langchain/docs/versioned_docs/version-0.2.x/integrations/text_embedding/yandex.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# YandexGPT\n",
    "\n",
    "This notebook goes over how to use Langchain with [YandexGPT](https://cloud.yandex.com/en/services/yandexgpt) embeddings models.\n",
    "\n",
    "To use, you should have the `yandexcloud` python package installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --upgrade --quiet  yandexcloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, you should [create service account](https://cloud.yandex.com/en/docs/iam/operations/sa/create) with the `ai.languageModels.user` role.\n",
    "\n",
    "Next, you have two authentication options:\n",
    "- [IAM token](https://cloud.yandex.com/en/docs/iam/operations/iam-token/create-for-sa).\n",
    "    You can specify the token in a constructor parameter `iam_token` or in an environment variable `YC_IAM_TOKEN`.\n",
    "- [API key](https://cloud.yandex.com/en/docs/iam/operations/api-key/create)\n",
    "    You can specify the key in a constructor parameter `api_key` or in an environment variable `YC_API_KEY`.\n",
    "\n",
    "To specify the model you can use `model_uri` parameter, see [the documentation](https://cloud.yandex.com/en/docs/yandexgpt/concepts/models#yandexgpt-embeddings) for more details.\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.embeddings.yandex import YandexGPTEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = YandexGPTEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = embeddings.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_result = embeddings.embed_documents([text])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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    "query_result[:5]"
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   "metadata": {},
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    "doc_result[0][:5]"
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