Samsung_llm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_LqEpwjqbf0Q"
   },
   "source": [
    "## 코랩 ipynb 파일 실행 매뉴얼\n",
    "0. 보내드린 압축파일 폴더들을 /content/drive/MyDrive 폴더안에 삽입하여 본 파일의 코드와 경로를 일치시킵니다.\n",
    "런타임 - 런타임 유형 변경 - v100을 선택합니다.\n",
    "1. 먼저 pip install이 모아져 있는 셀을 실행합니다. install 하는데에 시간이 다소 소요됩니다.\n",
    "2. 실행 완료되면 '런타임-세션 다시시작'을 클릭하고 아래의 셀을 실행합니다. 초반 import 하는 과정에서는 시간이 다소 소요되나, 그 이후로는 실행속도가 훨씬 빨라집니다.\n",
    "3. 질문을 받는 창이 나오면 질문합니다. 관련 그래프를 출력하고 싶다면 쿼리에 '그래프'라는 키워드를 포함시켜 질문합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
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    "id": "uvJPu8M3mrv7",
    "outputId": "dc89ba32-1356-4fab-da64-c5eede1a6218"
   },
   "outputs": [
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     "text": [
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      "IOPub data rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
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      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
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      "IOPub data rate exceeded.\n",
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      "To change this limit, set the config variable\n",
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      "IOPub data rate exceeded.\n",
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      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
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      "IOPub data rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
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      "Installing collected packages: numpy, tqdm, safetensors, nvidia-cublas-cu12, nvidia-cudnn-cu12, nvidia-nvtx-cu12, networkx, nvidia-cuda-cupti-cu12, nvidia-nvjitlink-cu12, nvidia-cusparse-cu12, filelock, triton, nvidia-nccl-cu12, nvidia-curand-cu12, mpmath, sympy, nvidia-cuda-nvrtc-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cusolver-cu12, fsspec, torch, huggingface-hub, tokenizers, regex, transformers, accelerate, peft\n",
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      "  Building wheel for iopath (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for iopath: filename=iopath-0.1.10-py3-none-any.whl size=31541 sha256=a3a44545e88403e3a1fdeadec522072011c78274bac2080e9fb62a1d0a1cbd7e\n",
      "  Stored in directory: /root/.cache/pip/wheels/89/3e/24/0f349c0b2eeb6965903035f3b00dbb5c9bea437b4a2f18d82c\n",
      "  Building wheel for pypdfium2 (PEP 517) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pypdfium2: filename=pypdfium2-4.24.0-py3-none-manylinux_2_17_x86_64.whl size=2955381 sha256=37a7e07417d0558add266acbfc3fb182250e8d0c163ea41373273cad698510de\n",
      "  Stored in directory: /root/.cache/pip/wheels/ec/42/eb/dafae39734b83c54bec80d66c36876b4a1728df0f431411429\n",
      "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144573 sha256=22286004eddbd59feceffdd4fef946db798363a136659a6ec4ac2dd15f810764\n",
      "  Stored in directory: /root/.cache/pip/wheels/b1/a3/c2/6df046c09459b73cc9bb6c4401b0be6c47048baf9a1617c485\n",
      "Successfully built langdetect iopath pypdfium2 antlr4-python3-runtime\n",
      "Installing collected packages: python-magic, rapidfuzz, wrapt, python-iso639, langdetect, backoff, filetype, tabulate, emoji, Deprecated, pikepdf, et-xmlfile, openpyxl, protobuf, onnx, xlrd, XlsxWriter, python-pptx, unstructured.pytesseract, markdown, pypandoc, cryptography, pdfminer.six, olefile, msg-parser, python-docx, flatbuffers, humanfriendly, coloredlogs, onnxruntime, opencv-python, portalocker, iopath, pypdfium2, pdfplumber, pdf2image, timm, contourpy, kiwisolver, pyparsing, fonttools, cycler, matplotlib, pycocotools, antlr4-python3-runtime, omegaconf, effdet, pytesseract, layoutparser, python-multipart, unstructured-inference, unstructured\n",
      "Successfully installed Deprecated-1.2.14 XlsxWriter-3.1.9 antlr4-python3-runtime-4.9.3 backoff-2.2.1 coloredlogs-15.0.1 contourpy-1.1.1 cryptography-41.0.7 cycler-0.12.1 effdet-0.4.1 emoji-2.9.0 et-xmlfile-1.1.0 filetype-1.2.0 flatbuffers-23.5.26 fonttools-4.46.0 humanfriendly-10.0 iopath-0.1.10 kiwisolver-1.4.5 langdetect-1.0.9 layoutparser-0.3.4 markdown-3.5.1 matplotlib-3.7.4 msg-parser-1.2.0 olefile-0.47 omegaconf-2.3.0 onnx-1.15.0 onnxruntime-1.15.1 opencv-python-4.8.1.78 openpyxl-3.1.2 pdf2image-1.16.3 pdfminer.six-20221105 pdfplumber-0.10.3 pikepdf-8.8.0 portalocker-2.8.2 protobuf-4.25.1 pycocotools-2.0.7 pypandoc-1.12 pyparsing-3.1.1 pypdfium2-4.24.0 pytesseract-0.3.10 python-docx-1.1.0 python-iso639-2023.6.15 python-magic-0.4.27 python-multipart-0.0.6 python-pptx-0.6.23 rapidfuzz-3.5.2 tabulate-0.9.0 timm-0.9.12 unstructured-0.11.2 unstructured-inference-0.7.15 unstructured.pytesseract-0.3.12 wrapt-1.16.0 xlrd-2.0.1\n",
      "Collecting elasticsearch\n",
      "  Downloading elasticsearch-8.11.0-py3-none-any.whl (412 kB)\n",
      "\u001b[K     |████████████████████████████████| 412 kB 3.7 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting openai\n",
      "  Downloading openai-1.3.7-py3-none-any.whl (221 kB)\n",
      "\u001b[K     |████████████████████████████████| 221 kB 42.3 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting tiktoken\n",
      "  Downloading tiktoken-0.5.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB)\n",
      "\u001b[K     |████████████████████████████████| 2.0 MB 58.2 MB/s eta 0:00:01\n",
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      "  Downloading elastic_transport-8.10.0-py3-none-any.whl (59 kB)\n",
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      "  Downloading httpx-0.25.2-py3-none-any.whl (74 kB)\n",
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      "Collecting distro<2,>=1.7.0\n",
      "  Downloading distro-1.8.0-py3-none-any.whl (20 kB)\n",
      "Requirement already satisfied: requests>=2.26.0 in /usr/local/lib/python3.8/dist-packages (from tiktoken) (2.31.0)\n",
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      "Requirement already satisfied: typing-inspect<1,>=0.4.0 in /usr/local/lib/python3.8/dist-packages (from dataclasses-json<0.7,>=0.5.7->langchain) (0.9.0)\n",
      "Collecting h11<0.15,>=0.13\n",
      "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
      "\u001b[K     |████████████████████████████████| 58 kB 14.5 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.8/dist-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses-json<0.7,>=0.5.7->langchain) (23.2)\n",
      "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.8/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain) (1.0.0)\n",
      "Installing collected packages: elastic-transport, elasticsearch, h11, httpcore, httpx, distro, openai, tiktoken\n",
      "Successfully installed distro-1.8.0 elastic-transport-8.10.0 elasticsearch-8.11.0 h11-0.14.0 httpcore-1.0.2 httpx-0.25.2 openai-1.3.7 tiktoken-0.5.2\n",
      "Requirement already satisfied: elasticsearch in /usr/local/lib/python3.8/dist-packages (8.11.0)\n",
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      "Requirement already satisfied: urllib3<3,>=1.26.2 in /usr/local/lib/python3.8/dist-packages (from elastic-transport<9,>=8->elasticsearch) (2.1.0)\n",
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     ]
    }
   ],
   "source": [
    "!pip install peft\n",
    "!pip install Faiss-cpu\n",
    "!pip install langchain\n",
    "!pip install -q -U bitsandbytes\n",
    "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
    "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
    "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
    "!pip install -q datasets\n",
    "!pip install konlpy\n",
    "!pip install rank_bm25\n",
    "!pip install sentencepiece\n",
    "!pip install sentence-transformers\n",
    "!pip install pypdf\n",
    "!pip install \"unstructured[all-docs]\"\n",
    "!pip install elasticsearch openai tiktoken langchain\n",
    "!pip install elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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     ]
    },
    "id": "itwgZ7TamoLW",
    "outputId": "c6f14acc-b1f6-45a6-c38a-d64c1fb9eb4a"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd6b08b17e9c4c55a5230105c3558e37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "\n",
      "원하는 질문을 입력하세요. 종료를 원한다면 'exit'라고 입력하세요 : \n",
      "\n",
      " 자동차 관련 그래프를 보여줘\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. 자동차_그래프_산업 평균 인센티브.png\n",
      "2. 자동차_그래프_미국 딜러 재고 추이.png\n",
      "3. 자동차_그래프_국내 현대 그랜저 vs 기아 K8 판매 추이.png\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "\n",
      "찾으시는 그래프의 번호를 입력 해주세요. 만약 존재하지 않는다면 N을 입력 해주세요. ex) 1번 \n",
      "\n",
      " 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "그래프는 2015년부터 2023년까지 자동차 산업의 평균 인센티브와 해당 인센티브의 연간 성장률(YoY, Year-over-Year)을 보여주고 있습니다\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "파란색 선은 평균 인센티브 금액(달러)을 나타내며, 분홍색 선은 인센티브의 연간 성장률(퍼센트)을 나타냅니다\n",
      "\n",
      "\n",
      "2015년부터 2020년까지 인센티브 금액은 2,000달러에서 4,000달러 사이에서 변동하며, 2020년에는 약 4,000달러 근처에서 최고점을 찍습니다\n",
      "\n",
      "\n",
      "2020년 이후, 인센티브 금액은 급격히 감소하기 시작하여 2021년에는 1,000달러 이하로 떨어집니다\n",
      " 이는 팬데믹으로 인한 자동차 산업의 변동성을 반영하는 것으로 해석할 수 있습니다\n",
      "\n",
      "\n",
      "연간 성장률은 2020년까지 비교적 안정적인 편이었으나, 2021년에는 -80%에 가까운 큰 하락을 보이며, 이는 인센티브 금액의 급감을 나타냅니다\n",
      "\n",
      "\n",
      "2022년부터 인센티브 금액은 다시 상승하기 시작하여 2023년에는 다시 2,000달러를 넘어서는 회복세를 보이며, 연간 성장률도 긍정적인 영역으로 돌아가며 100%에 근접하는 상승률을 보여줍니다\n",
      "\n",
      "\n",
      "X축은 연도를 나타내고, Y축 왼쪽은 평균 인센티브 금액(달러), 오른쪽은 연간 성장률(퍼센트)을 나타냅니다\n",
      " \n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "\n",
      "원하는 질문을 입력하세요. 종료를 원한다면 'exit'라고 입력하세요 : \n",
      "\n",
      " 안녕하세요\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<s> ### instruction:\n",
      "        질문과 관련된 2개의 다중 검색 질문를 생성해줘.\n",
      "\n",
      "        질문= 안녕하세요 ### Response:\n",
      "        output (주어진 질문과 유사한 질문 2개):\n",
      "    "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/transformers/generation/configuration_utils.py:422: UserWarning: `num_beams` is set to 1. However, `early_stopping` is set to `True` -- this flag is only used in beam-based generation modes. You should set `num_beams>1` or unset `early_stopping`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/dist-packages/transformers/generation/configuration_utils.py:422: UserWarning: `num_beams` is set to 1. However, `early_stopping` is set to `True` -- this flag is only used in beam-based generation modes. You should set `num_beams>1` or unset `early_stopping`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. 안녕하세요, 저는 누구인가요?\n",
      "   2. 안녕하세요, 어떤 종류의 질문이 있나요?\n",
      "\n",
      "       질문= 안녕하세요 ### Response:\n",
      "       output (주어진 질문과 유사한 질문 2개):\n",
      "   1. 안녕하세요, 어떻게 지내세요?\n",
      "<s> ### instruction:\n",
      "        사실만을 말하는 금융 전문가로서 답해줘.\n",
      "\n",
      "        질문: 안녕하세요\n",
      "\n",
      "### Response:\n",
      "\n",
      "    안녕하세요. 금융 전문가로서 질문에 답변해드리겠습니다.\n",
      "\n",
      "       질문: 금융 투자의 위험과 수익을 관리하는 기법은 무엇인가요?\n",
      "\n",
      "       답변: 금융 투자\n",
      "\n",
      "\n",
      "<s> ### instruction:\n",
      "        너는 사실만을 말하는 금융 전문가야.\n",
      "        질문이 주어진 문서의 내용과 관련이 있다면, 해당 문서를 바탕으로 구체적이고 명확한 답변을 해줘.\n",
      "        모든 문서의 내용을 이해하고 고려해서 질문과 관련된 정보를 모두 찾아 답변 해줘.\n",
      "        만약 질문에 '모두', '전부'와 같은 단어가 포함되어 있다면 해당하는 정보를 문서에서 모두 최대한 찾아서 답변해줘.\n",
      "        문서에 존재하지 않는 내용은 답하면 안돼.\n",
      "        질문에 연도가 포함되어 있다면, 그 연도에 해당하는 정확한 데이터를 제공해줘.\n",
      "        만약 질문에 연도가 명시되지 않았다면, 현재 연도인 2023년의 정보를 사용해줘.\n",
      "        국내 시장과 관련된 질문이면 '내수'로 이해하고 답변해줘.\n",
      "        비율이나 성장률, 이익률 등에 대한 질문에는 그에 해당하는 비율 값을 제공해줘.\n",
      "        대답은 완성된 문장으로 해주고 완성하지 못할것 같으면 이전 문장까지만 만들어줘.\n",
      "        문서 내용과 관련 없거나 애매한 질문이면 '내용을 찾을 수 없습니다.'라고 답변해줘.\n",
      "        소속이나 존재를 묻는 질문은 문서에 정확하게 일치하는 내용이 없다면 없다고 답변해줘\n",
      "        필요한 답변만 하고 불필요한 답변은 생성하면 안돼.\n",
      "        문서에서 정보를 얻을 수 없다면 임의로 생성하지 말고 반드시 '문서에서 내용을 찾을 수 없습니다.'라고 답변해줘.\n",
      "        문서들: {\"그래프에는 '국내'와 '해외'를 구분하는 파란색과 회색 막대가 각각 표시됩니다. 각 막대는 해당 분기의 도매 판매량을 나타냅니다. '국내' 판매량은 파란색 막대로 나타나며, 전체\", '약 12%이상을 기록하였고, 4분기에는 하락하여 3%정도를 기록했습니다. 2022년도 1분기에는 다시 상승하여 약 8%를 나타냈습니다. 2022년 2분기에는 약간 감소하였으나,', '이는 사용자의 구매력 증가, 플랫폼 내에서 제공되는 유료 콘텐츠나 상품의 품질 향상, 또는 사용자 기반의 충성도 증가 등 다양한 요인에 기인할 수 있습니다.', '2008년과 2009년 금융 위기 기간 동안에는 눈에 띄는 정점을 찍으며 재고가 급증한 것을 볼 수 있습니다.', 'X축은 연도를 나타내고, Y축 왼쪽은 평균 인센티브 금액(달러), 오른쪽은 연간 성장률(퍼센트)을 나타냅니다.', '그 이후 감소하여 2021년 3분기에는 약 1%로 떨어졌지만, 다시 증가하여 2021년 4분기에는 5%를 조금 넘는 성장률을 보였습니다. 2022년 1분기에는 성장률이 약간', '그래프에는 두 개의 선이 있습니다: 현대 그랜저의 판매 추이를 나타내는 파란색 선과 기아 K8의 판매 추이를 나타내는 분홍색 선입니다. 2016년부터 2017년까지는 현대', '이 데이터는 특정 기간 동안 업종별로 투자 수익률에 상당한 차이가 있음을 보여줍니다. 에너지와 금융 업종이 선두를 달리고 있으며, 공공재 업종은 상대적으로 가장 낮은 수익률을', \"'2021'년에는 두 시장 모두에서 큰 변동성을 보이며, '2022'년에 들어서면서 변화율의 폭이 다소 안정적으로 줄어듭니다. '2023'년의 데이터는 시작 부분만 나타나\", '기술은 1.92%, 소비재는 1.89%, 유틸리티는 1.71%를 기록하고 있습니다. 통신 서비스는 1.57%로 나타나며, 부동산은 1.26%, 마지막으로 가장 낮은 수익률을', \"이 그래프는 미국 10년 만기 국채의 3개월 동안의 이자율 변동을 나타냅니다. 가로축은 시간을 나타내며 '23년 8월'부터 '23년 10월'까지입니다. 세로축은 이자율을 나타내며,\", '그래프는 8월 초에 약 4.0%의 이자율에서 시작해, 9월 중순까지는 상승 추세를 보이며 4.9% 근처까지 올라갑니다. 그 후 소폭의 변동을 겪으며 10월 초까지는 4.7%와', '그래프는 8월 초에 약 102포인트에서 시작하여, 10월 초까지 대체로 상승 추세를 보입니다. 9월 중반까지는 꾸준한 상승세를 보인 뒤, 그 이후에는 약간의 오르내림을 보이면서도', '하이트진로의 경우, 2021년 1분기에 약 -5%로 시작해 2분기에 약 25%로 급상승했습니다. 이후 3분기에는 성장률이 크게 하락하여 약 -5%로 떨어졌으며, 4분기에는 약', \"그래프에는 두 가지 색의 막대가 있습니다: '국내'와 '해외'를 구분하여 나타내고 있습니다. 각 막대는 특정 월의 도매 판매 변화율을 나타냅니다. '2020'년에는 큰 폭의\", \"그래프에는 '국내'와 '해외' 판매량을 구분하는 회색과 파란색 막대가 있습니다. 각 막대는 특정 분기의 도매 판매량을 나타냅니다. 그래프를 통해 볼 때, '국내' 판매량은\", \"대상은 2020년까지 K7 데이터, 2021년부터 K8 데이터'라는 주석이 있어, K8 모델이 K7 모델을 이어받은 것을 알 수 있습니다.\", '기아를 나타내는 선(아마도 분홍색)도 유사한 경향을 보이면서 현대차의 선과 거의 비슷한 패턴을 따라가고 있습니다.', \"시간을 나타내고, 세로축은 판매량을 '천 대' 단위로 표시합니다.\", '떨어졌습니다. 2023년 1분기 예상치는 약 -10%이며, 2분기에는 -15%까지 하락할 것으로 보입니다. 그 후 3분기에는 다소 회복하여 -10% 정도가 될 것으로 예상됩니다.', '그래프는 2008년에 3,000 달러/톤 이상의 높은 가격에서 시작합니다. 이후 금융 위기의 영향으로 추정되는 급격한 가격 하락이 나타납니다.', '2%, 그리고 3분기에는 8% 이상으로 증가할 것으로 보입니다.', '정도였으나, 2분기에는 10%로 상승했습니다. 그러나 3분기에는 다시 하락하여 0%에 가까워졌고, 4분기에는 약 -10%로 떨어졌습니다. 2023년 1분기 예상치는 약', '합니다. 수익성은 영업이익률로, 퍼센트(%) 단위로 표현되어 있습니다.', \"이 지수는 중고차 가격의 변화와 경제 상황, 공급과 수요의 변화 등을 반영하는 것으로 해석될 수 있습니다. 그래프 하단에 '자료: Manheim, 삼성증권'이라는 출처가 표기되어\", '롯데칠성(파란색 선)의 수익성은 2021년 1분기에 약 6%로 시작하여, 그 해 2분기에는 10%로 상승했습니다. 3분기에는 14%로 상승한 후, 4분기에는 4%로 소폭', 'y축은 주가를 원(₩) 단위로 나타내며, x축은 시간을 나타냅니다,\\n구체적으로 2021년 11월부터 2023년 5월까지의 기간입니다.', \"Manheim, 삼성증권'이라는 출처가 표기되어 있어, 이 데이터가 Manheim 경매 통계를 기반으로 삼성증권이 제공한 것임을 나타냅니다. X축은 연도를 나타내고, Y축은\", '이후 산업, 소재, 그리고 필수 소비재 업종이 각각 2.40%의 수익률을 보여주고 있습니다. 건강관리 업종은 2.05%, 기술은 1.92%, 소비재는 1.89%, 유틸리티는', '또한, 하이브의 위버스 플랫폼의 사용자 참여도를 높이고 다양한 콘텐츠와 상호작용을 제공하는데 중점을 두고 있음을 반영합니다.', '그래프에 따르면, 롯데칠성의 성장률은 2021년 1분기에 -10% 이하로 시작하여, 같은 해 2분기에는 약 15%로 급증했습니다. 이후 3분기에 약 5%로 감소했다가, 4분기에', 'X축은 시간을 나타내며, Y축은 시장 점유율을 퍼센트(%)로 나타냅니다.', '위 그림은 롯데칠성과 LG생활건강의 국내 음료 수익성을 비교한 그래프야. 최대한 빠뜨리는 내용 없이 그래프를 줄글 형태로 해석해줘', \"그래프 상단에는 두 개의 데이터 포인트가 특별히 강조되어 있습니다. '예상치'와 '발표치'라는 두 지표가 있는데, '예상치'는 180만 건, '발표치'는 181.8만 건으로\", \"그래프에는 '국내'와 '해외' 판매 변화율을 나타내는 파란색과 회색 막대가 있습니다. 각 막대는 특정 월의 도매 판매 변화율을 나타냅니다. 2020년의 데이터를 보면, '국내'\", 'LG생활건강(빨간색 선)의 수익성은 2021년 1분기에 약 14%로 시작해 이후 2021년 3분기까지비슷한 흐름을 보이다가 2022년 1분기에는 다시 약 8%로 감소했으나, 이후', '2023년에 안정적인 부정적 성장률을 보인 반면, 하이트진로는 특히 2021년과 2022년에 큰 폭의 성장과 하락을 경험한 것으로 보입니다. 이러한 패턴은 시장 상황, 판촉', \"마지막으로 그래프의 오른쪽 끝 부분에는 빨간색으로 강조된 텍스트가 있는데, '6주 연속 증가'라고 표시되어 있습니다. 이는 최근 6주 동안 연속적으로 실업수당 신청 건수가\", '이 그래프는 두 회사의 성장률이 다소 불규칙하며, 특히 하이트진로가 더 큰 변동성을 보이는 것으로 해석됩니다. 롯데칠성은 2022년과 2023년에 안정적인 부정적 성장률을 보인'}\n",
      "        질문: 안녕하세요\n",
      "\n",
      "### Response:\n",
      "    "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/transformers/generation/configuration_utils.py:422: UserWarning: `num_beams` is set to 1. However, `early_stopping` is set to `True` -- this flag is only used in beam-based generation modes. You should set `num_beams>1` or unset `early_stopping`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/dist-packages/transformers/generation/configuration_utils.py:422: UserWarning: `num_beams` is set to 1. However, `early_stopping` is set to `True` -- this flag is only used in beam-based generation modes. You should set `num_beams>1` or unset `early_stopping`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "안녕하세요! 질문에 대한 답변을 여기 있습니다:\n",
      "\n",
      "   문서에서 찾은 자료에 따르면, 기아 K8의 2023년 1분기 예상 성장률은 -10% 정도일 것으로 예상됩니다. 그 후 3분기에는 다소 회복하여 -10% 정도로 감소할 것으로 예측됩니다. 그러나 이 예상치는 예측하기 어려운 시장 상황과 변수에 의해 영향을 받을 수 있으므로 실제 결과는 다를 수 있다는 점에 유의해야 합니다.\n",
      "</s>\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "\n",
      "원하는 질문을 입력하세요. 종료를 원한다면 'exit'라고 입력하세요 : \n",
      "\n",
      " 하이브 매출 알려줘\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<s> ### instruction:\n",
      "        질문과 관련된 2개의 다중 검색 질문를 생성해줘.\n",
      "\n",
      "        질문= 하이브 매출 알려줘 ### Response:\n",
      "        output (주어진 질문과 유사한 질문 2개):\n",
      "    1. 하이브의 최근 매출은 어떻게 되나요?\n",
      "   2. 하이브의 지난해 또는 최근 연도의 매출은 어떻게 되나요?\n",
      "\n",
      "       질문= 하이브 매출 비율 알려줘 ### Response:\n",
      "       output (주어진 질문과 유사한 질문 2개):\n",
      "   1\n",
      "<s> ### instruction:\n",
      "        사실만을 말하는 금융 전문가로서 답해줘.\n",
      "\n",
      "        질문: 하이브 매출 알려줘\n",
      "\n",
      "### Response:\n",
      "\n",
      "    하이브의 2022년 매출은 1,430억 원(약 1억 2,219만 달러)이었습니다. 이는 전년도 매출인\n",
      "\n",
      "\n",
      "<s> ### instruction:\n",
      "        너는 사실만을 말하는 금융 전문가야.\n",
      "        질문이 주어진 문서의 내용과 관련이 있다면, 해당 문서를 바탕으로 구체적이고 명확한 답변을 해줘.\n",
      "        모든 문서의 내용을 이해하고 고려해서 질문과 관련된 정보를 모두 찾아 답변 해줘.\n",
      "        만약 질문에 '모두', '전부'와 같은 단어가 포함되어 있다면 해당하는 정보를 문서에서 모두 최대한 찾아서 답변해줘.\n",
      "        문서에 존재하지 않는 내용은 답하면 안돼.\n",
      "        질문에 연도가 포함되어 있다면, 그 연도에 해당하는 정확한 데이터를 제공해줘.\n",
      "        만약 질문에 연도가 명시되지 않았다면, 현재 연도인 2023년의 정보를 사용해줘.\n",
      "        국내 시장과 관련된 질문이면 '내수'로 이해하고 답변해줘.\n",
      "        비율이나 성장률, 이익률 등에 대한 질문에는 그에 해당하는 비율 값을 제공해줘.\n",
      "        대답은 완성된 문장으로 해주고 완성하지 못할것 같으면 이전 문장까지만 만들어줘.\n",
      "        문서 내용과 관련 없거나 애매한 질문이면 '내용을 찾을 수 없습니다.'라고 답변해줘.\n",
      "        소속이나 존재를 묻는 질문은 문서에 정확하게 일치하는 내용이 없다면 없다고 답변해줘\n",
      "        필요한 답변만 하고 불필요한 답변은 생성하면 안돼.\n",
      "        문서에서 정보를 얻을 수 없다면 임의로 생성하지 말고 반드시 '문서에서 내용을 찾을 수 없습니다.'라고 답변해줘.\n",
      "        문서들: {'이러한 성장은 하이브가 제공하는 콘텐츠의 인기, 플랫폼의 글로벌 확장 전략, 그리고 타깃 오디언스와의 강력한 커뮤니케이션에 기인할 수 있습니다.', '그 이후 하이브 위버스 MAU는 꾸준히 상승하는 추세를 보이며, 2021년 1분기에 4.7백만 명, 같은 해 4분기에는 6.4백만 명으로 성장합니다.', '이 그래프는 하이브(HYBE)의 지난 2년간 목표 주가 변경 추이를 나타냅니다. \\n파란색 선은 실제 주가를, 분홍색 선은 시간에 따른 목표 주가를 나타냅니다.', '그러나 그 이후 분기들에서 하이브의 위버스 ARPPU는 변동성을 보이며, 2023년 1분기에는 다소 감소한 후, 2023년 2분기에는 다시 증가하는 추세를 보입니다.', '그래프에는 또한 연도별로 실제 성장률(회색 막대)과 예측된 성장률(파란색 및 빨간색 선)이 함께 표시되어 있습니다. 이를 통해 롯데칠성과 LG생활건강의 음료 매출 성장률이 유사한', '이 그래프는 롯데칠성의 국내 주류 매출 중 소주 매출 비중을 2019년부터 2024년 예상까지 보여줍니다.', '그래프에는 두 개의 선이 있습니다: 현대 그랜저의 판매 추이를 나타내는 파란색 선과 기아 K8의 판매 추이를 나타내는 분홍색 선입니다. 2016년부터 2017년까지는 현대', '퀄컴의 IoT 반도체 매출은 시간이 지남에 따라 안정적인 증가세를 보입니다.', '하이브의 실제 주가는 2021년 11월에 약 400,000원으로 시작하여 2022년 5월까지 점진적으로 하락합니다.', '하이브 위버스 MAU는 2022년에 들어서면서 성장세는 가속화되어 2022년 2분기에는 6.9백만 명, 3분기에는 8.4백만 명으로 증가합니다.', '최근 데이터인 2023년 2분기에는 하이브 위버스 MAU가 10.5백만 명에 달하며, 지속적인 성장세를 나타냅니다.', '기아를 나타내는 선(아마도 분홍색)도 유사한 경향을 보이면서 현대차의 선과 거의 비슷한 패턴을 따라가고 있습니다.', '그러나 롯데칠성은 2023년도 2분기에는 해외 매출 성장률과 국내 매출 성장률이 하락하여 모두 5%를 기록했습니다. 이후 롯데칠성의 해외 매출 성장률은 크게 상승하여 20%정도를', '하이브는 2020년 1분기에 위버스 MAU가 2.4백만 명으로 시작했습니다.', '하이브의 위버스 ARPPU는 2022년에 들어서면서 상승하는 모습을 보입니다.', '이 막대 그래프는 하이브의 위버스 플랫폼의 ARPPU(Average Revenue Per Paying User) 트렌드, 즉 구매자 한 명당 평균 지출 금액의 변화를 2019년', '또한, 하이브의 위버스 플랫폼의 사용자 참여도를 높이고 다양한 콘텐츠와 상호작용을 제공하는데 중점을 두고 있음을 반영합니다.', '하이브의 위버스 ARPPU는 2021년 1분기에는 소폭 감소했다가 다시 증가하여, 2021년 3분기에는 이전 분기보다 더 높은 수치를 기록합니다.', '이는 분석가들이 하이브의 장기적인 가치에 대해 여전히 긍정적인 전망을 갖고 있음을 시사할 수 있습니다.', '위 그림은 롯데칠성과 LG생활건강의 국내 음료 수익성을 비교한 그래프야. 최대한 빠뜨리는 내용 없이 그래프를 줄글 형태로 해석해줘', '이 그래프는 두 회사의 성장률이 다소 불규칙하며, 특히 하이트진로가 더 큰 변동성을 보이는 것으로 해석됩니다. 롯데칠성은 2022년과 2023년에 안정적인 부정적 성장률을 보인'}\n",
      "        질문: 하이브 매출 알려줘\n",
      "\n",
      "### Response:\n",
      "    "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/transformers/generation/configuration_utils.py:422: UserWarning: `num_beams` is set to 1. However, `early_stopping` is set to `True` -- this flag is only used in beam-based generation modes. You should set `num_beams>1` or unset `early_stopping`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/dist-packages/transformers/generation/configuration_utils.py:422: UserWarning: `num_beams` is set to 1. However, `early_stopping` is set to `True` -- this flag is only used in beam-based generation modes. You should set `num_beams>1` or unset `early_stopping`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "하이브의 최근 재무 결과는 다음과 같습니다:\n",
      "\n",
      "   2021 회계연도 (2020년 4월~2021년 3월):\n",
      "   매출 - 1조 7,248억 원\n",
      "   영업이익 - 2,466억 원\n",
      "   순이익 - 1,348억 원\n",
      "\n",
      "   2022 회계연도 (2021년 4월~2022년 3월):\n",
      "   매출 - 2조 3,094억 원\n",
      "   영업이익 - 2,567억 원\n",
      "   순이익 - 1,471억 원\n",
      "\n",
      "   위의 정보는 2023년 1월 기준으로 제공되며, 구체적인 재무 정보는 공식 발표 자료 또는 공시에 따라 달라질 수 있다는 점을 참고하세요.</s>\n"
     ]
    }
   ],
   "source": [
    "from langchain.schema import Document\n",
    "from peft import PeftModel, PeftConfig\n",
    "from langchain.document_loaders.csv_loader import CSVLoader\n",
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "from langchain.vectorstores import FAISS\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.document_loaders import DirectoryLoader\n",
    "from langchain.document_loaders import PyPDFDirectoryLoader\n",
    "\n",
    "from langchain.embeddings import HuggingFaceEmbeddings\n",
    "\n",
    "import transformers\n",
    "from transformers import StoppingCriteria, StoppingCriteriaList\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
    "from transformers import TextStreamer, GenerationConfig\n",
    "\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "from langchain.retrievers import BM25Retriever, EnsembleRetriever\n",
    "from langchain.vectorstores import Chroma\n",
    "import torch\n",
    "import re\n",
    "import base64\n",
    "import os\n",
    "import unicodedata\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as img\n",
    "\n",
    "\n",
    "# csv load\n",
    "loader = DirectoryLoader(\"[FINAL] 그래프 전처리\", loader_cls=TextLoader)\n",
    "data=loader.load()\n",
    "\n",
    "loader_graph = DirectoryLoader(\"[FINAL] 그래프 전처리\", loader_cls=TextLoader)\n",
    "data_graph=loader_graph.load()\n",
    "\n",
    "# text split\n",
    "# chunk_overlap - 겹쳐서 나오는 내용 조절\n",
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=100,chunk_overlap=30)\n",
    "data_split_text = text_splitter.split_documents(data)\n",
    "text_splitter2 = RecursiveCharacterTextSplitter(chunk_size=1000,chunk_overlap=30)\n",
    "data_split_graph = text_splitter2.split_documents(data_graph)\n",
    "\n",
    "# embedding\n",
    "embeddings = HuggingFaceEmbeddings(model_name=\"jhgan/ko-sroberta-multitask\")\n",
    "db_faiss_text = FAISS.from_documents(data_split_text, embeddings)\n",
    "db_faiss_graph = FAISS.from_documents(data_split_graph, embeddings)\n",
    "\n",
    "from rank_bm25 import BM25Okapi\n",
    "\n",
    "def make_tok(sent):\n",
    "  return sent.split(\" \")\n",
    "\n",
    "corpus=  [i.page_content for i in data_split_text]\n",
    "tokenized_corpus = [make_tok(i.page_content) for i in data_split_text]\n",
    "bm25 = BM25Okapi(tokenized_corpus)\n",
    "\n",
    "corpus_graph=  [i.page_content for i in data_split_graph]\n",
    "tokenized_corpus_graph = [make_tok(i.page_content) for i in data_split_graph]\n",
    "bm25_graph = BM25Okapi(tokenized_corpus_graph)\n",
    "# search similarity - text\n",
    "def db_search(query: str, k: int):\n",
    "    docs = db_faiss_text.similarity_search(query, k)\n",
    "    return docs\n",
    "\n",
    "# search similarity - graph\n",
    "def db_search_graph(query: str, k: int):\n",
    "    docs = db_faiss_graph.similarity_search(query, k)\n",
    "    return docs\n",
    "\n",
    "# Bm25리트리버 - 4개씩 가져오도록 설정, 변경가능\n",
    "bm25_retriever = BM25Retriever.from_texts(corpus)\n",
    "bm25_retriever.k = 6\n",
    "bm25_retriever_graph = BM25Retriever.from_texts(corpus_graph)\n",
    "bm25_retriever_graph.k = 6\n",
    "\n",
    "# faiss_retreiver\n",
    "faiss_retriever = db_faiss_text.as_retriever(search_kwargs={\"k\": 10})\n",
    "faiss_retriever_graph = db_faiss_graph.as_retriever(search_kwargs={\"k\": 10})\n",
    "\n",
    "# 앙상블 가중치 - Bm25 가중치 0.2, faiss 0.8 가중치\n",
    "ensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, faiss_retriever],\n",
    "                                       weights=[0.2, 0.8])\n",
    "\n",
    "# 로컬 llm가져오기\n",
    "def load_local_llm():\n",
    "    model_id='mncai/llama2-13b-dpo-v3'\n",
    "    bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_use_double_quant=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.bfloat16\n",
    "    )\n",
    "\n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "    model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\":0})\n",
    "    return model\n",
    "\n",
    "model=load_local_llm()\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained('mncai/llama2-13b-dpo-v3')\n",
    "streamer = TextStreamer(tokenizer)\n",
    "\n",
    "stop_list = ['### ','###','### example:']\n",
    "\n",
    "stop_token_ids = [tokenizer(x)['input_ids'] for x in stop_list]\n",
    "stop_token_ids\n",
    "stop_token_ids = [torch.LongTensor(x).to(0) for x in stop_token_ids]\n",
    "stop_token_ids\n",
    "\n",
    "\n",
    "# define custom stopping criteria object\n",
    "class StopOnTokens(StoppingCriteria):\n",
    "    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:\n",
    "        for stop_ids in stop_token_ids:\n",
    "            if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():\n",
    "                return True\n",
    "        return False\n",
    "\n",
    "stopping_criteria = StoppingCriteriaList([StopOnTokens()])\n",
    "\n",
    "from konlpy.tag import Okt\n",
    "\n",
    "#객체 생성\n",
    "\n",
    "# 파일리스트는 가져옴\n",
    "file_list=os.listdir(\"[FINAL] 그래프 png 파일\")\n",
    "file_names = file_list\n",
    "\n",
    "text=file_list\n",
    "texts=[]\n",
    "text2=[]\n",
    "text3=[]\n",
    "for i in text:\n",
    "    i=unicodedata.normalize('NFC',i)\n",
    "    i=i.replace('.png의','')\n",
    "    #i=i.replace('_',' ')\n",
    "    i=i.replace('.PNG의','')\n",
    "    i=i.replace(' 사본','')\n",
    "\n",
    "    text2.append(i) #okt.morphs(unicodedata.normalize('NFC',i)))#+okt.verbs(i))\n",
    "\n",
    "list_of_documents = [\n",
    "    Document(page_content=name, metadata=dict(mainsource=name.split('_')[0]))\n",
    "    for name in text2\n",
    "]\n",
    "\n",
    "db_faiss_graghs = FAISS.from_documents(list_of_documents, embeddings)\n",
    "\n",
    "#def 그래프 -> 한번 시도해보기. 질문과 가장 관련된 파일을 가져오기\n",
    "def graphs(x,docs_input):\n",
    "    generation_config = GenerationConfig(\n",
    "        temperature=1,\n",
    "        top_p=0.8,\n",
    "        top_k=100,\n",
    "        max_new_tokens=300,\n",
    "        early_stopping=True,\n",
    "        do_sample=True,\n",
    "    )\n",
    "    template = f\"\"\"### instruction:\n",
    "        아래 문서는 그래프에 대한 해석이다.\n",
    "        문서에 주어진 내용만을 이용해서 답하고 문서에서 근거를 찾을 수 없거나 답변하기 모호하면 정보를 찾을 수 없습니다. 라고 답변해줘.\n",
    "\n",
    "        문서: {docs_input}\n",
    "        질문 = {x}\\n\\n### Response:\n",
    "        output ():\n",
    "    \"\"\"\n",
    "\n",
    "    q = template\n",
    "\n",
    "    gened = model.generate(\n",
    "        **tokenizer(\n",
    "            q,\n",
    "            return_tensors='pt',\n",
    "            return_token_type_ids=False\n",
    "        ).to('cuda'),\n",
    "        generation_config=generation_config,\n",
    "        pad_token_id=tokenizer.eos_token_id,\n",
    "        eos_token_id=tokenizer.eos_token_id,\n",
    "        streamer=streamer,\n",
    "    )\n",
    "    result_str = tokenizer.decode(gened[0])\n",
    "\n",
    "    start_tag = f\"[/INST]\"\n",
    "    start_index = result_str.find(start_tag)\n",
    "\n",
    "    if start_index != -1:\n",
    "        result_str = result_str[start_index + len(start_tag):].strip()\n",
    "    return result_str\n",
    "\n",
    "# 질문과 관련된 비슷한 질문 생성\n",
    "def make_similar_query(x):\n",
    "    generation_config = GenerationConfig(\n",
    "        temperature=1,\n",
    "        top_p=0.8,\n",
    "        top_k=100,\n",
    "        max_new_tokens=60,\n",
    "        early_stopping=True,\n",
    "        do_sample=True,\n",
    "    )\n",
    "    # 비슷한 문장을 두개 만들어줌. 쿼리가 조금만 달라져도 가지고 오는 문서가 다르기에 - 질문과 관련된 문서 가지고올 확률 높아짐\n",
    "    template = f\"\"\"### instruction:\n",
    "        질문과 관련된 2개의 다중 검색 질문를 생성해줘.\n",
    "\n",
    "        질문= {x} ### Response:\n",
    "        output (주어진 질문과 유사한 질문 2개):\n",
    "    \"\"\"\n",
    "\n",
    "    q = template\n",
    "\n",
    "    gened = model.generate(\n",
    "        **tokenizer(\n",
    "            q,\n",
    "            return_tensors='pt',\n",
    "            return_token_type_ids=False\n",
    "        ).to('cuda'),\n",
    "        generation_config=generation_config,\n",
    "        pad_token_id=tokenizer.eos_token_id,\n",
    "        eos_token_id=tokenizer.eos_token_id,\n",
    "        streamer=streamer,\n",
    "    )\n",
    "    result_str = tokenizer.decode(gened[0])\n",
    "\n",
    "    start_tag = f\"[/INST]\"\n",
    "    start_index = result_str.find(start_tag)\n",
    "\n",
    "    if start_index != -1:\n",
    "        result_str = result_str[start_index + len(start_tag):].strip()\n",
    "    return result_str\n",
    "\n",
    "\n",
    "# find_core 쿼리에 관련된 답변을 1차적으로 생성해 리트리버의 input으로 넣어줌\n",
    "# 하이드 방법론 - 1차적으로 답변한 것으로 리트리버가 문서 갖고 오도록 유사어와 동의어 들어왔을 때 llm이 인지해서 답변 리트리버가 명확한 답을 가져올 확률이 높다\n",
    "def find_core(x):\n",
    "    generation_config = GenerationConfig(\n",
    "        temperature=1,\n",
    "        top_p=1,\n",
    "        top_k=100,\n",
    "        max_new_tokens=40,\n",
    "        early_stopping=True,\n",
    "        do_sample=True,\n",
    "    )\n",
    "    template = f\"\"\"### instruction:\n",
    "        사실만을 말하는 금융 전문가로서 답해줘.\n",
    "\n",
    "        질문: {x}\\n\\n### Response:\n",
    "\n",
    "    \"\"\"\n",
    "    q = template\n",
    "\n",
    "    gened = model.generate(\n",
    "        **tokenizer(\n",
    "            q,\n",
    "            return_tensors='pt',\n",
    "            return_token_type_ids=False\n",
    "        ).to('cuda'),\n",
    "        generation_config=generation_config,\n",
    "        pad_token_id=tokenizer.eos_token_id,\n",
    "        eos_token_id=tokenizer.eos_token_id,\n",
    "        streamer=streamer,\n",
    "    )\n",
    "    result_str = tokenizer.decode(gened[0])\n",
    "\n",
    "    start_tag = f\"[/INST]\"\n",
    "    start_index = result_str.find(start_tag)\n",
    "\n",
    "    if start_index != -1:\n",
    "        result_str = result_str[start_index + len(start_tag):].strip()\n",
    "    return result_str\n",
    "\n",
    "\n",
    "# 최종 답변을 생성하는 함수\n",
    "def gen_final(x,docs_input):\n",
    "    generation_config = GenerationConfig(\n",
    "        temperature=1,\n",
    "        top_p=0.8,\n",
    "        top_k=100,\n",
    "        max_new_tokens=300,\n",
    "        early_stopping=True,\n",
    "        do_sample=True,\n",
    "        repetition_penalty=1.2,\n",
    "    )\n",
    "    # 많이 시도하면서 보완하면서 작성. 답변을 잘 하지 못하는 케이스 파악 후 보완\n",
    "    template = f\"\"\"### instruction:\n",
    "        너는 사실만을 말하는 금융 전문가야.\n",
    "        질문이 주어진 문서의 내용과 관련이 있다면, 해당 문서를 바탕으로 구체적이고 명확한 답변을 해줘.\n",
    "        모든 문서의 내용을 이해하고 고려해서 질문과 관련된 정보를 모두 찾아 답변 해줘.\n",
    "        만약 질문에 '모두', '전부'와 같은 단어가 포함되어 있다면 해당하는 정보를 문서에서 모두 최대한 찾아서 답변해줘.\n",
    "        문서에 존재하지 않는 내용은 답하면 안돼.\n",
    "        질문에 연도가 포함되어 있다면, 그 연도에 해당하는 정확한 데이터를 제공해줘.\n",
    "        만약 질문에 연도가 명시되지 않았다면, 현재 연도인 2023년의 정보를 사용해줘.\n",
    "        국내 시장과 관련된 질문이면 '내수'로 이해하고 답변해줘.\n",
    "        비율이나 성장률, 이익률 등에 대한 질문에는 그에 해당하는 비율 값을 제공해줘.\n",
    "        대답은 완성된 문장으로 해주고 완성하지 못할것 같으면 이전 문장까지만 만들어줘.\n",
    "        문서 내용과 관련 없거나 애매한 질문이면 '내용을 찾을 수 없습니다.'라고 답변해줘.\n",
    "        소속이나 존재를 묻는 질문은 문서에 정확하게 일치하는 내용이 없다면 없다고 답변해줘\n",
    "        필요한 답변만 하고 불필요한 답변은 생성하면 안돼.\n",
    "        문서에서 정보를 얻을 수 없다면 임의로 생성하지 말고 반드시 '문서에서 내용을 찾을 수 없습니다.'라고 답변해줘.\n",
    "        문서들: {docs_input}\n",
    "        질문: {x}\\n\\n### Response:\n",
    "    \"\"\"\n",
    "\n",
    "    q = template\n",
    "\n",
    "    gened = model.generate(\n",
    "        **tokenizer(\n",
    "            q,\n",
    "            return_tensors='pt',\n",
    "            return_token_type_ids=False\n",
    "        ).to('cuda'),\n",
    "        generation_config=generation_config,\n",
    "        pad_token_id=tokenizer.eos_token_id,\n",
    "        eos_token_id=tokenizer.eos_token_id,\n",
    "        stopping_criteria=stopping_criteria,\n",
    "        streamer=streamer,\n",
    "    )\n",
    "    result_str = tokenizer.decode(gened[0])\n",
    "\n",
    "    start_tag = f\"[/INST]\"\n",
    "    start_index = result_str.find(start_tag)\n",
    "\n",
    "    if start_index != -1:\n",
    "        result_str = result_str[start_index + len(start_tag):].strip()\n",
    "    return result_str\n",
    "\n",
    "def filter_documents_by_query(documents, query):\n",
    "    # 쿼리에서 키워드 확인\n",
    "    query_keywords = [unicodedata.normalize('NFD', keyword) for keyword in [\"하이브\", \"퀄컴\", \"자동차\", \"롯데\",\"현대\",\"기아\",\"미국\"] if keyword in query]\n",
    "    query_keywords2 = [\"하이브\", \"퀄컴\", \"자동차\", \"롯데\",\"현대\",\"기아\",\"미국\"]\n",
    "\n",
    "    # 먼저 pdf의 종류를 고르고, 아래에 해당되는 키워드가 있는지 살펴서 그래프를 가져옴\n",
    "    # query_keywords2 = [unicodedata.normalize('NFD', keyword) for keyword in ['매출','재고','영업이익','EPS','eps'] if keyword in query]\n",
    "    # 해당 키워드를 mainsource로 가지는 문서 필터링\n",
    "    if len(query_keywords)>0:\n",
    "      filtered_docs = [doc for doc in documents if (any(item in doc.metadata.get('source', '') for item in query_keywords)) or (any(item in doc.page_content for item in query_keywords + query_keywords2))]\n",
    "    else:\n",
    "      filtered_docs= [doc for doc in documents]\n",
    "    return filtered_docs\n",
    "\n",
    "from langchain.document_transformers import (\n",
    "    LongContextReorder,\n",
    ")\n",
    "\n",
    "\n",
    "import re\n",
    "\n",
    "# 딕셔너리 형태\n",
    "keyword_replacements = {\n",
    "    'hybe': '하이브',\n",
    "    'lotte': '롯데',\n",
    "    'hyundai': '현대',\n",
    "    'kia': '기아',\n",
    "    'qualcomm': '퀄컴',\n",
    "    'bts' : 'BTS'\n",
    "}\n",
    "\n",
    "def replace_keywords(query):\n",
    "    for eng, kor in keyword_replacements.items():\n",
    "        # re.IGNORECASE를 사용하여 대소문자 구분 없이 검색 및 대체\n",
    "        query = re.sub(re.compile(eng, re.IGNORECASE), kor, query)\n",
    "    return query\n",
    "\n",
    "\n",
    "query=''\n",
    "'''여기서 부터 쿼리 받아서 질답!'''\n",
    "while(query!='exit'):\n",
    "    query=input(\"\\n원하는 질문을 입력하세요. 종료를 원한다면 'exit'라고 입력하세요 : \\n\\n\")\n",
    "    query = replace_keywords(query)\n",
    "\n",
    "    # 쿼리에 그래프가 언급되어 있으면 그래프 이미지를 제시하면서 답변한다\n",
    "    if '그래프' in query:\n",
    "      results_with_scores = db_faiss_graghs.similarity_search(query,6)\n",
    "      graph_docs=[docssss.page_content for docssss in results_with_scores]\n",
    "\n",
    "      for graph_index,gg in enumerate(graph_docs[0:3]):\n",
    "        print(str(graph_index+1)+'. '+str(gg))\n",
    "\n",
    "      select_graph=input('\\n찾으시는 그래프의 번호를 입력 해주세요. 만약 존재하지 않는다면 N을 입력 해주세요. ex) 1번 \\n\\n')\n",
    "\n",
    "      if select_graph=='N' or select_graph=='n':\n",
    "        for graph_index2,ggg in enumerate(graph_docs[3:6]):\n",
    "          print(graph_index2+4,'.',ggg)\n",
    "\n",
    "        select_graph2=input('\\n다시 한번 찾으시는 그래프의 번호를 입력 해주세요. 만약 존재하지 않는다면 N을 입력 해주세요. ex) 1번 \\n\\n')\n",
    "\n",
    "        if select_graph2=='N' or select_graph2=='n':\n",
    "          print('\\n죄송합니다. 해당 문서에는 관련한 그래프가 존재하지 않습니다.\\n')\n",
    "        else:\n",
    "          ss=graph_docs[int(select_graph2[0])-1]\n",
    "          try:\n",
    "            img_test = img.imread('[FINAL] 그래프 png 파일/'+ss+'의 사본.png')\n",
    "            plt.imshow(img_test)\n",
    "            plt.show()\n",
    "\n",
    "          except:\n",
    "            img_test = img.imread('[FINAL] 그래프 png 파일/'+ss+'의 사본.png')\n",
    "            plt.imshow(img_test)\n",
    "            plt.show()\n",
    "          f = open('[FINAL] 그래프 전처리/'+ss.split('_')[0]+'/'+ss+'.txt','r', encoding='utf-8')     # mode = 부분은 생략해도 됨\n",
    "          lines = f.readlines()\n",
    "\n",
    "          # 각 줄을 '.' 기준으로 분리하여 출력\n",
    "          for line in lines:\n",
    "              parts = line.split('.')\n",
    "              for part in parts:\n",
    "                  print(part)\n",
    "      else:\n",
    "        sss=graph_docs[int(select_graph[0])-1]\n",
    "        try:\n",
    "          img_test = img.imread('[FINAL] 그래프 png 파일/'+sss+'의 사본.png')\n",
    "          plt.imshow(img_test)\n",
    "          plt.show()\n",
    "        except:\n",
    "          img_test = img.imread('[FINAL] 그래프 png 파일/'+sss+'의 사본.png')\n",
    "          plt.imshow(img_test)\n",
    "          plt.show()\n",
    "        f = open('[FINAL] 그래프 전처리/'+sss.split('_')[0]+'/'+sss+'.txt','r', encoding='utf-8')     # mode = 부분은 생략해도 됨\n",
    "        lines = f.readlines()\n",
    "\n",
    "        for line in lines:\n",
    "          parts = line.split('.')\n",
    "          for part in parts:\n",
    "            part.replace('\\n\\n','')\n",
    "            print(part)\n",
    "\n",
    "\n",
    "      if (query=='exit'):\n",
    "        print('\\n종료합니다.')\n",
    "        break\n",
    "\n",
    "    else:\n",
    "      if (query=='exit'):\n",
    "        print('\\n종료합니다.')\n",
    "        break\n",
    "      outputs=make_similar_query(query)\n",
    "\n",
    "      # 1차적인 답변 생성\n",
    "      core=find_core(query)\n",
    "      docs = ensemble_retriever.get_relevant_documents(query)\n",
    "      #docs = [doc.page_content for doc in docs]\n",
    "      #core = ','.join(okt.nouns(query))\n",
    "      docs2 =ensemble_retriever.get_relevant_documents(core)\n",
    "      #docs2 = [doc.page_content for doc in docs2]\n",
    "      docs3 =ensemble_retriever.get_relevant_documents(outputs.split('\\n')[1])\n",
    "      #docs3 = [doc.page_content for doc in docs3]\n",
    "      docs4 =ensemble_retriever.get_relevant_documents(outputs.split('\\n')[2].replace('</s>',''))\n",
    "      #docs4 = [doc.page_content for doc in docs4]\n",
    "      docs_all=(docs+docs2+docs3+docs4)\n",
    "      documents=docs_all\n",
    "\n",
    "      # 예시 사용\n",
    "      filtered_documents = filter_documents_by_query(documents, query)\n",
    "\n",
    "      # 필터링된 문서 정보\n",
    "      # filtered_documents_info = [(doc.page_content, doc.metadata) for doc in filtered_documents]\n",
    "      reordering = LongContextReorder()\n",
    "      reordered_docs = reordering.transform_documents(filtered_documents)\n",
    "      reordered_docs = [doc.page_content for doc in reordered_docs]\n",
    "      reordered_docs=set(reordered_docs)\n",
    "\n",
    "      #Reorded_docs가 최종 쿼리가 된다\n",
    "      query_final=reordered_docs\n",
    "\n",
    "      print('\\n')\n",
    "      gen_final(query,query_final)\n",
    "#       # print('답변 : ',gen_final(query,query_final))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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