langchain_community.embeddings.tensorflow_hub
.TensorflowHubEmbeddings¶
- class langchain_community.embeddings.tensorflow_hub.TensorflowHubEmbeddings[source]¶
Bases:
BaseModel
,Embeddings
TensorflowHub embedding models.
To use, you should have the
tensorflow_text
python package installed.Example
from langchain_community.embeddings import TensorflowHubEmbeddings url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3" tf = TensorflowHubEmbeddings(model_url=url)
Initialize the tensorflow_hub and tensorflow_text.
- param model_url: str = 'https://tfhub.dev/google/universal-sentence-encoder-multilingual/3'¶
Model name to use.
- 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]¶
Compute doc embeddings using a TensorflowHub embedding model.
- Parameters
texts – The list of texts to embed.
- Returns
List of embeddings, one for each text.
- embed_query(text: str) List[float] [source]¶
Compute query embeddings using a TensorflowHub embedding 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 ¶