mirror of
https://github.com/hwchase17/langchain
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64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
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from typing import Any, Dict, List, Tuple
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from langchain_core.pydantic_v1 import BaseModel, Extra, Field
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from langchain_community.cross_encoders.base import BaseCrossEncoder
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DEFAULT_MODEL_NAME = "BAAI/bge-reranker-base"
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class HuggingFaceCrossEncoder(BaseModel, BaseCrossEncoder):
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"""HuggingFace cross encoder models.
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Example:
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.. code-block:: python
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from langchain_community.cross_encoders import HuggingFaceCrossEncoder
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model_name = "BAAI/bge-reranker-base"
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model_kwargs = {'device': 'cpu'}
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hf = HuggingFaceCrossEncoder(
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model_name=model_name,
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model_kwargs=model_kwargs
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)
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"""
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client: Any #: :meta private:
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model_name: str = DEFAULT_MODEL_NAME
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"""Model name to use."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Keyword arguments to pass to the model."""
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def __init__(self, **kwargs: Any):
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"""Initialize the sentence_transformer."""
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super().__init__(**kwargs)
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try:
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import sentence_transformers
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except ImportError as exc:
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raise ImportError(
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"Could not import sentence_transformers python package. "
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"Please install it with `pip install sentence-transformers`."
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) from exc
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self.client = sentence_transformers.CrossEncoder(
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self.model_name, **self.model_kwargs
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)
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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def score(self, text_pairs: List[Tuple[str, str]]) -> List[float]:
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"""Compute similarity scores using a HuggingFace transformer model.
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Args:
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text_pairs: The list of text text_pairs to score the similarity.
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Returns:
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List of scores, one for each pair.
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"""
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scores = self.client.predict(text_pairs)
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return scores
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