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community[minor]: Add YandexGPT embeddings (#14767)
- **Description:** Introducing an ability to work with the [YandexGPT](https://cloud.yandex.com/en/services/yandexgpt) embeddings models. --------- Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>
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Dmitry Tyumentsev
GitHub
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d21f44b484
155
docs/docs/integrations/text_embedding/yandex.ipynb
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155
docs/docs/integrations/text_embedding/yandex.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# YandexGPT\n",
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"\n",
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"This notebook goes over how to use Langchain with [YandexGPT](https://cloud.yandex.com/en/services/yandexgpt) embeddings models.\n",
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"\n",
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"To use, you should have the `yandexcloud` python package installed."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install yandexcloud"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, you should [create service account](https://cloud.yandex.com/en/docs/iam/operations/sa/create) with the `ai.languageModels.user` role.\n",
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"\n",
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"Next, you have two authentication options:\n",
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"- [IAM token](https://cloud.yandex.com/en/docs/iam/operations/iam-token/create-for-sa).\n",
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" You can specify the token in a constructor parameter `iam_token` or in an environment variable `YC_IAM_TOKEN`.\n",
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"- [API key](https://cloud.yandex.com/en/docs/iam/operations/api-key/create)\n",
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" You can specify the key in a constructor parameter `api_key` or in an environment variable `YC_API_KEY`.\n",
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"\n",
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"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",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.embeddings.yandex import YandexGPTEmbeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = YandexGPTEmbeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_result = embeddings.embed_documents([text])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[-0.021392822265625,\n",
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" 0.096435546875,\n",
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" -0.046966552734375,\n",
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" -0.0183258056640625,\n",
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" -0.00555419921875]"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"query_result[:5]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[-0.021392822265625,\n",
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" 0.096435546875,\n",
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" -0.046966552734375,\n",
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" -0.0183258056640625,\n",
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" -0.00555419921875]"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"doc_result[0][:5]"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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167
libs/community/langchain_community/embeddings/yandex.py
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167
libs/community/langchain_community/embeddings/yandex.py
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"""Wrapper around YandexGPT embedding models."""
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from __future__ import annotations
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import logging
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from typing import Any, Callable, Dict, List
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, root_validator
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from langchain_core.utils import get_from_dict_or_env
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from tenacity import (
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before_sleep_log,
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retry,
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retry_if_exception_type,
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stop_after_attempt,
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wait_exponential,
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)
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logger = logging.getLogger(__name__)
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class YandexGPTEmbeddings(BaseModel, Embeddings):
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"""YandexGPT Embeddings models.
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To use, you should have the ``yandexcloud`` python package installed.
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There are two authentication options for the service account
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with the ``ai.languageModels.user`` role:
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- You can specify the token in a constructor parameter `iam_token`
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or in an environment variable `YC_IAM_TOKEN`.
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- You can specify the key in a constructor parameter `api_key`
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or in an environment variable `YC_API_KEY`.
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To use the default model specify the folder ID in a parameter `folder_id`
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or in an environment variable `YC_FOLDER_ID`.
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Or specify the model URI in a constructor parameter `model_uri`
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Example:
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.. code-block:: python
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from langchain_community.embeddings.yandex import YandexGPTEmbeddings
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embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", model_uri="emb://<folder-id>/text-search-query/latest")
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"""
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iam_token: str = ""
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"""Yandex Cloud IAM token for service account
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with the `ai.languageModels.user` role"""
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api_key: str = ""
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"""Yandex Cloud Api Key for service account
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with the `ai.languageModels.user` role"""
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model_uri: str = ""
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"""Model uri to use."""
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folder_id: str = ""
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"""Yandex Cloud folder ID"""
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model_uri: str = ""
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"""Model uri to use."""
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model_name: str = "text-search-query"
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"""Model name to use."""
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model_version: str = "latest"
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"""Model version to use."""
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url: str = "llm.api.cloud.yandex.net:443"
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"""The url of the API."""
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max_retries: int = 6
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"""Maximum number of retries to make when generating."""
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that iam token exists in environment."""
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iam_token = get_from_dict_or_env(values, "iam_token", "YC_IAM_TOKEN", "")
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values["iam_token"] = iam_token
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api_key = get_from_dict_or_env(values, "api_key", "YC_API_KEY", "")
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values["api_key"] = api_key
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folder_id = get_from_dict_or_env(values, "folder_id", "YC_FOLDER_ID", "")
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values["folder_id"] = folder_id
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if api_key == "" and iam_token == "":
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raise ValueError("Either 'YC_API_KEY' or 'YC_IAM_TOKEN' must be provided.")
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if values["iam_token"]:
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values["_grpc_metadata"] = [
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("authorization", f"Bearer {values['iam_token']}")
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]
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if values["folder_id"]:
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values["_grpc_metadata"].append(("x-folder-id", values["folder_id"]))
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else:
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values["_grpc_metadata"] = (
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("authorization", f"Api-Key {values['api_key']}"),
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)
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if values["model_uri"] == "" and values["folder_id"] == "":
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raise ValueError("Either 'model_uri' or 'folder_id' must be provided.")
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if not values["model_uri"]:
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values[
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"model_uri"
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] = f"emb://{values['folder_id']}/{values['model_name']}/{values['model_version']}"
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return values
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed documents using a YandexGPT embeddings models.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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return _embed_with_retry(self, texts=texts)
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using a YandexGPT embeddings models.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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return _embed_with_retry(self, texts=[text])[0]
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def _create_retry_decorator(llm: YandexGPTEmbeddings) -> Callable[[Any], Any]:
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from grpc import RpcError
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min_seconds = 1
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max_seconds = 60
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return retry(
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reraise=True,
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stop=stop_after_attempt(llm.max_retries),
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wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
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retry=(retry_if_exception_type((RpcError))),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def _embed_with_retry(llm: YandexGPTEmbeddings, **kwargs: Any) -> Any:
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"""Use tenacity to retry the embedding call."""
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retry_decorator = _create_retry_decorator(llm)
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@retry_decorator
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def _completion_with_retry(**_kwargs: Any) -> Any:
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return _make_request(llm, **_kwargs)
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return _completion_with_retry(**kwargs)
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def _make_request(self: YandexGPTEmbeddings, texts: List[str]):
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try:
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import grpc
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from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2 import ( # noqa: E501
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TextEmbeddingRequest,
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)
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from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2_grpc import ( # noqa: E501
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EmbeddingsServiceStub,
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)
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except ImportError as e:
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raise ImportError(
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"Please install YandexCloud SDK" " with `pip install yandexcloud`."
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) from e
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result = []
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channel_credentials = grpc.ssl_channel_credentials()
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channel = grpc.secure_channel(self.url, channel_credentials)
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for text in texts:
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request = TextEmbeddingRequest(model_uri=self.model_uri, text=text)
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stub = EmbeddingsServiceStub(channel)
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res = stub.TextEmbedding(request, metadata=self._grpc_metadata)
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result.append(res.embedding)
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return result
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