Source code for langchain_community.embeddings.huggingface

from typing import Any, Dict, List, Optional

import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, Field, SecretStr

DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
DEFAULT_BGE_MODEL = "BAAI/bge-large-en"
DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
DEFAULT_QUERY_INSTRUCTION = (
    "Represent the question for retrieving supporting documents: "
)
DEFAULT_QUERY_BGE_INSTRUCTION_EN = (
    "Represent this question for searching relevant passages: "
)
DEFAULT_QUERY_BGE_INSTRUCTION_ZH = "为这个句子生成表示以用于检索相关文章:"


[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_community.embeddings 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 model.""" encode_kwargs: Dict[str, Any] = Field(default_factory=dict) """Keyword arguments to pass when calling the `encode` method of the model.""" 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 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 = 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 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]
[docs]class HuggingFaceInstructEmbeddings(BaseModel, Embeddings): """Wrapper around sentence_transformers embedding models. To use, you should have the ``sentence_transformers`` and ``InstructorEmbedding`` python packages installed. Example: .. code-block:: python from langchain_community.embeddings import HuggingFaceInstructEmbeddings model_name = "hkunlp/instructor-large" model_kwargs = {'device': 'cpu'} encode_kwargs = {'normalize_embeddings': True} hf = HuggingFaceInstructEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs ) """ client: Any #: :meta private: model_name: str = DEFAULT_INSTRUCT_MODEL """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 model.""" encode_kwargs: Dict[str, Any] = Field(default_factory=dict) """Keyword arguments to pass when calling the `encode` method of the model.""" embed_instruction: str = DEFAULT_EMBED_INSTRUCTION """Instruction to use for embedding documents.""" query_instruction: str = DEFAULT_QUERY_INSTRUCTION """Instruction to use for embedding query.""" def __init__(self, **kwargs: Any): """Initialize the sentence_transformer.""" super().__init__(**kwargs) try: from InstructorEmbedding import INSTRUCTOR self.client = INSTRUCTOR( self.model_name, cache_folder=self.cache_folder, **self.model_kwargs ) except ImportError as e: raise ImportError("Dependencies for InstructorEmbedding not found.") from e class Config: """Configuration for this pydantic object.""" extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a HuggingFace instruct model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ instruction_pairs = [[self.embed_instruction, text] for text in texts] embeddings = self.client.encode(instruction_pairs, **self.encode_kwargs) return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]: """Compute query embeddings using a HuggingFace instruct model. Args: text: The text to embed. Returns: Embeddings for the text. """ instruction_pair = [self.query_instruction, text] embedding = self.client.encode([instruction_pair], **self.encode_kwargs)[0] return embedding.tolist()
[docs]class HuggingFaceBgeEmbeddings(BaseModel, Embeddings): """HuggingFace sentence_transformers embedding models. To use, you should have the ``sentence_transformers`` python package installed. To use Nomic, make sure the version of ``sentence_transformers`` >= 2.3.0. Bge Example: .. code-block:: python from langchain_community.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en" model_kwargs = {'device': 'cpu'} encode_kwargs = {'normalize_embeddings': True} hf = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs ) Nomic Example: .. code-block:: python from langchain_community.embeddings import HuggingFaceBgeEmbeddings model_name = "nomic-ai/nomic-embed-text-v1" model_kwargs = { 'device': 'cpu', 'trust_remote_code':True } encode_kwargs = {'normalize_embeddings': True} hf = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction = "search_query:", embed_instruction = "search_document:" ) """ client: Any #: :meta private: model_name: str = DEFAULT_BGE_MODEL """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 model.""" encode_kwargs: Dict[str, Any] = Field(default_factory=dict) """Keyword arguments to pass when calling the `encode` method of the model.""" query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN """Instruction to use for embedding query.""" embed_instruction: str = "" """Instruction to use for embedding document.""" 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.SentenceTransformer( self.model_name, cache_folder=self.cache_folder, **self.model_kwargs ) if "-zh" in self.model_name: self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH class Config: """Configuration for this pydantic object.""" extra = 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. """ texts = [self.embed_instruction + t.replace("\n", " ") for t in texts] embeddings = self.client.encode(texts, **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. """ text = text.replace("\n", " ") embedding = self.client.encode( self.query_instruction + text, **self.encode_kwargs ) return embedding.tolist()
[docs]class HuggingFaceInferenceAPIEmbeddings(BaseModel, Embeddings): """Embed texts using the HuggingFace API. Requires a HuggingFace Inference API key and a model name. """ api_key: SecretStr """Your API key for the HuggingFace Inference API.""" model_name: str = "sentence-transformers/all-MiniLM-L6-v2" """The name of the model to use for text embeddings.""" api_url: Optional[str] = None """Custom inference endpoint url. None for using default public url.""" @property def _api_url(self) -> str: return self.api_url or self._default_api_url @property def _default_api_url(self) -> str: return ( "https://api-inference.huggingface.co" "/pipeline" "/feature-extraction" f"/{self.model_name}" ) @property def _headers(self) -> dict: return {"Authorization": f"Bearer {self.api_key.get_secret_value()}"}
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Get the embeddings for a list of texts. Args: texts (Documents): A list of texts to get embeddings for. Returns: Embedded texts as List[List[float]], where each inner List[float] corresponds to a single input text. Example: .. code-block:: python from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings hf_embeddings = HuggingFaceInferenceAPIEmbeddings( api_key="your_api_key", model_name="sentence-transformers/all-MiniLM-l6-v2" ) texts = ["Hello, world!", "How are you?"] hf_embeddings.embed_documents(texts) """ # noqa: E501 response = requests.post( self._api_url, headers=self._headers, json={ "inputs": texts, "options": {"wait_for_model": True, "use_cache": True}, }, ) return response.json()
[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]