QA_system.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U7njnkkFKdaI"
   },
   "source": [
    "# Импорт библиотек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 14268,
     "status": "ok",
     "timestamp": 1717148940881,
     "user": {
      "displayName": "Quill",
      "userId": "02979416796145646991"
     },
     "user_tz": -180
    },
    "id": "QE5b7-hqLxCi"
   },
   "outputs": [],
   "source": [
    "from langchain_chroma import Chroma\n",
    "from langchain_community.embeddings import HuggingFaceEmbeddings\n",
    "import chromadb\n",
    "import torch\n",
    "import os\n",
    "from google.colab import drive\n",
    "from transformers import AutoModelForQuestionAnswering\n",
    "from transformers import AutoTokenizer\n",
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1717148940881,
     "user": {
      "displayName": "Quill",
      "userId": "02979416796145646991"
     },
     "user_tz": -180
    },
    "id": "GsSqLoWmOq8M",
    "outputId": "201c2338-c4c2-4370-88ff-469d5c374d7c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(DEVICE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GKsUH4WvLlcE"
   },
   "source": [
    "## Подключение Google Drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 32615,
     "status": "ok",
     "timestamp": 1717148824719,
     "user": {
      "displayName": "Quill",
      "userId": "02979416796145646991"
     },
     "user_tz": -180
    },
    "id": "Q4Enl5dRCYpr",
    "outputId": "525d32db-403f-4132-8df1-2137bf9efb1f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TJhmJeuzKiV3"
   },
   "source": [
    "# Подготовка хранилища данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1717148940882,
     "user": {
      "displayName": "Quill",
      "userId": "02979416796145646991"
     },
     "user_tz": -180
    },
    "id": "LE9yF2uNOnNm"
   },
   "outputs": [],
   "source": [
    "# Удобная функция для получения модели эмбеддингов\n",
    "def get_embeddings(model_name):\n",
    "    model_kwargs = {'device': DEVICE}\n",
    "    return HuggingFaceEmbeddings(\n",
    "        model_name=model_name,\n",
    "        model_kwargs=model_kwargs\n",
    "    )"
   ]
  },
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    "executionInfo": {
     "elapsed": 16755,
     "status": "ok",
     "timestamp": 1717148957619,
     "user": {
      "displayName": "Quill",
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    },
    "id": "tj9SWo4dKhEk",
    "outputId": "ddac9fb2-a9bd-4f78-ffc4-f3e5649d027d"
   },
   "outputs": [
    {
     "data": {
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       "version_major": 2,
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      "text/plain": [
       "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
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     "output_type": "display_data"
    },
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      },
      "text/plain": [
       "config_sentence_transformers.json:   0%|          | 0.00/118 [00:00<?, ?B/s]"
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     },
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     "output_type": "display_data"
    },
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      "text/plain": [
       "README.md:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
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     },
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    },
    {
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      "text/plain": [
       "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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    {
     "data": {
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      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/471 [00:00<?, ?B/s]"
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    },
    {
     "data": {
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      "text/plain": [
       "sentencepiece.bpe.model:   0%|          | 0.00/1.27M [00:00<?, ?B/s]"
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    {
     "data": {
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      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/4.23M [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "c3b838f63ba945baad32696cd7d79b7e",
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       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/279 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d686fafc6d4347febaf26494e6411944",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Загрузка БД, созданной в chroma_preparing.ipynb\n",
    "db = Chroma(persist_directory='/content/drive/MyDrive/M_QA/chroma/war_and_peace',\n",
    "            collection_name='war_and_peace',\n",
    "            collection_metadata={'hnsw:space': 'cosine'},\n",
    "            embedding_function=get_embeddings('d0rj/e5-base-en-ru'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T3DE1Oi1KljJ"
   },
   "source": [
    "# Подготовка модели для ответов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 20919,
     "status": "ok",
     "timestamp": 1717148990739,
     "user": {
      "displayName": "Quill",
      "userId": "02979416796145646991"
     },
     "user_tz": -180
    },
    "id": "N3i3fJ3LKZuY"
   },
   "outputs": [],
   "source": [
    "model_name = '/content/drive/MyDrive/M_QA/ruBERT_QA_1_epoch'\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model = AutoModelForQuestionAnswering.from_pretrained(model_name).to(DEVICE)\n",
    "qa_pipeline = pipeline('question-answering', model=model, tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 552,
     "status": "ok",
     "timestamp": 1717148991274,
     "user": {
      "displayName": "Quill",
      "userId": "02979416796145646991"
     },
     "user_tz": -180
    },
    "id": "dcRWcqKMI4es",
    "outputId": "c33497ea-8584-4fd2-dd03-2db4d3844d00"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'score': 0.16905491054058075,\n",
       "  'start': 0,\n",
       "  'end': 48,\n",
       "  'answer': 'Я был рожден не каким-то там человеком, а ящером'},\n",
       " {'score': 0.4450800120830536,\n",
       "  'start': 67,\n",
       "  'end': 80,\n",
       "  'answer': 'захватить мир'}]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Пример ответов модели ruBERT-base\n",
    "qa_pipeline(question=['Кто я?', 'Что я планирую?'],\n",
    "            context=['Я был рожден не каким-то там человеком, а ящером, и теперь я готов захватить мир']*2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8DWYPvACKn-0"
   },
   "source": [
    "# Объединенная система"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1717148991274,
     "user": {
      "displayName": "Quill",
      "userId": "02979416796145646991"
     },
     "user_tz": -180
    },
    "id": "yKluz77Yrp1r"
   },
   "outputs": [],
   "source": [
    "# Проверить, является ли объект итеративным\n",
    "def is_iterable(obj):\n",
    "    try:\n",
    "        iter(obj)\n",
    "        return not isinstance(obj, (str, dict))\n",
    "    except TypeError:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1717148991274,
     "user": {
      "displayName": "Quill",
      "userId": "02979416796145646991"
     },
     "user_tz": -180
    },
    "id": "yX4YPr8x8Xul"
   },
   "outputs": [],
   "source": [
    "# Извлечь название книги из его пути\n",
    "def extract_book_name(path):\n",
    "    base_name = os.path.basename(path)\n",
    "    return os.path.splitext(base_name)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1717148991274,
     "user": {
      "displayName": "Quill",
      "userId": "02979416796145646991"
     },
     "user_tz": -180
    },
    "id": "AiqyFAWsLvW1"
   },
   "outputs": [],
   "source": [
    "# Функция для адекватного вывода текстов фрагментов\n",
    "def wrap_text(text, width=100, indent=''):\n",
    "    text = text.replace('\\n', ' ').replace('\\r', '')\n",
    "    lines = [indent + text[i:i+width] for i in range(0, len(text), width)]\n",
    "    return '\\n'.join(lines)"
   ]
  },
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   "execution_count": 20,
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   "outputs": [],
   "source": [
    "# Главная функция объединенной QA-системы\n",
    "def qa_system(questions, k_relevant=3, return_type=0):\n",
    "    \"\"\"\n",
    "    Главная функция для взаимодействия с QA-системой\n",
    "\n",
    "    PARAMETERS\n",
    "    ----------\n",
    "    questions : str or list[str]\n",
    "        вопрос или список вопросов к системе\n",
    "    \n",
    "    k_relevent : int\n",
    "        количество релевантных фрагментов к каждому\n",
    "        вопросу\n",
    "    \n",
    "    return_type : 0 or 1 or 2\n",
    "        формат вывода ответов:\n",
    "        - 0: вернуть только список ответов;\n",
    "        - 1: вернуть кортеж из списка ответов, списка\n",
    "             соответствующих источников и списка абсолютных\n",
    "             позиций ответов в этих источниках;\n",
    "        - 2: вернуть список из кортежей (контекст, источник),\n",
    "             где в контексте выделен ответ, а в источнике\n",
    "             указан и документ, и позиция ответа в нем\n",
    "    \"\"\"\n",
    "    # Преобразование вопроса в список вопросов\n",
    "    if not is_iterable(questions):\n",
    "        questions = [questions]\n",
    "\n",
    "    # Поиск релевантных отрывков для каждого вопроса\n",
    "    contexts_data = [db.similarity_search_with_score('query: ' + q,\n",
    "                                                     k=k_relevant)\n",
    "                     for q in questions]\n",
    "\n",
    "    # Обрезка префикса \"passage: \" у контекстов\n",
    "    for contexts_with_score in contexts_data:\n",
    "        for context, _ in contexts_with_score:\n",
    "            context.page_content = context.page_content[9:]\n",
    "\n",
    "    # Список из собранных контекстов для каждого вопроса\n",
    "    contexts_texts = ['\\n'.join(context.page_content#[9:]\n",
    "                               for context, score in k_contexts)\n",
    "                     for k_contexts in contexts_data]\n",
    "\n",
    "    # Получение ответов с помощью модели\n",
    "    answers_data = qa_pipeline(question=questions,\n",
    "                               context=contexts_texts)\n",
    "\n",
    "    # Преобразование ответов в список\n",
    "    if not is_iterable(answers_data):\n",
    "        answers_data = [answers_data]\n",
    "\n",
    "    # Извлечение только текстов ответа\n",
    "    answers_texts = [answer['answer'] for answer in answers_data]\n",
    "\n",
    "    # Вернуть тексты ответов\n",
    "    if return_type == 0:\n",
    "        return answers_texts\n",
    "\n",
    "    # Цвета для выделения ответа в контексте\n",
    "    selection_start = '\\033[96m'\n",
    "    selection_end = '\\033[0m'\n",
    "\n",
    "    context_ids = []\n",
    "    sources = []\n",
    "    rel_source_starts = []\n",
    "    abs_source_starts = []\n",
    "\n",
    "    # Определение позиции ответов в контексте\n",
    "    for i, (context_data, answer_data) in enumerate(zip(contexts_data,\n",
    "                                                   answers_data)):\n",
    "        cumulative_starts = 0\n",
    "        for j, (context_doc, score) in enumerate(context_data):\n",
    "            if answer_data['answer'] in context_doc.page_content:\n",
    "                context_ids.append(j)\n",
    "                sources.append(extract_book_name(context_doc.metadata['source']))\n",
    "                rel_source_starts.append(answer_data['start'] - cumulative_starts)\n",
    "                abs_source_starts.append(rel_source_starts[-1] + context_doc.metadata['start_index'])\n",
    "                break\n",
    "            cumulative_starts += len(contexts_data[i][j][0].page_content) + 1\n",
    "\n",
    "    if return_type == 1:\n",
    "        return answers_texts, sources, abs_source_starts\n",
    "    else:\n",
    "        selected_answers = []\n",
    "        for i in range(len(questions)):\n",
    "            # Обрезание префикса \"passage: \" у контекста\n",
    "            context = contexts_data[i][context_ids[i]][0].page_content\n",
    "            start_pos = rel_source_starts[i]\n",
    "            end_pos = start_pos + len(answers_data[i]['answer'])\n",
    "            selected_answers.append((''.join([context[:start_pos],\n",
    "                                              selection_start,\n",
    "                                              context[start_pos:end_pos],\n",
    "                                              selection_end,\n",
    "                                              context[end_pos:]]),\n",
    "                                     f'(C) {sources[i]}, символ {abs_source_starts[i]}'))\n",
    "\n",
    "        return selected_answers"
   ]
  },
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   "source": [
    "# Вывести ответ системы, если использовался return_type=2\n",
    "def print_qa_result(result, width=100):\n",
    "    text, source = result\n",
    "    text = text.replace('\\r', '')\n",
    "    print(wrap_text(text, width=width))\n",
    "    print(f'{source:>{width}}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Примеры работы системы"
   ]
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     "text": [
      "Вопрос: Когда было получено известие о потере Москвы?\n",
      "В контексте:\n",
      "о Бородинском сражении, о наших потерях убитыми и ранеными, а еще более страшное известие о потере Москвы были получены в Воронеже \u001b[96mв половине сентября\u001b[0m. Княжна Марья, узнав только из газет о ране брата и не имея о нем никаких определенных сведений, собралась ехать отыскивать князя Андрея, как слышал Николай (сам же он не видал ее). Получив известие о Бородинском сражении и об оставлении Москвы, Ростов не то чтобы испытывал отчаяние, злобу или месть и тому подобные чувства, но ему вдруг все стало\n",
      "                                                               (C) Война и мир. Том 4, символ 47994\n",
      "\n",
      "Вопрос: Когда было Бородинское сражение?\n",
      "В контексте:\n",
      "пущено ни одного выстрела ни с той, ни с другой стороны, \u001b[96m26-го\u001b[0m произошло Бородинское сражение. Для чего и как были даны и приняты сражения при Шевардине и при Бородине? Для чего было дано Бородинское сражение? Ни для французов, ни для русских оно не имело ни малейшего смысла. Результатом ближайшим было и должно было быть - для русских то, что мы приблизились к погибели Москвы (чего мы боялись больше всего в мире), а для французов то, что они приблизились к погибели всей армии (чего они тоже\n",
      "                                                              (C) Война и мир. Том 3, символ 376016\n",
      "\n",
      "Вопрос: Где служил Николай Ростов?\n",
      "В контексте:\n",
      "и безупречной праздности состояла и будет состоять главная привлекательность военной службы. Николай Ростов испытывал вполне это блаженство, после 1807 года продолжая служить \u001b[96mв Павлоградском полку\u001b[0m, в котором он уже командовал эскадроном, принятым от Денисова. Ростов сделался загрубелым, добрым малым, которого московские знакомые нашли бы несколько mauvais genre [  дурного тона]  , но который был любим и уважаем товарищами, подчиненными и начальством и который был доволен своей жизнью. В\n",
      "                                                              (C) Война и мир. Том 2, символ 479044\n",
      "\n",
      "Вопрос: В каком городе была главная квартира Кутузова?\n",
      "В контексте:\n",
      "жителей, располагались у крепости Браунау. \u001b[96mВ Браунау\u001b[0m была главная квартира главнокомандующего Кутузова. 11-го октября 1805 года один из только-что пришедших к Браунау пехотных полков, ожидая смотра главнокомандующего, стоял в полумиле от города.\n",
      "                                                              (C) Война и мир. Том 1, символ 267222\n",
      "\n",
      "Вопрос: Когда русские войска располагались у крепости Бранау?\n",
      "В контексте:\n",
      "и хорошо! - сказал он, сердито посмотрев на бесчувственную маленькую княгиню, укоризненно покачал головою и захлопнул дверь. ЧАСТЬ BTОРАЯI\u001b[96mВ октябре 1805 года\u001b[0m русские войска занимали села и города эрцгерцогства Австрийского, и еще новые полки приходили из России и, отягощая постоем жителей, располагались у крепости Браунау. В Браунау была главная квартира главнокомандующего Кутузова. 11-го октября 1805 года один из только-что пришедших к Браунау пехотных полков, ожидая смотра\n",
      "                                                              (C) Война и мир. Том 1, символ 267035\n",
      "\n",
      "Вопрос: Где смертельно ранили Андрея Болконского?\n",
      "В контексте:\n",
      "месте, \u001b[96mгде он упал с древком знамени в руках\u001b[0m, лежал князь Андрей Болконский, истекая кровью, и, сам не зная того, стонал тихим, жалостным и детским стоном. К вечеру он перестал стонать и совершенно затих. Он не знал, как долго продолжалось его забытье. Вдруг он опять чувствовал себя живым и страдающим от жгучей и разрывающей что-то боли в голове. \"Где оно, это высокое небо, которое я не знал до сих пор и увидал нынче?\" было первою его мыслью. \"И страдания этого я не знал также, - подумал он. -\n",
      "                                                              (C) Война и мир. Том 1, символ 707755\n",
      "\n",
      "CPU times: user 1.58 s, sys: 6.32 ms, total: 1.59 s\n",
      "Wall time: 1.66 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "questions = [\n",
    "    'Когда было получено известие о потере Москвы?',\n",
    "    'В каком году было Бородинское сражение?',\n",
    "    'Когда было Бородинское сражение?',\n",
    "    'Где служил Николай Ростов?',\n",
    "    'В каком городе была главная квартира Кутузова?',\n",
    "    'Когда русские войска располагались у крепости Бранау?',\n",
    "    'Где смертельно ранили Андрея Болконского?'\n",
    "]\n",
    "\n",
    "qa_results = qa_system(questions, k_relevant=3, return_type=3)\n",
    "for question, result in zip(questions, qa_results):\n",
    "    print('Вопрос:', question)\n",
    "    print('В контексте:')\n",
    "    print_qa_result(result)\n",
    "    print()"
   ]
  }
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