similarity_search() — langchain Function Reference

Architecture documentation for the similarity_search() function in qdrant.py from the langchain codebase.

Function python LangChainCore Runnables calls 1

Entity Profile

LangChainCore→ Runnables→ similarity_search() — langchain Function Reference

Dependency Diagram

graph TD
  7d69a481_b834_1cc5_a801_d2c961c57680["similarity_search()"]
  671b47a0_cdd3_a89d_e90f_0631a4bd67d3["QdrantVectorStore"]
  7d69a481_b834_1cc5_a801_d2c961c57680 -->|defined in| 671b47a0_cdd3_a89d_e90f_0631a4bd67d3
  554efa04_b226_2427_5014_2b27a2b0b32f["similarity_search_with_score()"]
  7d69a481_b834_1cc5_a801_d2c961c57680 -->|calls| 554efa04_b226_2427_5014_2b27a2b0b32f
  style 7d69a481_b834_1cc5_a801_d2c961c57680 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/qdrant/langchain_qdrant/qdrant.py lines 520–549

    def similarity_search(
        self,
        query: str,
        k: int = 4,
        filter: models.Filter | None = None,  # noqa: A002
        search_params: models.SearchParams | None = None,
        offset: int = 0,
        score_threshold: float | None = None,
        consistency: models.ReadConsistency | None = None,
        hybrid_fusion: models.FusionQuery | None = None,
        **kwargs: Any,
    ) -> list[Document]:
        """Return docs most similar to query.

        Returns:
            List of `Document` objects most similar to the query.

        """
        results = self.similarity_search_with_score(
            query,
            k,
            filter=filter,
            search_params=search_params,
            offset=offset,
            score_threshold=score_threshold,
            consistency=consistency,
            hybrid_fusion=hybrid_fusion,
            **kwargs,
        )
        return list(map(itemgetter(0), results))