'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 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("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