# PairwiseEmbeddingDistanceEvalChain

> **Class** in `langchain_classic`

📖 [View in docs](https://reference.langchain.com/python/langchain-classic/evaluation/embedding_distance/base/PairwiseEmbeddingDistanceEvalChain)

Use embedding distances to score semantic difference between two predictions.

Examples:
>>> chain = PairwiseEmbeddingDistanceEvalChain()
>>> result = chain.evaluate_string_pairs(prediction="Hello", prediction_b="Hi")
>>> print(result)
{'score': 0.5}

## Signature

```python
PairwiseEmbeddingDistanceEvalChain()
```

## Extends

- `_EmbeddingDistanceChainMixin`
- `PairwiseStringEvaluator`

## Properties

- `input_keys`
- `evaluation_name`

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[View source on GitHub](https://github.com/langchain-ai/langchain/blob/ee95ad6907f5eab94644183393a20aa2a032bb19/libs/langchain/langchain_classic/evaluation/embedding_distance/base.py#L506)