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 grpc_metadata: Sequence [Required]#

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]) – List of text to embed.
Returns:: List of embeddings.
Return type:: List[List[float]]

async aembed_query(text: str) → List[float]#

Asynchronous Embed query text.

Parameters:: text (str) – Text to embed.
Returns:: Embedding.
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

YandexGPT