## Embedding Distance

[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/embedding_distance.ipynb)

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To measure semantic similarity (or dissimilarity) between a prediction and a reference label string, you could use a vector distance metric the two embedded representations using the `embedding_distance` evaluator.[\[1\]](#cite_note-1)

**Note:** This returns a **distance** score, meaning that the lower the number, the **more** similar the prediction is to the reference, according to their embedded representation.

Check out the reference docs for the [EmbeddingDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html#langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain) for more info.

```
from langchain.evaluation import load_evaluatorevaluator = load_evaluator("embedding_distance")
```

```
evaluator.evaluate_strings(prediction="I shall go", reference="I shan't go")
```

```
{'score': 0.0966466944859925}
```

```
evaluator.evaluate_strings(prediction="I shall go", reference="I will go")
```

```
{'score': 0.03761174337464557}
```

## Select the Distance Metric[​](#select-the-distance-metric "Direct link to Select the Distance Metric")

By default, the evaluator uses cosine distance. You can choose a different distance metric if you’d like.

```
from langchain.evaluation import EmbeddingDistancelist(EmbeddingDistance)
```

```
[<EmbeddingDistance.COSINE: 'cosine'>, <EmbeddingDistance.EUCLIDEAN: 'euclidean'>, <EmbeddingDistance.MANHATTAN: 'manhattan'>, <EmbeddingDistance.CHEBYSHEV: 'chebyshev'>, <EmbeddingDistance.HAMMING: 'hamming'>]
```

```
# You can load by enum or by raw python stringevaluator = load_evaluator(    "embedding_distance", distance_metric=EmbeddingDistance.EUCLIDEAN)
```

## Select Embeddings to Use[​](#select-embeddings-to-use "Direct link to Select Embeddings to Use")

The constructor uses `OpenAI` embeddings by default, but you can configure this however you want. Below, use huggingface local embeddings

```
from langchain_community.embeddings import HuggingFaceEmbeddingsembedding_model = HuggingFaceEmbeddings()hf_evaluator = load_evaluator("embedding_distance", embeddings=embedding_model)
```

```
hf_evaluator.evaluate_strings(prediction="I shall go", reference="I shan't go")
```

```
{'score': 0.5486443280477362}
```

```
hf_evaluator.evaluate_strings(prediction="I shall go", reference="I will go")
```

```
{'score': 0.21018880025138598}
```

_1\. Note: When it comes to semantic similarity, this often gives better results than older string distance metrics (such as those in the \[StringDistanceEvalChain\](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string\_distance.base.StringDistanceEvalChain.html#langchain.evaluation.string\_distance.base.StringDistanceEvalChain)), though it tends to be less reliable than evaluators that use the LLM directly (such as the \[QAEvalChain\](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval\_chain.QAEvalChain.html#langchain.evaluation.qa.eval\_chain.QAEvalChain) or \[LabeledCriteriaEvalChain\](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval\_chain.LabeledCriteriaEvalChain.html#langchain.evaluation.criteria.eval\_chain.LabeledCriteriaEvalChain))_

* * *

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