HuggingFaceEmbeddings#

class langchain_community.embeddings.huggingface.HuggingFaceEmbeddings[source]#

Bases: BaseModel, Embeddings

Deprecated since version 0.2.2: Use langchain_huggingface.HuggingFaceEmbeddings instead.

HuggingFace sentence_transformers embedding models.

To use, you should have the sentence_transformers python package installed.

Example

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
)

Initialize the sentence_transformer.

param cache_folder: str | None = None#: Path to store models. Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.

param encode_kwargs: Dict[str, Any] [Optional]#: 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

param model_kwargs: Dict[str, Any] [Optional]#: 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

param model_name: str = 'sentence-transformers/all-mpnet-base-v2'#: Model name to use.

param multi_process: bool = False#: Run encode() on multiple GPUs.

param show_progress: bool = False#: Whether to show a progress bar.

async aembed_documents(texts: List[str]) → List[List[float]]#

Asynchronous Embed search docs.

Parameters:: texts (List[str]) – List of text to embed.
Returns:: List of embeddings.
Return type:: List[List[float]]

async aembed_query(text: str) → List[float]#

Asynchronous Embed query text.

Parameters:: text (str) – Text to embed.
Returns:: Embedding.
Return type:: List[float]

embed_documents(texts: List[str]) → List[List[float]][source]#

Compute doc embeddings using a HuggingFace transformer model.

Parameters:: texts (List[str]) – The list of texts to embed.
Returns:: List of embeddings, one for each text.
Return type:: List[List[float]]

embed_query(text: str) → List[float][source]#

Compute query embeddings using a HuggingFace transformer model.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

Examples using HuggingFaceEmbeddings