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https://github.com/hwchase17/langchain.git
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844955d6e1
Assigning missed defaults in various classes. Most clients were being
assigned during the `model_validator(mode="before")` step, so this
change should amount to a no-op in those cases.
---
This PR was autogenerated using gritql
```shell
grit apply 'class_definition(name=$C, $body, superclasses=$S) where {
$C <: ! "Config", // Does not work in this scope, but works after class_definition
$body <: block($statements),
$statements <: some bubble assignment(left=$x, right=$y, type=$t) as $A where {
or {
$y <: `Field($z)`,
$x <: "model_config"
}
},
// And has either Any or Optional fields without a default
$statements <: some bubble assignment(left=$x, right=$y, type=$t) as $A where {
$t <: or {
r"Optional.*",
r"Any",
r"Union[None, .*]",
r"Union[.*, None, .*]",
r"Union[.*, None]",
},
$y <: ., // Match empty node
$t => `$t = None`,
},
}
' --language python .
```
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
from typing import Any, Dict, List, Tuple
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from pydantic import BaseModel, ConfigDict, 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 = None #: :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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model_config = ConfigDict(extra="forbid", protected_namespaces=())
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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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# Some models e.g bert-multilingual-passage-reranking-msmarco
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# gives two score not_relevant and relevant as compare with the query.
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if len(scores.shape) > 1: # we are going to get the relevant scores
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scores = map(lambda x: x[1], scores)
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return scores
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