35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from typing import Any, List
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class MyEmbeddings(HuggingFaceEmbeddings):
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def __init__(self, **kwargs: Any):
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super().__init__(**kwargs)
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Compute doc embeddings using a HuggingFace transformer model.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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texts = list(map(lambda x: x.replace("\n", " "), texts))
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embeddings = self.client.encode(texts, normalize_embeddings=True)
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return embeddings.tolist()
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def embed_query(self, text: str) -> List[float]:
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"""Compute query embeddings using a HuggingFace transformer model.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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text = text.replace("\n", " ")
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embedding = self.client.encode(text, normalize_embeddings=True)
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return embedding.tolist()
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