langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings

class langchain_nvidia_ai_endpoints.embeddings.NVIDIAEmbeddings[source]

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]][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_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