similarity_search_by_vector() — langchain Function Reference

Architecture documentation for the similarity_search_by_vector() function in base.py from the langchain codebase.

Function python DataProcessing VectorStores

Entity Profile

DataProcessing→ VectorStores→ similarity_search_by_vector() — langchain Function Reference

Dependency Diagram

graph TD
  0e206dd1_b1ce_3417_838f_3147b795deb4["similarity_search_by_vector()"]
  6c336ac6_f55c_1ad7_6db3_73dbd71fb625["VectorStore"]
  0e206dd1_b1ce_3417_838f_3147b795deb4 -->|defined in| 6c336ac6_f55c_1ad7_6db3_73dbd71fb625
  style 0e206dd1_b1ce_3417_838f_3147b795deb4 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/langchain_core/vectorstores/base.py lines 624–637

    def similarity_search_by_vector(
        self, embedding: list[float], k: int = 4, **kwargs: Any
    ) -> list[Document]:
        """Return docs most similar to embedding vector.

        Args:
            embedding: Embedding to look up documents similar to.
            k: Number of `Document` objects to return.
            **kwargs: Arguments to pass to the search method.

        Returns:
            List of `Document` objects most similar to the query vector.
        """
        raise NotImplementedError