langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings

class langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings[source]

Bases: BaseModel, 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 NVIDIAEmbeddings embedder.

This class provides access to a NVIDIA NIM for embedding. By default, it connects to a hosted NIM, but can be configured to connect to a local NIM using the base_url parameter. An API key is required to connect to the hosted NIM.

Parameters
  • model (str) – The model to use for embedding.

  • nvidia_api_key (str) – The API key to use for connecting to the hosted NIM.

  • api_key (str) – Alternative to nvidia_api_key.

  • base_url (str) – The base URL of the NIM to connect to.

  • trucate (str) – “NONE”, “START”, “END”, truncate input text if it exceeds the model’s context length. Default is “NONE”, which raises an error if an input is too long.

API Key: - The recommended way to provide the API key is through the NVIDIA_API_KEY

environment variable.

param base_url: str = 'https://integrate.api.nvidia.com/v1'

Base url for model listing an invocation

param max_batch_size: int = 50
param model: str = 'NV-Embed-QA'

Name of the model to invoke

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

(DEPRECATED) 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]][source]

Input pathway for document embeddings.

Parameters

texts (List[str]) –

Return type

List[List[float]]

embed_query(text: str) List[float][source]

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_models(**kwargs: Any) List[Model][source]

Get a list of available models that work with NVIDIAEmbeddings.

Parameters

kwargs (Any) –

Return type

List[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

property available_models: List[Model]

Get a list of available models that work with NVIDIAEmbeddings.