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{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"id": "d024645c",
"metadata": {},
"outputs": [],
"source": [
"__import__('pysqlite3')\n",
"import sys\n",
"import os\n",
"sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')\n",
"os.environ['ALLOW_RESET'] = 'True'\n",
"\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"import time\n",
"\n",
"import chromadb\n",
"from gigachat import GigaChat\n",
"\n",
"client = chromadb.PersistentClient(path='db')\n",
"collection = client.get_collection(name=\"administrative_codex\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "17dae6a5",
"metadata": {},
"outputs": [],
"source": [
"docs = collection.get()['documents']\n",
"prompt = 'Задание: напиши в виде нумерованного списка 3 конкретных независимых друг от друга вопроса, ответ на которые можно найти в приведенном тексте. Не упоминай федеральные законы. Не упоминай КоАП.'"
]
},
{
"cell_type": "markdown",
"id": "91549726-3c7a-44ef-8519-c1afc3adde0f",
"metadata": {},
"source": [
"### Генерируем вопросы к каждому фрагменту текста"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "06f82948",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████| 2130/2130 [54:44<00:00, 1.54s/it]\n"
]
}
],
"source": [
"for doc in tqdm(docs[1:]):\n",
" question_ready = False\n",
" \n",
" while not question_ready:\n",
" try:\n",
" text = f'{doc}\\n\\n{prompt}'\n",
" \n",
" with GigaChat(credentials='N2ZiNDIxZTgtM2Y4Yy00MGJjLWI4OTgtN2M5NGM5MTYzZTNiOmFmYjJmZTUwLTc1OWItNGQ5MC1iMGVmLTMwYTNlODU3YzVmZg==', scope='GIGACHAT_API_PERS', verify_ssl_certs=False) as giga:\n",
" questions = giga.chat(text).choices[0].message.content\n",
" \n",
" question_ready = True\n",
" except:\n",
" time.sleep(5)\n",
"\n",
" df = pd.read_csv('generated_questions.csv')\n",
" new_df = pd.DataFrame({'text': [doc], 'questions': [questions]})\n",
" pd.concat([df, new_df], ignore_index=True).to_csv('generated_questions.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "2f44eac2-7ce0-4d26-9f4a-41f5bfe0fa44",
"metadata": {},
"outputs": [],
"source": [
"generated_questions_df = pd.read_csv('generated_questions.csv')\n",
"docs = generated_questions_df['text'].tolist()\n",
"generated_questions = generated_questions_df['questions'].tolist()\n",
"\n",
"prompt = 'В России. Дай подробный ответ текстом, похожим на закон, не пиши ничего лишнего.'"
]
},
{
"cell_type": "markdown",
"id": "90b543f8-0b94-4c0c-8a69-9574b7c54db9",
"metadata": {},
"source": [
"### Генерируем ответы к вопросам, в которых есть слово штраф"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "a7937078-6200-44ba-b43f-4867e947b750",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|███████████████████████████████████████████████████████████████████████████████| 1978/1978 [50:30<00:00, 1.53s/it]\n"
]
}
],
"source": [
"for doc, g_questions in zip(tqdm(docs[153:]), generated_questions[153:]):\n",
" llm_answer_ready = False\n",
" fine_question = ''\n",
"\n",
" for question in g_questions.split('\\n'):\n",
" question = ' '.join(question.split()[1:])\n",
" \n",
" if 'штраф' in question:\n",
" fine_question = question\n",
" break\n",
"\n",
" if not fine_question:\n",
" continue\n",
" \n",
" while not llm_answer_ready:\n",
" try:\n",
" text = f'Помоги, пожалуйста. {fine_question} {prompt}'\n",
" \n",
" with GigaChat(credentials='MmU3OTdhNmItMTQzYy00NGQzLWEyYTctZjcxOWJmYThiMWE5OmE1ZDdhNDkxLWI5ZTEtNGFkZS04N2JjLTExZjE5MTYwNGQ5Yg==', scope='GIGACHAT_API_PERS', verify_ssl_certs=False) as giga:\n",
" llm_answer = giga.chat(text).choices[0].message.content.split('\\n')[0]\n",
" \n",
" llm_answer_ready = True\n",
" except:\n",
" time.sleep(5)\n",
"\n",
" \n",
" if len(llm_answer) > 100:\n",
" df = pd.read_csv('generated_additional_llm_answer.csv')\n",
" new_df = pd.DataFrame({'text': [doc], 'question': [fine_question], 'llm_answer': [llm_answer]})\n",
" pd.concat([df, new_df], ignore_index=True).to_csv('generated_additional_llm_answer.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5fd64855-01b5-4c66-a425-b6d91b355a22",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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