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Showing content from https://python.langchain.com/docs/integrations/text_embedding/yandex below:

YandexGPT | 🦜️🔗 LangChain

This notebook goes over how to use Langchain with YandexGPT embeddings models.

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

%pip install --upgrade --quiet  yandexcloud

First, you should create service account with the ai.languageModels.user role.

Next, you have two authentication options:

• IAM token. You can specify the token in a constructor parameter iam_token or in an environment variable YC_IAM_TOKEN.
• API key You can specify the key in a constructor parameter api_key or in an environment variable YC_API_KEY.

To specify the model you can use model_uri parameter, see the documentation for more details.

By default, the latest version of text-search-query is used from the folder specified in the parameter folder_id or YC_FOLDER_ID environment variable.

from langchain_community.embeddings.yandex import YandexGPTEmbeddings

embeddings = YandexGPTEmbeddings()

text = "This is a test document."

query_result = embeddings.embed_query(text)

doc_result = embeddings.embed_documents([text])

[-0.021392822265625,
 0.096435546875,
 -0.046966552734375,
 -0.0183258056640625,
 -0.00555419921875]

[-0.021392822265625,
 0.096435546875,
 -0.046966552734375,
 -0.0183258056640625,
 -0.00555419921875]

• Embedding model conceptual guide
• Embedding model how-to guides

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