HuggingFaceBgeEmbeddings#

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

Bases: 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:

from langchain_community.embeddings import HuggingFaceBgeEmbeddings

model_name = "BAAI/bge-large-en-v1.5"
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:

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:"
)

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 embed_instruction: str = ''#: Instruction to use for embedding document.

param encode_kwargs: Dict[str, Any] [Optional]#: Keyword arguments to pass when calling the encode method of the model.

param model_kwargs: Dict[str, Any] [Optional]#: Keyword arguments to pass to the model.

param model_name: str = 'BAAI/bge-large-en'#: Model name to use.

param query_instruction: str = 'Represent this question for searching relevant passages: '#: Instruction to use for embedding query.

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 HuggingFaceBgeEmbeddings