_euclidean_relevance_score_fn() — langchain Function Reference

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

Function python DataProcessing DocumentLoaders calls 1

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DataProcessing→ DocumentLoaders→ _euclidean_relevance_score_fn() — langchain Function Reference

Dependency Diagram

graph TD
  680bde2d_2c90_9b48_dfe7_46fa0de5f49d["_euclidean_relevance_score_fn()"]
  6c336ac6_f55c_1ad7_6db3_73dbd71fb625["VectorStore"]
  680bde2d_2c90_9b48_dfe7_46fa0de5f49d -->|defined in| 6c336ac6_f55c_1ad7_6db3_73dbd71fb625
  0cd8a5fb_f912_cf41_5d28_286a9342680c["embeddings()"]
  680bde2d_2c90_9b48_dfe7_46fa0de5f49d -->|calls| 0cd8a5fb_f912_cf41_5d28_286a9342680c
  style 680bde2d_2c90_9b48_dfe7_46fa0de5f49d fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/core/langchain_core/vectorstores/base.py lines 376–388

    def _euclidean_relevance_score_fn(distance: float) -> float:
        """Return a similarity score on a scale [0, 1]."""
        # The 'correct' relevance function
        # may differ depending on a few things, including:
        # - the distance / similarity metric used by the VectorStore
        # - the scale of your embeddings (OpenAI's are unit normed. Many
        #  others are not!)
        # - embedding dimensionality
        # - etc.
        # This function converts the Euclidean norm of normalized embeddings
        # (0 is most similar, sqrt(2) most dissimilar)
        # to a similarity function (0 to 1)
        return 1.0 - distance / math.sqrt(2)