similarity_search() — langchain Function Reference

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

Function python CoreAbstractions RunnableInterface calls 1

Entity Profile

CoreAbstractions→ RunnableInterface→ similarity_search() — langchain Function Reference

Dependency Diagram

graph TD
  c2e030db_b7f7_376a_9ad2_1a06dee8dcd8["similarity_search()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab["Chroma"]
  c2e030db_b7f7_376a_9ad2_1a06dee8dcd8 -->|defined in| d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab
  931a8250_c45c_2dfc_c4d4_c2956f5c59b1["similarity_search_with_score()"]
  c2e030db_b7f7_376a_9ad2_1a06dee8dcd8 -->|calls| 931a8250_c45c_2dfc_c4d4_c2956f5c59b1
  style c2e030db_b7f7_376a_9ad2_1a06dee8dcd8 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/chroma/langchain_chroma/vectorstores.py lines 730–754

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

        Args:
            query: Query text to search for.
            k: Number of results to return.
            filter: Filter by metadata.
            kwargs: Additional keyword arguments to pass to Chroma collection query.

        Returns:
            List of documents most similar to the query text.
        """
        docs_and_scores = self.similarity_search_with_score(
            query,
            k,
            filter=filter,
            **kwargs,
        )
        return [doc for doc, _ in docs_and_scores]