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diff --git a/cookbook_ru/gigachat_vision/gigachat_vision.ipynb b/cookbook_ru/gigachat_vision/gigachat_vision.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# Работа с GigaChat Vision\n",
+    "В этом ноутбуке мы рассмотрим работу с GigaChat Vision\n",
+    "\n",
+    "Наши действия:\n",
+    "1. Загружаем фото на S3 хранилище GigaChat\n",
+    "2. Просим GigaChat проанализировать фотографии, выделить сущности на них и написать описание к фото\n",
+    "3. Выдать ответ в виде JSON, после чего преобразуем этот JSON в Pydantic модель\n",
+    "\n",
+    "Тестировать будем на этих фото\n",
+    "![фото 1](cat.jpg)\n",
+    "![фото 2](sea.jpg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# !pip install -U gigachain-community"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "is_executing": true
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import getpass\n",
+    "\n",
+    "if \"GIGACHAT_CREDENTIALS\" not in os.environ:\n",
+    "    os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass.getpass(\"Введите credentials gigachat: \")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Пока что Vision модель доступна в качестве preview на урле для новых моделей.\n",
+    "После релиза, можно будет использовать стандартный прод base_url и модель `GigaChat-Pro`"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models.gigachat import GigaChat\n",
+    "\n",
+    "llm = GigaChat(\n",
+    "    verify_ssl_certs=False,\n",
+    "    timeout=6000,\n",
+    "    base_url=\"https://gigachat-preview.devices.sberbank.ru/api/v1\",\n",
+    "    model=\"GigaChat-Pro-preview\",\n",
+    "    temperature=0.01,\n",
+    "    flags=[]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from typing import List, Optional\n",
+    "\n",
+    "from langchain.output_parsers import PydanticOutputParser\n",
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field, validator\n",
+    "from langchain_core.runnables import RunnableLambda, RunnableParallel\n",
+    "\n",
+    "\n",
+    "class Photo(BaseModel):\n",
+    "    \"\"\"Информация о фото\"\"\"\n",
+    "\n",
+    "    content: str = Field(..., description=\"Что изображено на фото? 1-3 слова\")\n",
+    "    description: str = Field(..., description=\"Опиши детальнее фото\")\n",
+    "\n",
+    "\n",
+    "# Set up a parser\n",
+    "parser = PydanticOutputParser(pydantic_object=Photo)\n",
+    "\n",
+    "\n",
+    "def _get_messages_from_url(url: str):\n",
+    "    return {\n",
+    "        \"history\": [\n",
+    "            HumanMessage(content=\"\", additional_kwargs={\"attachments\": [url]}),\n",
+    "        ]\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "# Prompt\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"Определи объект на фото. Ответь на запрос пользователя в формате JSON. Schema Information: \\n{format_instructions}\",\n",
+    "        ),\n",
+    "        MessagesPlaceholder(\"history\"),\n",
+    "    ]\n",
+    ").partial(format_instructions=parser.get_format_instructions())\n",
+    "\n",
+    "chain = RunnableLambda(_get_messages_from_url) | prompt | llm | parser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "В итоге мы создали LCEL цепочку, в которую мы можем передавать id загруженных файлов и в ответ получать Pydantic модели"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "[Photo(content='котенок', description='милый котенок с черно-белой шерстью и большими глазами'),\n Photo(content='пляж', description='Пляж с каменистым берегом и волнами. Вдалеке видны здания и маяк.')]"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Пример работы\n",
+    "file = llm.upload_file(open(\"cat.jpg\", \"rb\"))\n",
+    "file2 = llm.upload_file(open(\"sea.jpg\", \"rb\"))\n",
+    "chain.batch([file.id_, file2.id_])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
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