langchain_community.embeddings.huggingface.HuggingFaceEmbeddings

class langchain_community.embeddings.huggingface.HuggingFaceEmbeddings

Bases: BaseModel, Embeddings

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: Optional[str] = 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]) –

Return type

List[List[float]]

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

Asynchronous Embed query text.

Parameters

text (str) –

Return type

List[float]

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Parameters

• _fields_set (Optional[SetStr]) –

• values (Any) –

Return type

Model

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

• include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

• exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

• update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep (bool) – set to True to make a deep copy of the model

• self (Model) –

Returns

new model instance

Return type

Model

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters

• include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• by_alias (bool) –

• skip_defaults (Optional[bool]) –

• exclude_unset (bool) –

• exclude_defaults (bool) –

• exclude_none (bool) –

Return type

DictStrAny

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

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]

Compute query embeddings using a HuggingFace transformer model.

Parameters

text (str) – The text to embed.

Returns

Embeddings for the text.

Return type

List[float]

classmethod from_orm(obj: Any) → Model

Parameters

obj (Any) –

Return type

Model

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

Parameters

• include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• by_alias (bool) –

• skip_defaults (Optional[bool]) –

• exclude_unset (bool) –

• exclude_defaults (bool) –

• exclude_none (bool) –

• encoder (Optional[Callable[[Any], Any]]) –

• models_as_dict (bool) –

• dumps_kwargs (Any) –

Return type

unicode

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

Parameters

• path (Union[str, Path]) –

• content_type (unicode) –

• encoding (unicode) –

• proto (Protocol) –

• allow_pickle (bool) –

Return type

Model

classmethod parse_obj(obj: Any) → Model

Parameters

obj (Any) –

Return type

Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

Parameters

• b (Union[str, bytes]) –

• content_type (unicode) –

• encoding (unicode) –

• proto (Protocol) –

• allow_pickle (bool) –

Return type

Model

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny

Parameters

• by_alias (bool) –

• ref_template (unicode) –

Return type

DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode

Parameters

• by_alias (bool) –

• ref_template (unicode) –

• dumps_kwargs (Any) –

Return type

unicode

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters

localns (Any) –

Return type

None

classmethod validate(value: Any) → Model

Parameters

value (Any) –

Return type

Model

Examples using HuggingFaceEmbeddings

• %pip install –upgrade –quiet surrealdb langchain langchain-community

• Create collection if running for the first time. If the collection

• Ensure that all we need is installed

• Equivalent to SentenceTransformerEmbeddings(model_name=”all-MiniLM-L6-v2”)

• Get 3 diff embeddings.

• Get embeddings.

• Hugging Face

• OR

• OR (depending on Python version)

• Pip install necessary package

• Refresh is required for server use

• VDMS

• You can load by enum or by raw python string

• default metric is angular

• huggingfacehub.md

• os.environ[“LANGCHAIN_TRACING_V2”] = “true”

• pairwise_embedding_distance.md

• scann.md

• self-query-qdrant

• tiledb.md