Pairwise embedding distance

One way to measure the similarity (or dissimilarity) between two predictions on a shared or similar input is to embed the predictions and compute a vector distance between the two embeddings.^[1]

You can load the pairwise_embedding_distance evaluator to do this.

Note: This returns a distance score, meaning that the lower the number, the more similar the outputs are, according to their embedded representation.

Check out the reference docs for the PairwiseEmbeddingDistanceEvalChain for more info.

from langchain.evaluation import load_evaluator

evaluator = load_evaluator("pairwise_embedding_distance")

evaluator.evaluate_string_pairs(
    prediction="Seattle is hot in June", prediction_b="Seattle is cool in June."
)

{'score': 0.0966466944859925}

evaluator.evaluate_string_pairs(
    prediction="Seattle is warm in June", prediction_b="Seattle is cool in June."
)

{'score': 0.03761174337464557}

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 EmbeddingDistance

list(EmbeddingDistance)

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

evaluator = load_evaluator(
    "pairwise_embedding_distance", distance_metric=EmbeddingDistance.EUCLIDEAN
)

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 HuggingFaceEmbeddings

embedding_model = HuggingFaceEmbeddings()
hf_evaluator = load_evaluator("pairwise_embedding_distance", embeddings=embedding_model)

hf_evaluator.evaluate_string_pairs(
    prediction="Seattle is hot in June", prediction_b="Seattle is cool in June."
)

{'score': 0.5486443280477362}

hf_evaluator.evaluate_string_pairs(
    prediction="Seattle is warm in June", prediction_b="Seattle is cool in June."
)

{'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 `PairwiseStringDistanceEvalChain`), though it tends to be less reliable than evaluators that use the LLM directly (such as the `PairwiseStringEvalChain`)