similarity_search_with_score() — langchain Function Reference

Architecture documentation for the similarity_search_with_score() function in vectorstores.py from the langchain codebase.

Function python LangChainCore MessageInterface calls 1 called by 1

Entity Profile

LangChainCore→ MessageInterface→ similarity_search_with_score() — langchain Function Reference

Dependency Diagram

graph TD
  0a7f571a_baf1_50bb_32f7_1d2d10e43076["similarity_search_with_score()"]
  babbef04_3a0c_25f4_58a8_9d3209d5867e["Chroma"]
  0a7f571a_baf1_50bb_32f7_1d2d10e43076 -->|defined in| babbef04_3a0c_25f4_58a8_9d3209d5867e
  4c9c3785_e011_cdde_a2f4_4297d10bd826["similarity_search()"]
  4c9c3785_e011_cdde_a2f4_4297d10bd826 -->|calls| 0a7f571a_baf1_50bb_32f7_1d2d10e43076
  fa232e14_1436_226d_41f9_e49eff05c0d0["_results_to_docs_and_scores()"]
  0a7f571a_baf1_50bb_32f7_1d2d10e43076 -->|calls| fa232e14_1436_226d_41f9_e49eff05c0d0
  style 0a7f571a_baf1_50bb_32f7_1d2d10e43076 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/chroma/langchain_chroma/vectorstores.py lines 817–857

    def similarity_search_with_score(
        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, float]]:
        """Run similarity search with Chroma with distance.

        Args:
            query: Query text to search for.
            k: Number of results to return.
            filter: Filter by metadata.
            where_document: dict used to filter by 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
            distance in float for each. Lower score represents more similarity.
        """
        if self._embedding_function is None:
            results = self.__query_collection(
                query_texts=[query],
                n_results=k,
                where=filter,
                where_document=where_document,
                **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,
                **kwargs,
            )

        return _results_to_docs_and_scores(results)