hybrid_search() — langchain Function Reference

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

Function python LangChainCore Runnables

Entity Profile

LangChainCore→ Runnables→ hybrid_search() — langchain Function Reference

Dependency Diagram

graph TD
  eab54379_c553_ad20_cd24_933c0ff0a730["hybrid_search()"]
  babbef04_3a0c_25f4_58a8_9d3209d5867e["Chroma"]
  eab54379_c553_ad20_cd24_933c0ff0a730 -->|defined in| babbef04_3a0c_25f4_58a8_9d3209d5867e
  style eab54379_c553_ad20_cd24_933c0ff0a730 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/chroma/langchain_chroma/vectorstores.py lines 684–728

    def hybrid_search(self, search: Search) -> list[Document]:
        """Run hybrid search with Chroma.

        Args:
            search: The Search configuration for hybrid search.

        Returns:
            A list of documents resulting from the search operation.

        Example:
            from chromadb import Search, K, Knn, Rrf

            # Create RRF ranking with text query
            hybrid_rank = Rrf(
                ranks=[
                    Knn(query="query", return_rank=True, limit=300),
                    Knn(query="query learning applications", key="sparse_embedding")
                ],
                weights=[2.0, 1.0],  # Dense 2x more important
                k=60
            )

            # Build complete the search strategy
            search = (Search()
                .where(
                    (K("language") == "en") &
                    (K("year") >= 2020)
                )
                .rank(hybrid_rank)
                .limit(10)
                .select(K.DOCUMENT, K.SCORE, "title", "year")
            )

            results = vector_store.hybrid_search(search)
        """
        results = self._collection.search(search)
        return [
            Document(
                page_content=record["document"],
                metadata=record["metadata"],
                id=record["id"],
            )
            for record in results.rows()[0]
            if record["document"] is not None
        ]