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