Source code for langchain_huggingface.embeddings.huggingface

from typing import Any, Dict, List, Optional  # type: ignore[import-not-found]

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Field

DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"


[docs]class HuggingFaceEmbeddings(BaseModel, Embeddings): """HuggingFace sentence_transformers embedding models. To use, you should have the ``sentence_transformers`` python package installed. Example: .. code-block:: python from langchain_huggingface import HuggingFaceEmbeddings model_name = "sentence-transformers/all-mpnet-base-v2" model_kwargs = {'device': 'cpu'} encode_kwargs = {'normalize_embeddings': False} hf = HuggingFaceEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs ) """ client: Any #: :meta private: model_name: str = DEFAULT_MODEL_NAME """Model name to use.""" cache_folder: Optional[str] = None """Path to store models. Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Keyword arguments to pass to the Sentence Transformer model, such as `device`, `prompts`, `default_prompt_name`, `revision`, `trust_remote_code`, or `token`. See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer""" encode_kwargs: Dict[str, Any] = Field(default_factory=dict) """Keyword arguments to pass when calling the `encode` method of the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`, `precision`, `normalize_embeddings`, and more. See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode""" multi_process: bool = False """Run encode() on multiple GPUs.""" show_progress: bool = False """Whether to show a progress bar.""" def __init__(self, **kwargs: Any): """Initialize the sentence_transformer.""" super().__init__(**kwargs) try: import sentence_transformers # type: ignore[import] 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.SentenceTransformer( self.model_name, cache_folder=self.cache_folder, **self.model_kwargs ) class Config: """Configuration for this pydantic object.""" extra = "forbid"
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a HuggingFace transformer model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ import sentence_transformers # type: ignore[import] texts = list(map(lambda x: x.replace("\n", " "), texts)) if self.multi_process: pool = self.client.start_multi_process_pool() embeddings = self.client.encode_multi_process(texts, pool) sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool) else: embeddings = self.client.encode( texts, show_progress_bar=self.show_progress, **self.encode_kwargs ) return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]: """Compute query embeddings using a HuggingFace transformer model. Args: text: The text to embed. Returns: Embeddings for the text. """ return self.embed_documents([text])[0]