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103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
from typing import Any, Dict, List, Optional
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, Extra, Field
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DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
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class HuggingFaceEmbeddings(BaseModel, Embeddings):
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"""HuggingFace sentence_transformers embedding models.
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To use, you should have the ``sentence_transformers`` python package installed.
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Example:
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.. code-block:: python
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from langchain_huggingface import HuggingFaceEmbeddings
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model_name = "sentence-transformers/all-mpnet-base-v2"
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model_kwargs = {'device': 'cpu'}
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encode_kwargs = {'normalize_embeddings': False}
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hf = HuggingFaceEmbeddings(
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model_name=model_name,
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model_kwargs=model_kwargs,
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encode_kwargs=encode_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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cache_folder: Optional[str] = None
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"""Path to store models.
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Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Keyword arguments to pass to the Sentence Transformer model, such as `device`,
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`prompts`, `default_prompt_name`, `revision`, `trust_remote_code`, or `token`.
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See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer"""
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encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Keyword arguments to pass when calling the `encode` method of the Sentence
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Transformer model, such as `prompt_name`, `prompt`, `batch_size`, `precision`,
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`normalize_embeddings`, and more.
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See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
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multi_process: bool = False
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"""Run encode() on multiple GPUs."""
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show_progress: bool = False
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"""Whether to show a progress bar."""
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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 # type: ignore[import]
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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.SentenceTransformer(
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self.model_name, cache_folder=self.cache_folder, **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 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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import sentence_transformers # type: ignore[import]
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texts = list(map(lambda x: x.replace("\n", " "), texts))
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if self.multi_process:
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pool = self.client.start_multi_process_pool()
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embeddings = self.client.encode_multi_process(texts, pool)
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sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
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else:
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embeddings = self.client.encode(
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texts, show_progress_bar=self.show_progress, **self.encode_kwargs
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)
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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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return self.embed_documents([text])[0]
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