Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/â€‹OData connectionChainsMemoryAgentsCallbacksModulesGuidesEvaluationString EvaluatorsComparison EvaluatorsCustom Pairwise EvaluatorPairwise Embedding DistancePairwise String ComparisonTrajectory EvaluatorsExamplesDebuggingDeploymentLangSmithModel ComparisonEcosystemAdditional resourcesGuidesEvaluationComparison EvaluatorsPairwise Embedding DistanceOn this pagePairwise Embedding DistanceOne 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_evaluatorevaluator = 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 Metricâ€‹By default, the evalutor 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'>,