_hamming_distance() — langchain Function Reference

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

Function python LangChainCore LanguageModelBase calls 2

Entity Profile

LangChainCore→ LanguageModelBase→ _hamming_distance() — langchain Function Reference

Dependency Diagram

graph TD
  684dd98c_0ef0_d4ae_a6da_63af19e8c1d5["_hamming_distance()"]
  dc2d52f5_736c_0ec2_ad57_0e2fdaa94e04["_EmbeddingDistanceChainMixin"]
  684dd98c_0ef0_d4ae_a6da_63af19e8c1d5 -->|defined in| dc2d52f5_736c_0ec2_ad57_0e2fdaa94e04
  91b8ba41_75bf_8afd_e893_d85d055226da["_check_numpy()"]
  684dd98c_0ef0_d4ae_a6da_63af19e8c1d5 -->|calls| 91b8ba41_75bf_8afd_e893_d85d055226da
  caa53c2b_01ef_7204_a5e6_ad683c86dbd2["_import_numpy()"]
  684dd98c_0ef0_d4ae_a6da_63af19e8c1d5 -->|calls| caa53c2b_01ef_7204_a5e6_ad683c86dbd2
  style 684dd98c_0ef0_d4ae_a6da_63af19e8c1d5 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/langchain_classic/evaluation/embedding_distance/base.py lines 308–327

    def _hamming_distance(a: Any, b: Any) -> Any:
        """Compute the Hamming distance between two vectors.

        Args:
            a (np.ndarray): The first vector.
            b (np.ndarray): The second vector.

        Returns:
            np.floating: The Hamming distance.
        """
        try:
            from scipy.spatial.distance import hamming

            return hamming(a.flatten(), b.flatten())
        except ImportError:
            if _check_numpy():
                np = _import_numpy()
                return np.mean(a != b)

            return sum(1 for x, y in zip(a, b, strict=False) if x != y) / len(a)