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.

Example

from langchain_community.embeddings.yandex import YandexGPTEmbeddings
embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", folder_id=<folder-id>)

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 disable_request_logging: bool = False

YandexGPT API logs all request data by default. If you provide personal data, confidential information, disable logging.

param doc_model_name: str = 'text-search-doc'

Doc model name to use.

param doc_model_uri: str = ''

Doc model uri to use.

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' (alias 'query_model_name')

Query model name to use.

param model_uri: str = '' (alias 'query_model_uri')

Query 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.

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]

embed_documents(texts: List[str]) List[List[float]][source]

Embed documents using a YandexGPT embeddings models.

Parameters

texts (List[str]) – The list of texts to embed.

Returns

List of embeddings, one for each text.

Return type

List[List[float]]

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

Embed a query using a YandexGPT embeddings models.

Parameters

text (str) – The text to embed.

Returns

Embeddings for the text.

Return type

List[float]

Examples using YandexGPTEmbeddings