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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Ноутбук для решения задач из 4 модуля"
      ],
      "metadata": {
        "id": "4uF_KywH0yV9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m163.3/163.3 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m48.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m59.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m823.6/823.6 kB\u001b[0m \u001b[31m64.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.1/14.1 MB\u001b[0m \u001b[31m84.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m731.7/731.7 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.6/410.6 MB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.6/121.6 MB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 MB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.2/124.2 MB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.0/166.0 MB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m76.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install openai langchain tiktoken langchain-openai langchain-community sentence_transformers faiss-cpu pypdf -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from typing import List"
      ],
      "metadata": {
        "id": "hcaodDnGplxx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Если используете ключ от OpenAI, запустите эту ячейку 👇\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "CoofhZcDtp6R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "import os\n",
        "from getpass import getpass\n",
        "\n",
        "\n",
        "# os.environ['OPENAI_API_KEY'] = \"Введите ваш OpenAI API ключ\"\n",
        "os.environ['OPENAI_API_KEY'] = getpass(prompt='Введите ваш OpenAI API ключ')\n",
        "\n",
        "# Инициализируем языковую модель\n",
        "llm = ChatOpenAI(temperature=0.0)"
      ],
      "metadata": {
        "id": "k48wWXnPtKa2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Если используете ключ из курса, запустите эти ячейки 👇\n"
      ],
      "metadata": {
        "id": "DiQqMwQJt8rP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/a-milenkin/LLM_practical_course/main/notebooks/utils.py"
      ],
      "metadata": {
        "id": "Keq0WAjLuPgJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from utils import ChatOpenAI\n",
        "from getpass import getpass\n",
        "\n",
        "#course_api_key= \"Введите ваш API ключ с курса\"\n",
        "course_api_key = getpass(prompt='Введите API ключ')\n",
        "\n",
        "# Инициализируем языковую модель\n",
        "llm = ChatOpenAI(temperature=0.0, course_api_key=course_api_key)"
      ],
      "metadata": {
        "id": "PCS_WedHtPRg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Задание 4.1.5 Кастомный Text Splitter\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "Z9uleJTbu6Ui"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *Напишите функцию*"
      ],
      "metadata": {
        "id": "T5EkAGbIpOlB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_content_from_sections(text: str) -> List[str]:\n",
        "    # your code here\n",
        "    return"
      ],
      "metadata": {
        "id": "92DVpeI4lCW3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Тексты из тестов\n"
      ],
      "metadata": {
        "id": "Ya9qTJNOpsW-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "RqWRGgnN0vY7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "text1 = \"\"\"\n",
        "1. Вступление\n",
        "Описание курса, начальные требования, для кого этот курс.\n",
        "1.1 Как правильно входить в курс\n",
        "Цели курса, канал и комьюнити, глоссарий, PET-проект, хард режим vs лайт режим?\n",
        "1.2 Общий подход и точки улучшения приложений с LLM\n",
        "Рассказываем почему важно разбираться в LLM. Нужен ли ИИ обычному человеку?\n",
        "1.3 API ключ курса или от OpenAI?\n",
        "1.3.1 Ключ от команды курса\n",
        "Получаем ключ в боте.\n",
        "1.3.2 Ключ от OpenAI\n",
        "Получаем официальный ключ от OpenAI.\n",
        "1.3.3 Ключ от HuggingFace\n",
        "Ныряем в Open Source и получаем ключ от HuggingFace.\n",
        "2. Промптинг - объясни LLM, что тебе от неё надо!\n",
        "2.1 Введение в Prompt Engineering\n",
        "Поясняем за промпты. Техники и лайфхаки для промптинга. Из чего состоит промпт?\n",
        "2.2 Дизайн промптов в LangChain\n",
        "Few-shot learning. Output Parser.\n",
        "2.2.1 Введение в LangChain\n",
        "Рассказываем про преимущества LangChain.\n",
        "3. LangChain или причем тут попугаи?\n",
        "3.1 Память в LangChain\n",
        "Переводим LLM в чат-режим. Типы памяти.\n",
        "3.2 Chains - собери свою цепь\n",
        "Chains & LCEL.\n",
        "3.3 Агенты intro\n",
        "Агенты и цепи. Инструменты (tools).\n",
        "\"\"\"\n",
        "\n",
        "text2 = \"\"\"\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "text3 = \"\"\"\n",
        "1. Главный заголовок\n",
        "Тут содержимое, которое нужно достать из главного заголовка.\n",
        "1.1 Подзаголовок\n",
        "Тут содержимое, которое нужно достать из подзаголовка.\n",
        "\"\"\"\n",
        "\n",
        "text4 = \"\"\"\n",
        "1. Первый заголовок\n",
        "\n",
        "2. Второй заголовок\n",
        "\"\"\"\n",
        "\n",
        "text5 = \"\"\"\n",
        "1. Введение\n",
        "Краткое описание целей и задач документа.\n",
        "\n",
        "2. Основные разделы\n",
        "Краткий обзор основных разделов.\n",
        "\n",
        "2.1 Первый подраздел\n",
        "Какие-то мелкие детали.\n",
        "\n",
        "3. Заключение\n",
        "Выводы и заключительные комментарии.\n",
        "4. Приложения\n",
        "Дополнительная информация и материалы.\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "qoWh04ptlBoD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Задание 4.11.1 📚 Литературный RAG из pdf 🤖"
      ],
      "metadata": {
        "id": "P9zYNPu-p9l3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Загружаем файлы"
      ],
      "metadata": {
        "id": "0eRICp2Lq8s-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://stepik.org/media/attachments/lesson/1084288/The_Daughter_of_The_Commandant.pdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWFprDaxqy9Y",
        "outputId": "91f2da22-ba93-4532-a721-e261373b0e9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-04-03 11:15:46--  https://stepik.org/media/attachments/lesson/1084288/The_Daughter_of_The_Commandant.pdf\n",
            "Resolving stepik.org (stepik.org)... 178.248.239.111\n",
            "Connecting to stepik.org (stepik.org)|178.248.239.111|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 198595 (194K) [application/pdf]\n",
            "Saving to: ‘The_Daughter_of_The_Commandant.pdf’\n",
            "\n",
            "The_Daughter_of_The 100%[===================>] 193.94K   457KB/s    in 0.4s    \n",
            "\n",
            "2024-04-03 11:15:47 (457 KB/s) - ‘The_Daughter_of_The_Commandant.pdf’ saved [198595/198595]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "\n",
        "df = pd.read_csv('https://stepik.org/media/attachments/lesson/1084288/pushkin_questions.csv')"
      ],
      "metadata": {
        "id": "dBAX9Ldxq3uC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Соберем все компоненты для RAG"
      ],
      "metadata": {
        "id": "gjrto68msT-A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "<center> <img src='https://github.com/a-milenkin/LLM_practical_course/blob/main/images/RAG.png?raw=1' width=\"800\" height=\"250\">\n"
      ],
      "metadata": {
        "id": "zDg0bWUhr3dz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Определите Document loader для pdf\n",
        "loader =\n",
        "\n",
        "documents = loader.load()"
      ],
      "metadata": {
        "id": "WOwhANxRr3Ik"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Посмотрите, что хранится в переменной documents - может быть сплитер вам не нужен."
      ],
      "metadata": {
        "id": "2CulR5pyxe3k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Определите подходящий Splitter\n",
        "from langchain.text_splitter import (\n",
        "    CharacterTextSplitter,\n",
        "    RecursiveCharacterTextSplitter,\n",
        ")\n",
        "\n",
        "splitter =\n",
        "split_documents = splitter.create_documents()\n",
        "len(split_documents)"
      ],
      "metadata": {
        "id": "ng0MYLpAq3d5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Выбираем Embedding models"
      ],
      "metadata": {
        "id": "IClLxZZytsQ0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔱 Эмбеддинги от OpenAI (API)"
      ],
      "metadata": {
        "id": "QNFHkIi9t4JE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# # Если используете ключ от OpenAI запустите эту ячейку\n",
        "# from langchain_openai import OpenAIEmbeddings\n",
        "\n",
        "# embeddings_api_model = OpenAIEmbeddings()"
      ],
      "metadata": {
        "id": "CDMOJZ-3f0bs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Если используете ключ курса, запустите эту ячейку\n",
        "from utils import OpenAIEmbeddings\n",
        "\n",
        "embeddings_api_model = OpenAIEmbeddings(course_api_key=course_api_key)"
      ],
      "metadata": {
        "id": "a3loV8ySuB7e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🔱 Эмбеддинги от HuggingFace 🤗\n",
        "Переключитесь на среду с GPU, если хотите использовать видеокарту."
      ],
      "metadata": {
        "id": "0tdDE3ihuNDr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "\n",
        "# Если у вас нет видеокарты, укажите 'device': 'cpu'\n",
        "hf_embeddings_model = HuggingFaceEmbeddings(\n",
        "    model_name=\"cointegrated/LaBSE-en-ru\", model_kwargs={\"device\": \"cpu\"}\n",
        ")"
      ],
      "metadata": {
        "id": "ruu1kHTTuPSV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473,
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        },
        "outputId": "c7cc968a-18cd-4f51-d89b-b8660d036b15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/461 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1471d139cf54c7890f74f75d9c0653e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/1.71k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60cb760a5ec8498d880d83fdc21cff62"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/516M [00:00<?, ?B/s]"
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        {
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              "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/521k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/2.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XTMuKyqq1Y7O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Выбираем 🗂 Vector Store"
      ],
      "metadata": {
        "id": "mxBZ5FBMut1j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.vectorstores import FAISS\n",
        "\n",
        "db ="
      ],
      "metadata": {
        "id": "hRKdQT8QuPPT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Определим 🎣 Retriever"
      ],
      "metadata": {
        "id": "bHeBYyV-vPmu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retriever ="
      ],
      "metadata": {
        "id": "pcljBDDBvB2s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🚰 RAG Pipeline - подключаем RAG к LLM"
      ],
      "metadata": {
        "id": "YzCDWTNdvy_J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.schema import StrOutputParser\n",
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "from langchain_core.runnables import RunnablePassthrough\n",
        "\n",
        "# Создаём простой шаблон\n",
        "template = \"\"\"\n",
        "Answer the question based only on the following context:\n",
        "\n",
        "{context}\n",
        "\n",
        "Question: {question}\n",
        "\"\"\"\n",
        "# Создаём промпт из шаблона\n",
        "prompt = ChatPromptTemplate.from_template(template)\n",
        "\n",
        "\n",
        "# Объявляем функцию, которая будет собирать строку из полученных документов\n",
        "def format_docs(docs):\n",
        "    return \"\\n\\n\".join([d.page_content for d in docs])\n",
        "\n",
        "\n",
        "# Создаём цепочку\n",
        "chain =\n"
      ],
      "metadata": {
        "id": "119g8AIqvB0x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "answers = [] # Список, где будем хранить ответы модели\n",
        "\n",
        "for text_input in tqdm(df['question']):\n",
        "    answer = chain.invoke(text_input)\n",
        "    answers.append(answer) # Добавляем ответ в список\n",
        "    break # Для отладки. Уберите, когда убедитесь, что на одном примере работает"
      ],
      "metadata": {
        "id": "z5b0Ii9kvBx2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['answer'] = answers # Создаём новый столбец из ответов модели"
      ],
      "metadata": {
        "id": "ks63JYu1vBpt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('4_1_11_solution.csv', index=False) # Сохраняем файл, отправляем на Stepik, получаем баллы :)"
      ],
      "metadata": {
        "id": "FvJ8XyUQwrUK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xHDAt0_3wrRi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}