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from langchain.embeddings import HuggingFaceBgeEmbeddings |
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from langchain.embeddings import HuggingFaceEmbeddings |
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def get_embeddings_function(version = "v1.2"): |
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if version == "v1.2": |
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model_name = "BAAI/bge-base-en-v1.5" |
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encode_kwargs = {'normalize_embeddings': True} |
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embeddings_function = HuggingFaceBgeEmbeddings( |
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model_name=model_name, |
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encode_kwargs=encode_kwargs, |
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query_instruction="Represent this sentence for searching relevant passages: " |
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) |
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else: |
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embeddings_function = HuggingFaceEmbeddings(model_name = "sentence-transformers/multi-qa-mpnet-base-dot-v1") |
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return embeddings_function |