadatas", "embeddings"]
        if self._embedding_function is None:
            results = self.__query_collection(
                query_texts=[query],
                n_results=k,
                where=filter,
                where_document=where_document,
                include=include,
                **kwargs,
            )
        else:
            query_embedding = self._embedding_function.embed_query(query)
            results = self.__query_collection(
                query_embeddings=[query_embedding],
                n_results=k,
                where=filter,
                where_document=where_document,
                include=include,
                **kwargs,
            )

        return _results_to_docs_and_vectors(results)