langchain_community.embeddings.yandex.YandexGPTEmbeddings¶

class langchain_community.embeddings.yandex.YandexGPTEmbeddings[source]¶

Bases: BaseModel, Embeddings

YandexGPT Embeddings models.

To use, you should have the yandexcloud python package installed.

There are two authentication options for the service account with the ai.languageModels.user role:

  • You can specify the token in a constructor parameter iam_token

or in an environment variable YC_IAM_TOKEN. - You can specify the key in a constructor parameter api_key or in an environment variable YC_API_KEY.

To use the default model specify the folder ID in a parameter folder_id or in an environment variable YC_FOLDER_ID. Or specify the model URI in a constructor parameter model_uri

Example

from langchain_community.embeddings.yandex import YandexGPTEmbeddings
embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", model_uri="emb://<folder-id>/text-search-query/latest")

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: SecretStr = ''¶

Yandex Cloud Api Key for service account with the ai.languageModels.user role

Constraints

  • type = string

  • writeOnly = True

  • format = password

param folder_id: str = ''¶

Yandex Cloud folder ID

param iam_token: SecretStr = ''¶

Yandex Cloud IAM token for service account with the ai.languageModels.user role

Constraints

  • type = string

  • writeOnly = True

  • format = password

param max_retries: int = 6¶

Maximum number of retries to make when generating.

param model_name: str = 'text-search-query'¶

Model name to use.

param model_uri: str = ''¶

Model uri to use.

param model_version: str = 'latest'¶

Model version to use.

param sleep_interval: float = 0.0¶

Delay between API requests

param url: str = 'llm.api.cloud.yandex.net:443'¶

The url of the API.

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]¶

Embed documents using a YandexGPT embeddings models.

Parameters

texts – The list of texts to embed.

Returns

List of embeddings, one for each text.

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

Embed a query using a YandexGPT embeddings models.

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¶