РЖД__KIA_ipynb_.ipynb

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      "toc_visible": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Code"
      ],
      "metadata": {
        "id": "QsFGkXMMwcX3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "-nH2KyinoxhE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8be6ecc5-a30c-4fa5-c4d6-a9ea74b51083"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip  install  tiktoken==0.4.0  langchain==0.0.231 openai==0.27.8 faiss-cpu==1.7.4 gspread oauth2client nltk pydantic==1.10.8"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gdown\n",
        "from langchain.llms import OpenAI\n",
        "from langchain.docstore.document import Document\n",
        "import requests\n",
        "#database\n",
        "from langchain.embeddings.openai import OpenAIEmbeddings\n",
        "from langchain.vectorstores import FAISS\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.prompts import PromptTemplate\n",
        "import pathlib\n",
        "import subprocess\n",
        "import tempfile\n",
        "import ipywidgets as widgets\n",
        "import gspread\n",
        "from oauth2client.service_account import ServiceAccountCredentials\n",
        "import re\n",
        "import getpass\n",
        "import os\n",
        "import openai\n",
        "import tiktoken\n"
      ],
      "metadata": {
        "id": "wNTJkb02qexK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTliDteupaSa",
        "outputId": "b2c136ff-b9e9-4834-a8f1-027ccfdcba8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "openai_key = getpass.getpass(\"OpenAI API Key:\")\n",
        "os.environ[\"OPENAI_API_KEY\"] = openai_key\n",
        "openai.api_key = openai_key"
      ],
      "metadata": {
        "id": "jSdHAWANqs7C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6bb130a5-a935-4750-842d-0fb8bc4648bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "OpenAI API Key:··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Work DataBase\n",
        "from datetime import datetime\n",
        "import pytz\n",
        "\n",
        "def create_FAISSdb(source_chunks):\n",
        "  return FAISS.from_documents(source_chunks, OpenAIEmbeddings())\n",
        "\n",
        "def save_FAISSdb(db):\n",
        "  db.save_local(\"/content/drive/My Drive/faiss_vector_db_\" + datetime.now().strftime(\"%m-%d-%Y-%H-%M-%S\")) #База будет сохранена к вам на Google Drive\n",
        "  return True\n",
        "\n",
        "def load_FAISSdb(db_name):\n",
        "  return FAISS.load_local(db_name, OpenAIEmbeddings())\n",
        "\n",
        "def merg_FAISSdb():\n",
        "  return True\n",
        "\n",
        "def delete_FAISSdb():\n",
        "  return True"
      ],
      "metadata": {
        "id": "Ta3L6Cf2pnZr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_TURBO_16K = \"gpt-3.5-turbo-16k\"\n",
        "MODEL_TURBO_0613 = \"gpt-3.5-turbo-0613\"\n",
        "\n",
        "\n",
        "def num_tokens_from_messages(messages, model=MODEL_TURBO_16K):\n",
        "    \"\"\"Returns the number of tokens used by a list of messages.\"\"\"\n",
        "    try:\n",
        "        encoding = tiktoken.encoding_for_model(model)\n",
        "    except KeyError:\n",
        "        encoding = tiktoken.get_encoding(\"cl100k_base\")\n",
        "    if model == \"gpt-3.5-turbo-0301\":  # note: future models may deviate from this\n",
        "        num_tokens = 0\n",
        "        for message in messages:\n",
        "            num_tokens += 4  # every message follows <im_start>{role/name}\\n{content}<im_end>\\n\n",
        "            for key, value in message.items():\n",
        "                num_tokens += len(encoding.encode(value))\n",
        "                if key == \"name\":  # if there's a name, the role is omitted\n",
        "                    num_tokens += -1  # role is always required and always 1 token\n",
        "        num_tokens += 2  # every reply is primed with <im_start>assistant\n",
        "        return num_tokens\n",
        "    else:\n",
        "        raise NotImplementedError(f\"\"\"num_tokens_from_messages() is not presently implemented for model {model}.\"\"\")\n",
        "\n",
        "def insert_newlines(textstr: str, max_len: int = 90) -> str:\n",
        "    words = textstr.split()\n",
        "    lines = []\n",
        "    current_line = \"\"\n",
        "    for word in words:\n",
        "        if len(current_line + \" \" + word) > max_len:\n",
        "            lines.append(current_line)\n",
        "            current_line = \"\"\n",
        "        current_line += \" \" + word\n",
        "    lines.append(current_line)\n",
        "    return \"\\n\".join(lines)\n",
        "\n",
        "def answer_index(system, topic, search_index, temp = 0.3, verbose = 0, top_similar_documents = 5):\n",
        "\n",
        "    #Выборка документов по схожести с вопросом\n",
        "    docs = search_index.similarity_search(topic, k=top_similar_documents)\n",
        "    if (verbose): print('\\n ===========================================: ')\n",
        "    message_content = re.sub(r'\\n{2}', ' ', '\\n '.join([f'\\nОтрывок документа №{i+1}\\n=====================' + doc.page_content + '\\n' for i, doc in enumerate(docs)]))\n",
        "    if (verbose): print('message_content :\\n ======================================== \\n', message_content)\n",
        "\n",
        "    messages = [\n",
        "        {\"role\": \"system\", \"content\": system},\n",
        "        {\"role\": \"user\", \"content\": f\"Документ с информацией для ответа пользователю: {message_content}\\n\\nВопрос клиента: \\n{topic}\"}\n",
        "    ]\n",
        "\n",
        "    #print(messages)\n",
        "\n",
        "    # example token count from the function defined above\n",
        "    if (verbose): print('\\n ===========================================: ')\n",
        "    if (verbose): print(f\"{num_tokens_from_messages(messages, 'gpt-3.5-turbo-0301')} токенов использовано на вопрос\")\n",
        "\n",
        "    completion = openai.ChatCompletion.create(\n",
        "    model=MODEL_TURBO_16K,\n",
        "    messages=messages,\n",
        "    temperature=temp\n",
        "    )\n",
        "    answer = insert_newlines(completion.choices[0].message.content)\n",
        "    return answer  # возвращает ответ"
      ],
      "metadata": {
        "id": "eAafR4VwqtFS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# функция для загрузки документа по ссылке из гугл драйв\n",
        "def load_document_text(url: str) -> str:\n",
        "    # Extract the document ID from the URL\n",
        "    match_ = re.search('/document/d/([a-zA-Z0-9-_]+)', url)\n",
        "    if match_ is None:\n",
        "        raise ValueError('Invalid Google Docs URL')\n",
        "    doc_id = match_.group(1)\n",
        "\n",
        "    # Download the document as plain text\n",
        "    response = requests.get(f'https://docs.google.com/document/d/{doc_id}/export?format=txt')\n",
        "    response.raise_for_status()\n",
        "    text = response.text\n",
        "\n",
        "    return text"
      ],
      "metadata": {
        "id": "1YFsrIhi4Yct"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.text_splitter import MarkdownHeaderTextSplitter\n",
        "\n",
        "def load_search_indexes(url: str) -> str:\n",
        "    # Extract the document ID from the URL\n",
        "    match_ = re.search('/document/d/([a-zA-Z0-9-_]+)', url)\n",
        "    if match_ is None:\n",
        "        raise ValueError('Invalid Google Docs URL')\n",
        "    doc_id = match_.group(1)\n",
        "\n",
        "    # Download the document as plain text\n",
        "    response = requests.get(f'https://docs.google.com/document/d/{doc_id}/export?format=txt')\n",
        "    response.raise_for_status()\n",
        "    #text = response.content.decode('unicode_escape')\n",
        "    text = response.text\n",
        "    headers_to_split_on = [\n",
        "        (\"#\", \"Header 1\")\n",
        "    ]\n",
        "\n",
        "    markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on)\n",
        "    md_header_splits = markdown_splitter.split_text(text)\n",
        "\n",
        "    vectordateBase = FAISS.from_documents(md_header_splits, OpenAIEmbeddings())\n",
        "   # save_FAISSdb(vectordateBase)\n",
        "    return vectordateBase\n"
      ],
      "metadata": {
        "id": "hu-w7euIqtNF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "db = load_search_indexes('https://docs.google.com/document/d/1JnU-h2ma1yK1VJSuIef_4Xww2sZqLcM2tDDcsLSgyog')\n",
        "\n",
        "#db = load_FAISSdb('/content/drive/My Drive/faiss_vector_db_08-30-2023-13-19-16')"
      ],
      "metadata": {
        "id": "9CrXhbfVqtP_"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def answer_user_question_dialog(system: str, db: str, user_question: str, question_history: list) -> str:\n",
        "\n",
        "    # Если в истории более одного вопроса, применяем суммаризацию\n",
        "   # summarized_history = \"\"\n",
        "   # if len(question_history) > 0:\n",
        "    #    summarized_history = \"Вот краткий обзор предыдущего диалога: \" + summarize_questions([q + ' ' + (a if a is not None else '') for q, a in question_history])\n",
        "\n",
        "    # Добавляем явное разделение между историей диалога и текущим вопросом\n",
        "   # input_text =summarized_history + \"\\n\\nТекущий ответ пользователя: \" + user_question\n",
        "\n",
        "    # Извлечение наиболее похожих отрезков текста из базы знаний и получение ответа модели\n",
        "   # answer_text = answer_index(system, input_text, db, temperature, verbose)\n",
        "    answer_text = answer_index(system, user_question, db, temperature, verbose)\n",
        "\n",
        "    # Добавляем вопрос пользователя и ответ системы в историю\n",
        "   # question_history.append((\"\\n Пользователь: \" + user_question, \"\\n Консультант: \" + answer_text if answer_text is not None else ''))\n",
        "\n",
        "    # Выводим суммаризированный текст, который видит модель\n",
        "    #if summarized_history != \"\":\n",
        "    #if 1 == 0 :\n",
        "     #   print('****************************')\n",
        "      #  print(insert_newlines(summarized_history))\n",
        "       # print('****************************')\n",
        "\n",
        "    return insert_newlines(answer_text)"
      ],
      "metadata": {
        "id": "dsEKLyifvEto"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def run_dialog(system_doc_url, knowledge_base_url):\n",
        "    question_history = []\n",
        "    dialog = \"\"\n",
        "    while True:\n",
        "        user_question = input('Машинист ВЛ80С: ')\n",
        "        if ((user_question.lower() == 'stop') or (user_question.lower() == 'стоп')):\n",
        "            break\n",
        "        answer = answer_user_question_dialog(system_doc_url, knowledge_base_url, user_question, question_history)\n",
        "        dialog += f'\\nМашинист: {user_question} \\n Варвара: {answer}'\n",
        "        print('\\nВарвара: ', answer+'\\n')\n",
        "\n",
        "    return"
      ],
      "metadata": {
        "id": "g8N4WuC7qtKO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Промпт моделей машин\n",
        "#promt_url = load_document_text ('https://docs.google.com/document/d/1tjjJQ0s0qKSdmg2Yd69NXX4_RGWLr0up')\n",
        "promt_url = \"You are an assistant to a locomotive engineer. You have a troubleshooting guide with probable causes and methods of resolution. When given a malfunction in the text, you need to suggest the most suitable method to fix the issue.\""
      ],
      "metadata": {
        "id": "YCBakZx6qtVM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Run"
      ],
      "metadata": {
        "id": "Pe2RdzYtwvOF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "y3SIRj6dUAHp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "db = load_search_indexes('https://docs.google.com/document/d/1JnU-h2ma1yK1VJSuIef_4Xww2sZqLcM2tDDcsLSgyog')"
      ],
      "metadata": {
        "id": "IhewyuK21KGf"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "promt_url = \"You're the best train engineer's assistant. You know all malfunctions. When an engineer describes a problem, your job is to suggest potential causes. Provide concise, clear answers.\"\n",
        "#promt_url = \"You are an assistant to a locomotive engineer. You have a troubleshooting guide with probable causes and methods of resolution. When given a malfunction in the text, you need to suggest the most suitable method to fix the issue.\""
      ],
      "metadata": {
        "id": "aGNyJlVx2qoW"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "run_dialog(promt_url, db)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "90RAsJV5w3mE",
        "outputId": "d7547515-9943-46a4-b416-d2e4f93325db"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Машинист ВЛ80С: Сработало реле 113\n",
            "\n",
            "Варвара:   Вероятная причина: Сработало реле 113 из-за короткого замыкания или перегрузки в цепи\n",
            " вспомогательных машин.\n",
            "\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-62-24536ff3ab34>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_dialog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpromt_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-37-14e1633039ac>\u001b[0m in \u001b[0;36mrun_dialog\u001b[0;34m(system_doc_url, knowledge_base_url)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mdialog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0muser_question\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Машинист ВЛ80С: '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_question\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'stop'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0muser_question\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'стоп'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    849\u001b[0m                 \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m             )\n\u001b[0;32m--> 851\u001b[0;31m         return self._input_request(str(prompt),\n\u001b[0m\u001b[1;32m    852\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
          ]
        }
      ]
    }
  ]
}