<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 Useâ€‹The constructor uses OpenAI embeddings by default, but you can configure this however you want. Below, use huggingface local embeddingsfrom langchain.embeddings import HuggingFaceEmbeddingsembedding_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`) PreviousCustom Pairwise EvaluatorNextPairwise String ComparisonSelect the Distance MetricSelect Embeddings to UseCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.