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# `langchain_nvidia_ai_endpoints.embeddings`.NVIDIAEmbeddings

class langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings

  Bases: `_NVIDIAClient`, `Embeddings`

  Client to NVIDIA embeddings models.

  Fields: - model: str, the name of the model to use - truncate: “NONE”, “START”, “END”, truncate input text if it exceeds the model’s

  > maximum token length. Default is “NONE”, which raises an error if an input is too long.

  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 client: NVEModel = <class 'langchain_nvidia_ai_endpoints._common.NVEModel'>
  param curr_mode: _MODE_TYPE = 'nvidia'
  param infer_endpoint: str = '{base_url}/embeddings'
  param max_batch_size: int = 50
  param max_length: int = 2048
    Constraints

      - minimum = 1

      - maximum = 2048

  param model: str = 'ai-embed-qa-4'

    Name of the model to invoke

  param model_type: Optional[Literal['passage', 'query']] = None

    The type of text to be embedded.

  param truncate: Literal['NONE', 'START', 'END'] = 'NONE'

    Truncate input text if it exceeds the model’s maximum token length. Default is ‘NONE’, which raises an error if an input is too long.

  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]]

    Input pathway for document embeddings.

    Parameters

      texts (List[str]) –

    Return type

      List[List[float]]

  embed_query(text: str) → List[float]

    Input pathway for query embeddings.

    Parameters

      text (str) –

    Return type

      List[float]

  classmethod from_orm(obj: Any) → Model
    Parameters

      obj (Any) –

    Return type

      Model

  classmethod get_available_functions(mode: Optional[Literal['catalog', 'nvidia', 'nim', 'open', 'openai']] = None, client: Optional[_NVIDIAClient] = None, **kwargs: Any) → List[dict]

    Map the available functions that can be invoked. Callable from class

    Parameters

      - mode (Optional[Literal['catalog', 'nvidia', 'nim', 'open', 'openai']]) –

      - client (Optional[_NVIDIAClient]) –

      - kwargs (Any) –

    Return type

      List[dict]

  classmethod get_available_models(mode: Optional[Literal['catalog', 'nvidia', 'nim', 'open', 'openai']] = None, client: Optional[_NVIDIAClient] = None, list_all: bool = False, filter: Optional[str] = None, **kwargs: Any) → List[Model]

    Map the available models that can be invoked. Callable from class

    Parameters

      - mode (Optional[Literal['catalog', 'nvidia', 'nim', 'open', 'openai']]) –

      - client (Optional[_NVIDIAClient]) –

      - list_all (bool) –

      - filter (Optional[str]) –

      - kwargs (Any) –

    Return type

      List[Model]

  get_binding_model() → Optional[str]

    Get the model to bind to the client as default payload argument

    Return type

      Optional[str]

  get_model_details(model: Optional[str] = None) → dict

    Get more meta-details about a model retrieved by a given name

    Parameters

      model (Optional[str]) –

    Return type

      dict

  classmethod is_lc_serializable() → bool
    Return type

      bool

  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

  mode(mode: Optional[Literal['catalog', 'nvidia', 'nim', 'open', 'openai']] = 'nvidia', base_url: Optional[str] = None, model: Optional[str] = None, api_key: Optional[str] = None, infer_path: Optional[str] = None, models_path: Optional[str] = '{base_url}/models', force_mode: bool = False, force_clone: bool = True, **kwargs: Any) → Any

    Return a client swapped to a different mode

    Parameters

      - mode (Optional[Literal['catalog', 'nvidia', 'nim', 'open', 'openai']]) –

      - base_url (Optional[str]) –

      - model (Optional[str]) –

      - api_key (Optional[str]) –

      - infer_path (Optional[str]) –

      - models_path (Optional[str]) –

      - force_mode (bool) –

      - force_clone (bool) –

      - kwargs (Any) –

    Return type

      Any

  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

  property available_functions: List[dict]

    Map the available functions that can be invoked.

  property available_models: List[Model]

    Map the available models that can be invoked.

  property lc_attributes: Dict[str, Any]
  property lc_secrets: Dict[str, str]

# Examples using NVIDIAEmbeddings

- NVIDIA

- del os.environ[‘NVIDIA_API_KEY’] ## delete key and reset