langchain_community.embeddings.huggingface
.HuggingFaceInferenceAPIEmbeddings¶
- class langchain_community.embeddings.huggingface.HuggingFaceInferenceAPIEmbeddings[source]¶
Bases:
BaseModel
,Embeddings
Embed texts using the HuggingFace API.
Requires a HuggingFace Inference API key and a model name.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
- param api_key: str [Required]¶
Your API key for the HuggingFace Inference API.
- param api_url: Optional[str] = None¶
Custom inference endpoint url. None for using default public url.
- param model_name: str = 'sentence-transformers/all-MiniLM-L6-v2'¶
The name of the model to use for text embeddings.
- async aembed_documents(texts: List[str]) List[List[float]] ¶
Asynchronous Embed search docs.
- async aembed_query(text: str) List[float] ¶
Asynchronous Embed query text.
- 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
- 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 – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – 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 – set to True to make a deep copy of the model
- Returns
new model instance
- 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.
- embed_documents(texts: List[str]) List[List[float]] [source]¶
Get the embeddings for a list of texts.
- Parameters
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
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)
- embed_query(text: str) List[float] [source]¶
Compute query embeddings using a HuggingFace transformer model.
- Parameters
text – The text to embed.
- Returns
Embeddings for the text.
- classmethod from_orm(obj: Any) 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().
- classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- classmethod parse_obj(obj: Any) Model ¶
- classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny ¶
- classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode ¶
- classmethod update_forward_refs(**localns: Any) None ¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- classmethod validate(value: Any) Model ¶