instruction_datamaker.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip -q install openai"
      ],
      "metadata": {
        "id": "RTpJ8axD8j1Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8452125-18dc-4dda-f2cf-3e12caeaf6ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m221.4/221.4 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.0/75.0 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires tiktoken, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wucl58v8Z4M",
        "outputId": "4f9aeead-2bc8-4a93-a51b-eb7566c1a3df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'stanford_alpaca'...\n",
            "remote: Enumerating objects: 129, done.\u001b[K\n",
            "remote: Counting objects: 100% (75/75), done.\u001b[K\n",
            "remote: Compressing objects: 100% (25/25), done.\u001b[K\n",
            "remote: Total 129 (delta 57), reused 50 (delta 50), pack-reused 54\u001b[K\n",
            "Receiving objects: 100% (129/129), 9.14 MiB | 4.58 MiB/s, done.\n",
            "Resolving deltas: 100% (62/62), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/tatsu-lab/stanford_alpaca.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd stanford_alpaca"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7GbIimj8-15",
        "outputId": "2e7b7e85-9edb-41a5-abe6-76d6c192f398"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/stanford_alpaca\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Alpaca Data Creator\n",
        "\n",
        "\n",
        "<img src=\"https://github.com/tatsu-lab/stanford_alpaca/blob/main/assets/parse_analysis.png?raw=1\" alt=\"example image\" width=\"600\">"
      ],
      "metadata": {
        "id": "tibz9rzb9mKb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "id": "fLiaceig9A8l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8dabe06-9994-44ad-a6bb-1b2e0d9e1dc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: Could not open requirements file: [Errno 2] No such file or directory: 'requirements.txt'\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import openai\n",
        "\n",
        "openai.api_key ='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'\n",
        "os.environ['OPENAI_API_KEY'] = openai.api_key"
      ],
      "metadata": {
        "id": "M5b0ALlsp8Eh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Generations"
      ],
      "metadata": {
        "id": "SGbDrUbi_JJS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-0t-mVE6VQfT",
        "outputId": "751f1f22-d20f-4eb5-b232-509218333931"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain\n",
            "  Downloading langchain-0.0.350-py3-none-any.whl (809 kB)\n",
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            "\u001b[?25hRequirement already satisfied: PyYAML>=5.3 in /usr/local/lib/python3.10/dist-packages (from langchain) (6.0.1)\n",
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            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain)\n",
            "  Downloading dataclasses_json-0.6.3-py3-none-any.whl (28 kB)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Collecting langchain-community<0.1,>=0.0.2 (from langchain)\n",
            "  Downloading langchain_community-0.0.3-py3-none-any.whl (1.5 MB)\n",
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            "\u001b[?25hCollecting langchain-core<0.2,>=0.1 (from langchain)\n",
            "  Downloading langchain_core-0.1.0-py3-none-any.whl (189 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m189.1/189.1 kB\u001b[0m \u001b[31m18.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting langsmith<0.1.0,>=0.0.63 (from langchain)\n",
            "  Downloading langsmith-0.0.70-py3-none-any.whl (45 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy<2,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain) (1.23.5)\n",
            "Requirement already satisfied: pydantic<3,>=1 in /usr/local/lib/python3.10/dist-packages (from langchain) (1.10.13)\n",
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            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (23.1.0)\n",
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            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp<4.0.0,>=3.8.3->langchain) (1.4.0)\n",
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            "Collecting marshmallow<4.0.0,>=3.18.0 (from dataclasses-json<0.7,>=0.5.7->langchain)\n",
            "  Downloading marshmallow-3.20.1-py3-none-any.whl (49 kB)\n",
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            "\u001b[?25hCollecting typing-inspect<1,>=0.4.0 (from dataclasses-json<0.7,>=0.5.7->langchain)\n",
            "  Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
            "Collecting jsonpointer>=1.9 (from jsonpatch<2.0,>=1.33->langchain)\n",
            "  Downloading jsonpointer-2.4-py2.py3-none-any.whl (7.8 kB)\n",
            "Requirement already satisfied: anyio<5,>=3 in /usr/local/lib/python3.10/dist-packages (from langchain-core<0.2,>=0.1->langchain) (3.7.1)\n",
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            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1->langchain) (4.5.0)\n",
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            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3->langchain-core<0.2,>=0.1->langchain) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3->langchain-core<0.2,>=0.1->langchain) (1.2.0)\n",
            "Collecting mypy-extensions>=0.3.0 (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain)\n",
            "  Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Installing collected packages: mypy-extensions, marshmallow, jsonpointer, typing-inspect, langsmith, jsonpatch, langchain-core, dataclasses-json, langchain-community, langchain\n",
            "Successfully installed dataclasses-json-0.6.3 jsonpatch-1.33 jsonpointer-2.4 langchain-0.0.350 langchain-community-0.0.3 langchain-core-0.1.0 langsmith-0.0.70 marshmallow-3.20.1 mypy-extensions-1.0.0 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "import json\n",
        "import io\n",
        "import os\n",
        "import random\n",
        "import re\n",
        "import string\n",
        "from functools import partial\n",
        "from langchain.chat_models import ChatOpenAI\n",
        "from langchain import PromptTemplate, OpenAI, LLMChain\n",
        "from multiprocessing import Pool\n",
        "\n",
        "import numpy as np\n",
        "import tqdm\n",
        "# from rouge_score import rouge_scorer\n",
        "\n",
        "# import fire"
      ],
      "metadata": {
        "id": "qefoyuTtBjkn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def encode_prompt(prompt_instructions):\n",
        "    \"\"\"Encode multiple prompt instructions into a single string.\"\"\"\n",
        "    prompt = \"\"\n",
        "    for idx, task_dict in enumerate(prompt_instructions):\n",
        "        (instruction, input, output) = task_dict[\"instruction\"], task_dict[\"input\"], task_dict[\"output\"]\n",
        "        instruction = re.sub(r\"\\s+\", \" \", instruction).strip().rstrip(\":\")\n",
        "        # Check if input is a string before calling .lower()\n",
        "        input = \"<noinput>\" if isinstance(input, float) or input.strip() == \"\" else input\n",
        "        prompt += f\"Instruction: {instruction}\\n\"\n",
        "        prompt += f\"Input:\\n{input}\\n\"\n",
        "        prompt += f\"Output:\\n{output}\\n\\n\"\n",
        "    return prompt\n",
        "\n",
        "\n",
        "def post_process_gpt4_response(num_prompt_instructions, response):\n",
        "    if response is None:\n",
        "        return []\n",
        "    raw_instructions = f\"Instruction:\" + response\n",
        "    raw_instructions = re.split(\"###\", raw_instructions)\n",
        "    instructions = []\n",
        "    for idx, inst in enumerate(raw_instructions):\n",
        "        idx += num_prompt_instructions + 1\n",
        "        splitted_data = re.split(f\"(Instruction|Input|Output):\", inst)\n",
        "        inst = splitted_data[4].strip()\n",
        "        input = splitted_data[6].strip()\n",
        "        input = \"\" if input.lower() == \"<noinput>\" else input\n",
        "        output = splitted_data[8].strip()\n",
        "        # filter based on keywords that are not suitable for language models.\n",
        "        blacklist = [\n",
        "            \"imagem\",\n",
        "            \"imagens\",\n",
        "            \"gráfico\",\n",
        "            \"gráficos\",\n",
        "            \"foto\",\n",
        "            \"fotos\",\n",
        "            \"arquivo\",\n",
        "            \"arquivos\",\n",
        "            \"mapa\",\n",
        "            \"mapas\",\n",
        "            \"desenhar\",\n",
        "            \"parcela\",\n",
        "            \"ir para\",\n",
        "            \"vídeo\",\n",
        "            \"áudio\",\n",
        "            \"música\",\n",
        "            \"fluxograma\",\n",
        "            \"diagrama\",\n",
        "        ]\n",
        "\n",
        "        blacklist += []\n",
        "        if any(find_word_in_string(word, inst) for word in blacklist):\n",
        "            continue\n",
        "        # We found that the model tends to add \"write a program\" to some existing instructions, which lead to a lot of such instructions.\n",
        "        # And it's a bit comfusing whether the model need to write a program or directly output the result.\n",
        "        # Here we filter them out.\n",
        "        # Note this is not a comprehensive filtering for all programming instructions.\n",
        "        if inst.startswith(\"Write a program\"):\n",
        "            continue\n",
        "        instructions.append({\"instruction\": inst, \"input\": input, \"output\": output})\n",
        "    return instructions\n",
        "\n",
        "def _make_w_io_base(f, mode: str):\n",
        "    if not isinstance(f, io.IOBase):\n",
        "        f_dirname = os.path.dirname(f)\n",
        "        if f_dirname != \"\":\n",
        "            os.makedirs(f_dirname, exist_ok=True)\n",
        "        f = open(f, mode=mode)\n",
        "    return f\n",
        "\n",
        "def _make_r_io_base(f, mode: str):\n",
        "    if not isinstance(f, io.IOBase):\n",
        "        f = open(f, mode=mode)\n",
        "    return f\n",
        "\n",
        "def jdump(obj, f, mode=\"w\", indent=4, default=str):\n",
        "    \"\"\"Dump a str or dictionary to a file in json format.\n",
        "\n",
        "    Args:\n",
        "        obj: An object to be written.\n",
        "        f: A string path to the location on disk.\n",
        "        mode: Mode for opening the file.\n",
        "        indent: Indent for storing json dictionaries.\n",
        "        default: A function to handle non-serializable entries; defaults to `str`.\n",
        "    \"\"\"\n",
        "    f = _make_w_io_base(f, mode)\n",
        "    if isinstance(obj, (dict, list)):\n",
        "        json.dump(obj, f, indent=indent, default=default)\n",
        "    elif isinstance(obj, str):\n",
        "        f.write(obj)\n",
        "    else:\n",
        "        raise ValueError(f\"Unexpected type: {type(obj)}\")\n",
        "    f.close()\n",
        "\n",
        "def jload(f, mode=\"r\"):\n",
        "    \"\"\"Load a .json file into a dictionary.\"\"\"\n",
        "    f = _make_r_io_base(f, mode)\n",
        "    jdict = json.load(f)\n",
        "    f.close()\n",
        "    return jdict\n",
        "\n",
        "def find_word_in_string(w, s):\n",
        "    return re.compile(r\"\\b({0})\\b\".format(w), flags=re.IGNORECASE).search(s)\n",
        "\n",
        "\n",
        "def generate_instruction_following_data(\n",
        "    api_key: str,\n",
        "    output_dir=\"./\",\n",
        "    seed_tasks_path=\"./seed_tasks.jsonl\",\n",
        "    num_instructions_to_generate=100,\n",
        "    model_name=\"gpt-4\",\n",
        "    num_prompt_instructions=3,\n",
        "    request_batch_size=5,\n",
        "    temperature=0.1,\n",
        "    max_tokens=2000,\n",
        "    frequency_penalty=0,\n",
        "    presence_penalty=0,\n",
        "    top_p=0.75,\n",
        "    num_cpus=16,\n",
        "):\n",
        "\n",
        "    # Load JSON data from a file\n",
        "    with open(seed_tasks_path, 'r') as f:\n",
        "        seed_tasks = json.load(f)\n",
        "\n",
        "    # Transform the data\n",
        "    seed_instruction_data = [\n",
        "        {\"instruction\": t[\"instruction\"], \"input\": t[\"instances\"][0][\"input\"], \"output\": t[\"instances\"][0][\"output\"]}\n",
        "        for t in seed_tasks\n",
        "    ]\n",
        "\n",
        "    print(f\"Loaded {len(seed_instruction_data)} human-written seed instructions\")\n",
        "\n",
        "    os.makedirs(output_dir, exist_ok=True)\n",
        "    request_idx = 0\n",
        "    # load the LM-generated instructions\n",
        "    machine_instruction_data = []\n",
        "    if os.path.exists(os.path.join(output_dir, \"regen4.json\")):\n",
        "        machine_instruction_data = jload(os.path.join(output_dir, \"regen4.json\"))\n",
        "        print(f\"Loaded {len(machine_instruction_data)} machine-generated instructions\")\n",
        "\n",
        "    # similarities = {}\n",
        "    scorer = rouge_scorer.RougeScorer([\"rougeL\"], use_stemmer=False)\n",
        "\n",
        "    # now let's generate new instructions!\n",
        "    progress_bar = tqdm.tqdm(total=num_instructions_to_generate)\n",
        "    if machine_instruction_data:\n",
        "        progress_bar.update(len(machine_instruction_data))\n",
        "\n",
        "    # first we tokenize all the seed instructions and generated machine instructions\n",
        "    all_instructions = [d[\"instruction\"] for d in seed_instruction_data] + [\n",
        "        d[\"instruction\"] for d in machine_instruction_data\n",
        "    ]\n",
        "    all_instruction_tokens = [scorer._tokenizer.tokenize(inst) for inst in all_instructions]\n",
        "\n",
        "    prompt_tmp_txt = open(\"/content/stanford_alpaca/prompt2.txt\").read() + \"\\n\"\n",
        "\n",
        "    evidence_prompt_instruction = ''\n",
        "\n",
        "    # Initialize the OpenAI model\n",
        "    model = ChatOpenAI(\n",
        "        openai_api_key=api_key,\n",
        "        model_name=model_name,\n",
        "        temperature=temperature,\n",
        "        max_tokens=max_tokens,\n",
        "        frequency_penalty=frequency_penalty,\n",
        "        presence_penalty=presence_penalty,\n",
        "        top_p=top_p,\n",
        "        request_timeout=180\n",
        "    )\n",
        "\n",
        "    prompt_template = PromptTemplate(template=prompt_tmp_txt, input_variables=[\"input\"])\n",
        "    llm_chain = LLMChain(prompt=prompt_template, llm=model)\n",
        "\n",
        "    while len(machine_instruction_data) < num_instructions_to_generate:\n",
        "        request_idx += 1\n",
        "\n",
        "        results = []\n",
        "        request_start = time.time()\n",
        "\n",
        "        for _ in range(request_batch_size):\n",
        "            # only sampling from the seed tasks\n",
        "            prompt_instructions = random.sample(seed_instruction_data, num_prompt_instructions)\n",
        "            prompt = encode_prompt(prompt_instructions)\n",
        "            # print(prompt)\n",
        "            result = llm_chain.predict(input=prompt)\n",
        "            print(f'===Result===\\n\\n{result}\\n\\n')\n",
        "            results.append(result)\n",
        "\n",
        "        request_duration = time.time() - request_start\n",
        "\n",
        "        process_start = time.time()\n",
        "        instruction_data = []\n",
        "        for result in results:\n",
        "            new_instructions = post_process_gpt4_response(num_prompt_instructions, result)\n",
        "            instruction_data += new_instructions\n",
        "\n",
        "        total = len(instruction_data)\n",
        "        keep = 0\n",
        "        for instruction_data_entry in instruction_data:\n",
        "            # computing similarity with the pre-tokenzied instructions\n",
        "            new_instruction_tokens = scorer._tokenizer.tokenize(instruction_data_entry[\"instruction\"])\n",
        "            with Pool(num_cpus) as p:\n",
        "                rouge_scores = p.map(\n",
        "                    partial(rouge_scorer._score_lcs, new_instruction_tokens),\n",
        "                    all_instruction_tokens,\n",
        "                )\n",
        "            rouge_scores = [score.fmeasure for score in rouge_scores]\n",
        "            most_similar_instructions = {\n",
        "                all_instructions[i]: rouge_scores[i] for i in np.argsort(rouge_scores)[-10:][::-1]\n",
        "            }\n",
        "            instruction_data_entry[\"most_similar_instructions\"] = most_similar_instructions\n",
        "            instruction_data_entry[\"avg_similarity_score\"] = float(np.mean(rouge_scores))\n",
        "            machine_instruction_data.append(instruction_data_entry)\n",
        "            all_instructions.append(instruction_data_entry[\"instruction\"])\n",
        "            all_instruction_tokens.append(new_instruction_tokens)\n",
        "            progress_bar.update(1)\n",
        "            keep+=1\n",
        "        process_duration = time.time() - process_start\n",
        "        print(f\"Request {request_idx} took {request_duration:.2f}s, processing took {process_duration:.2f}s\")\n",
        "        print(f\"Generated {total} instructions, kept {keep} instructions\")\n",
        "        jdump(machine_instruction_data, os.path.join(output_dir, \"regen4.json\"))\n"
      ],
      "metadata": {
        "id": "JlvGPSJK_cT2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "t6C2kXsjGy47"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load JSON data from a file\n",
        "with open('/content/seed_tasks.json', 'r') as f:\n",
        "    seed_tasks = json.load(f)\n",
        "\n",
        "# Transform the data\n",
        "seed_instruction_data = [\n",
        "    {\"instruction\": t[\"instruction\"], \"input\": t[\"instances\"][0][\"input\"], \"output\": t[\"instances\"][0][\"output\"]}\n",
        "    for t in seed_tasks\n",
        "]"
      ],
      "metadata": {
        "id": "mvN0Cuw0KUtX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt_instructions=random.sample(seed_instruction_data, 3)\n",
        "prompt = encode_prompt(prompt_instructions)"
      ],
      "metadata": {
        "id": "M9htOdTJKdRy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "_17AvB8PMs1C",
        "outputId": "719a4dc8-03ee-478e-8663-896994131579"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'###\\nInstruction: Reescreva o exame exame físico a seguir\\nInput:\\nEXAME FÍSICO GERAL – BEG, aaa, corado, hidratado. Cardio =  BRNF sem sopros. Pulmonar = MV + sem RA. Abdomen = flácido, sem visceromegalias. Extremidades = sem edema.\\nOutput:\\nEXAME FÍSICO GERAL:\\nO paciente se apresenta em bom estado geral (BEG), alerta, ativo e alimentado (aaa), com coloração facial normal e adequadamente hidratado.\\nA avaliação cardiovascular não indica anormalidades, com batimentos rítmicos normofrequentes (BRNF) e ausência de sopros.\\nA avaliação pulmonar demonstra murmúrios vesiculares (MV) presentes e sem ruídos adventícios (RA).\\nO abdômen está flácido, sem aumento de órgãos internos (sem visceromegalias).\\nAs extremidades não apresentam sem edema.\\n###\\nInstruction: Crie uma anamnese de um caso de osteoartrite\\nInput:\\n<noinput>\\nOutput:\\nQueixa e duração:\\nPaciente queixa de dores nos joelhos havia três anos, com piora nos últimos meses. Também tem restrições para prática de esportes e exercícios.\\n\\nAntecedentes pessoais:\\n        •        Pacientes relatava ser portador de hiperuricemia controlada com o uso de medicamento.\\n        •        Tabagista regular ( +- 20 cigarros/dia).\\n        •        Relatava ter feito tratamento com antiinflamatórios (AINH) e fisioterapia por seis meses, com pouca melhora do quadro doloroso.\\n###\\nInstruction: Identifique o evento clínico mais recente da lista\\nInput:\\nResultados de exame: Creatinina = 0,9 (03/01/23); ECG normal (11/10/2021); EAS normal (26/06/2022); Creatinina = 1,1 (31/03/23); Hemograma com leve leucocitose (02/12/2021).\\nOutput:\\nO evento clínico mais recente é o exame de creatinina de 31/03/23.\\n###\\n4. Instruction:'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## the data"
      ],
      "metadata": {
        "id": "eDzyIc89ENYR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/data.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "UgmK0-D7MTJt",
        "outputId": "b13809fc-9678-44ca-b742-c8666a379dcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         instruction  \\\n",
              "0                  Encontre os diagnósticos no texto   \n",
              "1      Gere um plano terapêutico para o caso clínico   \n",
              "2  Identifique os sinais de alerta presentes no t...   \n",
              "3                        Sumarize o registro clínico   \n",
              "4                    Identifique os exames alterados   \n",
              "\n",
              "                                               input  \\\n",
              "0  Relata dor no ombro esquerdo há 20 dias, pior ...   \n",
              "1  Dor lombar iniciada ontem, após movimento brus...   \n",
              "2  Paciente do sexo feminino, 38 anos, com dor e ...   \n",
              "3  Registro Clínico Diário 1 \\n------------------...   \n",
              "4  Paciente traz exames: Hb = 9,8; Leuc = 8500; P...   \n",
              "\n",
              "                                              output classification  \n",
              "0  Foram encontrados os diagnósticos de Hipertens...          False  \n",
              "1  Antiinflamatório não esteroidal oral (Ibuprofe...          False  \n",
              "2  O sinais de alerta são: 1: Edema em membro inf...          False  \n",
              "3  Paciente de 58 anos, sexo masculino, admitido ...          False  \n",
              "4  Dentre os exames, o único com alteração é o de...          False  "
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>instruction</th>\n",
              "      <th>input</th>\n",
              "      <th>output</th>\n",
              "      <th>classification</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Encontre os diagnósticos no texto</td>\n",
              "      <td>Relata dor no ombro esquerdo há 20 dias, pior ...</td>\n",
              "      <td>Foram encontrados os diagnósticos de Hipertens...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Gere um plano terapêutico para o caso clínico</td>\n",
              "      <td>Dor lombar iniciada ontem, após movimento brus...</td>\n",
              "      <td>Antiinflamatório não esteroidal oral (Ibuprofe...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Identifique os sinais de alerta presentes no t...</td>\n",
              "      <td>Paciente do sexo feminino, 38 anos, com dor e ...</td>\n",
              "      <td>O sinais de alerta são: 1: Edema em membro inf...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sumarize o registro clínico</td>\n",
              "      <td>Registro Clínico Diário 1 \\n------------------...</td>\n",
              "      <td>Paciente de 58 anos, sexo masculino, admitido ...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Identifique os exames alterados</td>\n",
              "      <td>Paciente traz exames: Hb = 9,8; Leuc = 8500; P...</td>\n",
              "      <td>Dentre os exames, o único com alteração é o de...</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8bc24a2-3750-4798-811e-4fe8ba352ba3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-e0efa3f2-1dfc-48e3-b25f-9c85c4fcc6ed\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e0efa3f2-1dfc-48e3-b25f-9c85c4fcc6ed')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-e0efa3f2-1dfc-48e3-b25f-9c85c4fcc6ed button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f8bc24a2-3750-4798-811e-4fe8ba352ba3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8bc24a2-3750-4798-811e-4fe8ba352ba3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def transform_dataframe(df):\n",
        "    # Initialize an empty list to store the dictionaries\n",
        "    json_list = []\n",
        "\n",
        "    # Loop through each row in the dataframe\n",
        "    for idx, row in df.iterrows():\n",
        "        # Create a dictionary for each row\n",
        "        dict_ = {'id': 'seed_task_' + str(idx),\n",
        "                 'name': 'explain_behavior',\n",
        "                 'instruction': row['instruction'],\n",
        "                 'instances': [{'input': row['input'], 'output': row['output']}],\n",
        "                 'is_classification': False}\n",
        "\n",
        "        # Append the dictionary to the list\n",
        "        json_list.append(dict_)\n",
        "\n",
        "    # Return the list of dictionaries\n",
        "    return json_list\n",
        "\n",
        "# Call the function\n",
        "json_list = transform_dataframe(df)\n",
        "\n",
        "# Save the JSON to a file\n",
        "with open('/content/seed_tasks.json', 'w') as f:\n",
        "    json.dump(json_list, f)"
      ],
      "metadata": {
        "id": "2E627onbNCpY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "seed_tasks_path=\"/content/seed_tasks.json\""
      ],
      "metadata": {
        "id": "umZAAxQtNrX9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Seed Tasks"
      ],
      "metadata": {
        "id": "_ztLh8ck0NAc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# Load JSON data from a file\n",
        "with open(seed_tasks_path, 'r') as f:\n",
        "    seed_tasks = json.load(f)\n",
        "\n",
        "# Transform the data\n",
        "seed_instruction_data = [\n",
        "    {\"instruction\": t[\"instruction\"], \"input\": t[\"instances\"][0][\"input\"], \"output\": t[\"instances\"][0][\"output\"]}\n",
        "    for t in seed_tasks\n",
        "]\n",
        "\n",
        "print(f\"Loaded {len(seed_instruction_data)} human-written seed instructions\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "co3sx8Ed0MxQ",
        "outputId": "5c33c8f3-9759-422f-da05-b4c057ec2464"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded 24 human-written seed instructions\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seed_tasks[20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IjIydavr0OQH",
        "outputId": "81c3bed2-2c19-4594-a840-a6ce2b690c29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': 'seed_task_20',\n",
              " 'name': 'explain_behavior',\n",
              " 'instruction': 'Faça uma lista com os exames de rastreamento a serem solicitados no caso clínico',\n",
              " 'instances': [{'input': 'Sem queixas. Vem para exames de rotina. Nega doenças crônicas. Tabagista há 20 anos. Carga de 25 anos/maço. História familiar: pai falecido AVE isquêmico. Mãe, irmãoes e filhos hígidos. Exame físico sem alterações. PA: 130 x 80. FC: 72bpm. ',\n",
              "   'output': 'Exames de rastreamento\\n\\nTomografia computadorizada de baixa dose (TCBD) do tórax\\nJustificativa: Paciente é tabagista há 20 anos com carga de 25 anos/maço.\\n\\nMamografia\\nJustificativa: Paciente é uma mulher de 62 anos, dentro da faixa etária recomendada para o rastreamento do câncer de mama.\\n\\nCitopatológico (Papanicolau)\\nJustificativa: Paciente é uma mulher de 62 anos, dentro da faixa etária recomendada para o rastreamento do câncer do colo do útero.\\n\\nDensitometria óssea\\nJustificativa: Paciente é uma mulher de 62 anos, tabagista, o que aumenta o risco para osteoporose.\\n\\nPesquisa de sangue oculto nas fezes ou retossigmoidoscopia ou colonoscopia\\nJustificativa: Paciente está na faixa etária recomendada para o rastreamento do câncer colorretal.\\n\\nGlicemia de jejum ou hemoglobina glicada\\nJustificativa: Paciente está na faixa etária recomendada para o rastreamento de diabetes tipo 2.\\n\\nColesterol total e frações e Triglicérides\\nJustificativa: Paciente está na faixa etária recomendada para o rastreamento de dislipidemias.\\n'}],\n",
              " 'is_classification': False}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating a new set of seed tasks"
      ],
      "metadata": {
        "id": "LABR8_l-2mJN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "generate_instruction_following_data(\n",
        "    api_key=openai.api_key,\n",
        "    output_dir=\"/content/stanford_alpaca/new_tasks\",\n",
        "    seed_tasks_path=\"/content/seed_tasks.json\",\n",
        "    num_instructions_to_generate=300,\n",
        "    model_name=\"gpt-4\",\n",
        "    num_prompt_instructions=1,\n",
        "    request_batch_size=5,\n",
        "    temperature=0.5,\n",
        "    top_p=1,\n",
        "    num_cpus=4,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PitLnTF6Aznv",
        "outputId": "af7b013b-1fd3-4090-9d2e-8edd9d1b64e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded 24 human-written seed instructions\n",
            "Loaded 180 machine-generated instructions\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "  0%|          | 0/300 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[AWARNING:langchain.chat_models.openai:WARNING! frequency_penalty is not default parameter.\n",
            "                    frequency_penalty was transferred to model_kwargs.\n",
            "                    Please confirm that frequency_penalty is what you intended.\n",
            "WARNING:langchain.chat_models.openai:WARNING! presence_penalty is not default parameter.\n",
            "                    presence_penalty was transferred to model_kwargs.\n",
            "                    Please confirm that presence_penalty is what you intended.\n",
            "WARNING:langchain.chat_models.openai:WARNING! top_p is not default parameter.\n",
            "                    top_p was transferred to model_kwargs.\n",
            "                    Please confirm that top_p is what you intended.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "===Result===\n",
            "\n",
            "Instruction: Elabore um plano de tratamento para um paciente com diagnóstico recente de esquizofrenia.\n",
            "Input:\n",
            "Paciente do sexo masculino, 22 anos, apresentando delírios, alucinações auditivas, comportamento desorganizado e discurso incoerente. Diagnóstico recente de esquizofrenia. Sem comorbidades conhecidas. Exame físico normal. HD: esquizofrenia.\n",
            "Output:\n",
            "Iniciar Antipsicótico atípico (Risperidona 2mg/dia com aumento gradual até 4-6mg/dia conforme tolerância), Terapia Cognitivo-Comportamental com psicólogo especializado em esquizofrenia, suporte familiar e social, orientações sobre sinais de piora ou efeitos colaterais dos medicamentos, acompanhamento regular com psiquiatra e equipe multidisciplinar, retornar se houver piora dos sintomas ou surgimento de efeitos colaterais.\n",
            "\n",
            "\n",
            "===Result===\n",
            "\n",
            "Instruction: Crie um plano de tratamento para um paciente com diagnóstico recente de diabetes tipo 2.\n",
            "Input: \n",
            "Paciente de 55 anos, recém-diagnosticado com diabetes tipo 2. Sem outras comorbidades. Exame físico normal. HD: Diabetes mellitus tipo 2.\n",
            "Output: \n",
            "Iniciar metformina 500mg duas vezes ao dia, aumentando para 1000mg duas vezes ao dia após duas semanas se tolerado. Aconselhamento dietético e plano de exercícios físicos, com perda de peso se o IMC estiver acima de 25. Monitorização da glicemia em casa duas vezes ao dia, antes do café da manhã e do jantar. Agendar consulta de acompanhamento em 3 meses para avaliar o controle glicêmico e ajustar o tratamento se necessário. Orientações sobre sinais de hipoglicemia e hiperglicemia. Iniciar a estatinas e anti-hipertensivos se indicado para proteção cardiovascular.\n",
            "\n",
            "\n",
            "===Result===\n",
            "\n",
            "Instruction: Crie um registro de avaliação de paciente com suspeita de angina estável\n",
            "Input:\n",
            "<noinput>\n",
            "Output:\n",
            "Paciente apresenta queixa de dor torácica de caráter opressivo, localizada no meio do peito, irradiando para o braço esquerdo, com duração de cerca de 10 minutos, induzida por esforço físico e aliviada com repouso. \n",
            "No exame físico, está consciente e orientado, com pele fria e suada. \n",
            "Ausculta cardíaca revela ritmo cardíaco regular, sem sopros. \n",
            "Ausculta pulmonar sem alterações. \n",
            "Pulsos periféricos presentes e simétricos. \n",
            "Pressão arterial de 140/90 mmHg, frequência cardíaca de 80 bpm. \n",
            "ECG de repouso mostra alterações sugestivas de isquemia subendocárdica. \n",
            "O paciente será encaminhado para o teste de esforço para avaliação funcional da doença arterial coronariana. \n",
            "O uso de medicamento antianginoso será iniciado, e orientações sobre mudanças no estilo de vida e controle dos fatores de risco cardiovascular foram fornecidas.\n",
            "\n",
            "\n",
            "===Result===\n",
            "\n",
            "Instruction: Gere uma lista com as possíveis terapias para tratamento de esclerose múltipla\n",
            "Input:\n",
            "<noinput>\n",
            "Output:\n",
            "Terapias de modificação da doença (DMDs): Estas são o principal tipo de tratamento para a esclerose múltipla e incluem interferons (Avonex, Betaseron, Extavia, Plegridy, Rebif), glatiramer acetato (Copaxone, Glatopa), teriflunomida (Aubagio), dimetil fumarato (Tecfidera), fingolimod (Gilenya), alemtuzumab (Lemtrada), ocrelizumab (Ocrevus) e natalizumab (Tysabri).\n",
            "\n",
            "Corticosteroides: Prednisona oral e metilprednisolona intravenosa são comumente usados para tratar surtos de esclerose múltipla.\n",
            "\n",
            "Terapias de plasmaferese: Esta é uma opção para pessoas com surtos graves de sintomas da esclerose múltipla que não responderam ao tratamento com corticosteroides.\n",
            "\n",
            "Medicamentos para sintomas específicos: Estes podem incluir medicamentos para tratar a fadiga, dificuldade em caminhar, depressão, problemas de bexiga e intestino, espasticidade, dor, problemas sexuais e dificuldades cognitivas.\n",
            "\n",
            "Reabilitação: Fisioterapia, terapia ocupacional, terapia da fala, terapia cognitiva, terapia de deglutição e reabilitação vocacional podem ser úteis.\n",
            "\n",
            "Estimulação elétrica funcional: Este tratamento pode ajudar a melhorar a função de caminhada em algumas pessoas com esclerose múltipla.\n",
            "\n",
            "Terapias complementares e alternativas: Estas podem incluir acupuntura, hipnose, reflexologia, meditação, yoga, tai chi, massagem, suplementos dietéticos e cannabis medicinal.\n",
            "\n",
            "\n",
            "===Result===\n",
            "\n",
            "\"\"\"\n",
            "Instruction: Elabore um protocolo para avaliação de suspeita de infecção por Clostridium difficile\n",
            "Input:\n",
            "<noinput>\n",
            "Output:\n",
            "\n",
            "[ANAMNESE]\n",
            "- Início dos sintomas: ________\n",
            "- Quantidade de evacuações diárias: ________\n",
            "- Presença de muco ou sangue nas fezes: ________\n",
            "- Febre: ________\n",
            "- Dor abdominal: ________\n",
            "- Histórico de uso de antibióticos nas últimas 8 semanas: ________\n",
            "- Histórico de hospitalização recente: ________\n",
            "\n",
            "[EXAME FÍSICO]\n",
            "- Estado geral: ________________________________________\n",
            "- Sinais vitais: ________________________________________\n",
            "- Abdome: ___________________________________________\n",
            "- Desidratação: ______________________________________\n",
            "\n",
            "[EXAMES LABORATORIAIS]\n",
            "- Hemograma: ________________________________________\n",
            "- Função renal: _______________________________________\n",
            "- Eletrólitos: _________________________________________\n",
            "- Proteína C reativa: __________________________________\n",
            "- Coprocultura: ______________________________________\n",
            "- Teste de toxina A/B para C. difficile: ___________________\n",
            "\n",
            "[CONDUTA]\n",
            "- Suspensão do antibiótico causador, se possível: ________\n",
            "- Isolamento do paciente: ______________________________\n",
            "- Início de terapia específica para C. difficile: ____________\n",
            "- Avaliação de necessidade de cirurgia: _________________\n",
            "\n",
            "\"\"\"\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            " 60%|██████    | 181/300 [01:41<01:06,  1.79it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            " 61%|██████    | 182/300 [01:41<01:05,  1.80it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 61%|██████    | 183/300 [01:41<01:04,  1.82it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
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            "\n",
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            " 61%|██████▏   | 184/300 [01:41<01:03,  1.84it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            " 62%|██████▏   | 185/300 [01:41<01:01,  1.87it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Request 1 took 101.01s, processing took 0.61s\n",
            "Generated 5 instructions, kept 5 instructions\n",
            "===Result===\n",
            "\n",
            "Instruction: Elabore um plano de tratamento baseado nos sintomas e diagnósticos.\n",
            "Input:\n",
            "Dor abdominal crônica, diarreia recorrente. Diagnóstico de Doença de Crohn. Exame físico sem alterações.\n",
            "Output:\n",
            "Paciente com diagnóstico de Doença de Crohn, apresentando dor abdominal crônica e episódios recorrentes de diarreia. O plano de tratamento incluirá o uso de anti-inflamatórios intestinais, como a mesalazina, e imunossupressores, como o azatioprina, para reduzir a inflamação intestinal. A terapia nutricional também será essencial para garantir a ingestão adequada de nutrientes. O paciente será orientado a evitar alimentos que possam agravar os sintomas, como alimentos gordurosos, crus e com alto teor de fibras. Será feito acompanhamento regular para avaliar a resposta ao tratamento e ajustar as medicações conforme necessário.\n",
            "\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-127-a414c0716159>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m generate_instruction_following_data(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mapi_key\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mopenai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi_key\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0moutput_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"/content/stanford_alpaca/new_tasks\"\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[0mseed_tasks_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"/content/seed_tasks.json\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mnum_instructions_to_generate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-126-bc110813b36e>\u001b[0m in \u001b[0;36mgenerate_instruction_following_data\u001b[0;34m(api_key, output_dir, seed_tasks_path, num_instructions_to_generate, model_name, num_prompt_instructions, request_batch_size, temperature, max_tokens, frequency_penalty, presence_penalty, top_p, num_cpus)\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0mprompt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencode_prompt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt_instructions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0;31m# print(prompt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mllm_chain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprompt\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    186\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'===Result===\\n\\n{result}\\n\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m             \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\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/langchain/chains/llm.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    250\u001b[0m                 \u001b[0mcompletion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mllm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madjective\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"funny\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m         \"\"\"\n\u001b[0;32m--> 252\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_key\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    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m     \u001b[0;32masync\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mapredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mCallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\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/langchain/chains/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m)\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    242\u001b[0m             \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_error\u001b[0m\u001b[0;34m(\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[0;32m--> 243\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    244\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_chain_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m         final_outputs: Dict[str, Any] = self.prep_outputs(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)\u001b[0m\n\u001b[1;32m    235\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m             outputs = (\n\u001b[0;32m--> 237\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\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    238\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m                 \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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/langchain/chains/llm.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0mrun_manager\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mCallbackManagerForChainRun\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     ) -> Dict[str, str]:\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\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     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, input_list, run_manager)\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0;34m\"\"\"Generate LLM result from inputs.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0mprompts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprep_prompts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m         return self.llm.generate_prompt(\n\u001b[0m\u001b[1;32m    103\u001b[0m             \u001b[0mprompts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m             \u001b[0mstop\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/langchain/chat_models/base.py\u001b[0m in \u001b[0;36mgenerate_prompt\u001b[0;34m(self, prompts, stop, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    228\u001b[0m     ) -> LLMResult:\n\u001b[1;32m    229\u001b[0m         \u001b[0mprompt_messages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_messages\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprompts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt_messages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m     async def agenerate_prompt(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chat_models/base.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mrun_managers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m                     \u001b[0mrun_managers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_llm_error\u001b[0m\u001b[0;34m(\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[0;32m--> 125\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    126\u001b[0m         flattened_outputs = [\n\u001b[1;32m    127\u001b[0m             \u001b[0mLLMResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mllm_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllm_output\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/langchain/chat_models/base.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, messages, stop, callbacks, tags, metadata, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m                 results.append(\n\u001b[0;32m--> 115\u001b[0;31m                     self._generate_with_cache(\n\u001b[0m\u001b[1;32m    116\u001b[0m                         \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m                         \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\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/langchain/chat_models/base.py\u001b[0m in \u001b[0;36m_generate_with_cache\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    260\u001b[0m                 )\n\u001b[1;32m    261\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m                 return self._generate(\n\u001b[0m\u001b[1;32m    263\u001b[0m                     \u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chat_models/openai.py\u001b[0m in \u001b[0;36m_generate\u001b[0;34m(self, messages, stop, run_manager, **kwargs)\u001b[0m\n\u001b[1;32m    369\u001b[0m             )\n\u001b[1;32m    370\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mChatResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mChatGeneration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmessage\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[0;32m--> 371\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompletion_with_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmessage_dicts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\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    372\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_chat_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chat_models/openai.py\u001b[0m in \u001b[0;36mcompletion_with_retry\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    317\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_completion_with_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_combine_llm_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mllm_outputs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mdict\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/tenacity/__init__.py\u001b[0m in \u001b[0;36mwrapped_f\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    287\u001b[0m         \u001b[0;34m@\u001b[0m\u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mwrapped_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\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    290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mretry_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mWrappedFn\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/tenacity/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    377\u001b[0m         \u001b[0mretry_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRetryCallState\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_object\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\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--> 379\u001b[0;31m             \u001b[0mdo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretry_state\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    380\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDoAttempt\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    381\u001b[0m                 \u001b[0;32mtry\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/tenacity/__init__.py\u001b[0m in \u001b[0;36miter\u001b[0;34m(self, retry_state)\u001b[0m\n\u001b[1;32m    312\u001b[0m         \u001b[0mis_explicit_retry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfailed\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTryAgain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mis_explicit_retry\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_state\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[0;32m--> 314\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\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[0m\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    449\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\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    450\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 451\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\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[0m\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    401\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    404\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m                 \u001b[0;31m# Break a reference cycle with the exception in self._exception\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/tenacity/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    380\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDoAttempt\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    381\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    383\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# noqa: B902\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m                     \u001b[0mretry_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[arg-type]\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/langchain/chat_models/openai.py\u001b[0m in \u001b[0;36m_completion_with_retry\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m    315\u001b[0m         \u001b[0;34m@\u001b[0m\u001b[0mretry_decorator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m_completion_with_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 317\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    318\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_completion_with_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/openai/api_resources/chat_completion.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m     23\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[1;32m     24\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     26\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTryAgain\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     27\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtimeout\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/openai/api_resources/abstract/engine_api_resource.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(cls, api_key, api_base, api_type, request_id, api_version, organization, **params)\u001b[0m\n\u001b[1;32m    151\u001b[0m         )\n\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m         response, _, api_key = requestor.request(\n\u001b[0m\u001b[1;32m    154\u001b[0m             \u001b[0;34m\"post\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             \u001b[0murl\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/openai/api_requestor.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, headers, files, stream, request_id, request_timeout)\u001b[0m\n\u001b[1;32m    286\u001b[0m         \u001b[0mrequest_timeout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m     ) -> Tuple[Union[OpenAIResponse, Iterator[OpenAIResponse]], bool, str]:\n\u001b[0;32m--> 288\u001b[0;31m         result = self.request_raw(\n\u001b[0m\u001b[1;32m    289\u001b[0m             \u001b[0mmethod\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\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m             \u001b[0murl\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/openai/api_requestor.py\u001b[0m in \u001b[0;36mrequest_raw\u001b[0;34m(self, method, url, params, supplied_headers, files, stream, request_id, request_timeout)\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0m_thread_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession_create_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\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    595\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 596\u001b[0;31m             result = _thread_context.session.request(\n\u001b[0m\u001b[1;32m    597\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m                 \u001b[0mabs_url\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/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    527\u001b[0m         }\n\u001b[1;32m    528\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 529\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\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    530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\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/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    644\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 645\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\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/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    438\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m                 resp = conn.urlopen(\n\u001b[0m\u001b[1;32m    441\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\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/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    713\u001b[0m             \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 714\u001b[0;31m             httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m    715\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    716\u001b[0m                 \u001b[0mmethod\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/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    464\u001b[0m                     \u001b[0;31m# Python 3 (including for exceptions like SystemExit).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m                     \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 466\u001b[0;31m                     \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\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    467\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\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    468\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\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/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    459\u001b[0m                 \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 461\u001b[0;31m                     \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\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[0m\u001b[1;32m    462\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\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    463\u001b[0m                     \u001b[0;31m# Remove the TypeError from the exception chain in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1373\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1374\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1375\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\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[0m\u001b[1;32m   1376\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1377\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    316\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\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--> 318\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\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[0m\u001b[1;32m    319\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;32m--> 279\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\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    280\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    703\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[1;32m    704\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\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    706\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1272\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1273\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1274\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\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   1275\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1276\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1128\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1129\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1130\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\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   1131\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\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: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "file_path = \"/content/stanford_alpaca/new_tasks/regen.json\"\n",
        "\n",
        "with open(file_path, 'r') as json_file:\n",
        "    data = json.load(json_file)\n",
        "\n",
        "# drop the most similar as measured by Rouge\n",
        "for dictionary in data:\n",
        "    dictionary.pop('most_similar_instructions', None)\n",
        "    dictionary.pop('avg_similarity_score',None)\n",
        "\n",
        "\n",
        "print(data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ps-91LWXBXt5",
        "outputId": "3956f0d5-1e88-4109-9e0c-c70028a85853"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'instruction': 'Summarize the clinical record', 'input': 'Clinical Record Day 1\\n-------------------------------------------------\\nHospitalization days: Day 1\\nUnit: Emergency Room\\nSigns and symptoms: Patient, 45 years, female, presents with severe headache and blurred vision for 2 days. Reports nausea and vomiting.\\nPhysical examination: Blood pressure of 180/110 mmHg, heart rate of 90 bpm, respiratory rate of 18 rpm, O2 saturation of 98% in room air. Neurological examination shows no focal deficits.\\nTest results: Blood tests show elevated creatinine levels. Brain CT scan shows no abnormalities.\\nMedications: Patient on Metformin 500mg for diabetes.\\nPrevious diagnoses: Diabetes Mellitus Type 2 (DM2).\\nCurrent diagnostic hypotheses: Suspected hypertensive crisis.\\nTherapeutic plan: Initiation of antihypertensive therapy, monitoring of blood pressure, and reassessment of diabetes treatment.\\n\\nClinical Record Day 2\\n-------------------------------------------------\\nHospitalization days: Day 2\\nUnit: Intensive Care Unit\\nSigns and symptoms: Improvement in headache and vision, no nausea or vomiting.\\nPhysical examination: Blood pressure of 140/90 mmHg, heart rate of 85 bpm, respiratory rate of 18 rpm, O2 saturation of 98% in room air. Neurological examination shows no focal deficits.\\nTest results: Blood tests show normal creatinine levels.\\nMedications: Lisinopril 10mg, Metformin 500mg.\\nPrevious diagnoses: DM2. Hypertensive crisis.\\nCurrent diagnostic hypotheses: Stabilization of hypertensive crisis.\\nTherapeutic plan: Continuation of antihypertensive therapy, monitoring of blood pressure, and reassessment of diabetes treatment.\\n\\nClinical Record Day 3\\n-------------------------------------------------\\nHospitalization days: Day 3\\nUnit: Intensive Care Unit\\nSigns and symptoms: Asymptomatic.\\nPhysical examination: Blood pressure of 130/80 mmHg, heart rate of 80 bpm, respiratory rate of 18 rpm, O2 saturation of 98% in room air. Neurological examination shows no focal deficits.\\nTest results: Blood tests show normal creatinine levels.\\nMedications: Lisinopril 10mg, Metformin 500mg.\\nPrevious diagnoses: DM2. Hypertensive crisis.\\nCurrent diagnostic hypotheses: Recovery from hypertensive crisis.\\nTherapeutic plan: Discharge with referral for follow-up of hypertension and diabetes in primary care.', 'output': '45-year-old female patient admitted due to severe headache, blurred vision, nausea, and vomiting. She was suspected of having a hypertensive crisis and started on antihypertensive therapy. Her blood pressure improved on the second day and her headache and vision problems resolved. By the third day, she was asymptomatic with normal blood pressure and was discharged with a referral for follow-up of her hypertension and diabetes in primary care.'}, {'instruction': 'Identifique os medicamentos mencionados no texto', 'input': 'Paciente de 65 anos, com diagnóstico de hipertensão arterial sistêmica e diabetes mellitus tipo 2. Em uso de losartana 50mg/dia, metformina 500mg 2x/dia e sinvastatina 20mg/dia. Relata aderência ao tratamento e controle adequado das doenças.', 'output': 'Os medicamentos mencionados no texto são: Losartana, Metformina e Sinvastatina.'}, {'instruction': 'Crie um resumo do caso clínico apresentado', 'input': 'Paciente do sexo feminino, 45 anos, professora, comparece ao consultório com queixa de dor abdominal difusa há 3 meses. A dor é descrita como uma sensação de queimação, de intensidade moderada, que piora após as refeições e melhora com o uso de antiácidos. A paciente relata também episódios de azia e regurgitação, principalmente à noite. Nega febre, vômitos, alteração do hábito intestinal ou perda de peso. No exame físico, apresenta dor à palpação superficial do epigástrio, sem sinais de irritação peritoneal. O restante do exame físico é normal. A paciente tem histórico de gastrite diagnosticada há 5 anos e faz uso irregular de omeprazol.', 'output': 'Paciente do sexo feminino, 45 anos, professora, apresenta-se com dor abdominal difusa de 3 meses de duração, descrita como queimação, piora após as refeições e melhora com antiácidos. Relata também azia e regurgitação, principalmente à noite. Nega sintomas sistêmicos como febre, vômitos, alteração do hábito intestinal ou perda de peso. Exame físico revela dor à palpação superficial do epigástrio, sem sinais de irritação peritoneal. Histórico de gastrite diagnosticada há 5 anos e uso irregular de omeprazol.'}, {'instruction': 'Explique o que é a doença de Parkinson', 'input': '', 'output': 'A doença de Parkinson é um distúrbio neurológico crônico e progressivo que afeta principalmente a coordenação motora. É causada pela perda de células cerebrais que produzem dopamina, um neurotransmissor que ajuda a controlar os movimentos do corpo. Os sintomas mais comuns incluem tremores, rigidez muscular, lentidão de movimentos e problemas de equilíbrio. Embora não haja cura para a doença de Parkinson, existem tratamentos disponíveis para ajudar a controlar os sintomas.'}, {'instruction': 'Identifique o diagnóstico do paciente com base nos sintomas e exames', 'input': 'Paciente com dor abdominal, febre e leucocitose. Ultrassom abdominal mostra vesícula biliar com paredes espessadas e cálculos.', 'output': 'O diagnóstico do paciente é colecistite aguda.'}, {'instruction': 'Crie um resumo de alta hospitalar para um paciente com pneumonia', 'input': '', 'output': 'Paciente masculino, 65 anos, internado por 7 dias devido a pneumonia adquirida na comunidade. Recebeu tratamento com antibióticos intravenosos (ceftriaxona e azitromicina) com boa resposta clínica. Exames laboratoriais mostraram melhora dos parâmetros inflamatórios. Radiografia de tórax com redução das opacidades pulmonares. Paciente está afebril, sem queixas respiratórias e com boa tolerância à dieta oral. Receberá alta hospitalar com prescrição de azitromicina oral por mais 3 dias e orientações para retorno em caso de piora dos sintomas. Consulta de retorno agendada para daqui a 7 dias.'}, {'instruction': 'Crie um plano de tratamento para um paciente com diabetes tipo 2', 'input': '', 'output': '1. Educação do paciente: O paciente deve ser educado sobre a natureza crônica da diabetes tipo 2 e a importância do controle glicêmico para prevenir complicações.\\n\\n2. Dieta: Recomenda-se uma dieta balanceada, rica em fibras, com baixo teor de gorduras saturadas e açúcares simples. O paciente deve ser encaminhado para um nutricionista para orientação individualizada.\\n\\n3. Exercício: O paciente deve ser incentivado a realizar atividade física regular, como caminhada, pelo menos 30 minutos por dia, 5 dias por semana.\\n\\n4. Medicação: Iniciar metformina 500mg duas vezes ao dia, podendo ser ajustada de acordo com os níveis de glicemia. Se a metformina não for suficiente para controlar a glicemia, outros medicamentos podem ser adicionados.\\n\\n5. Monitoramento: O paciente deve monitorar a glicemia em casa e ter consultas regulares com o médico para ajustar o tratamento, se necessário.\\n\\n6. Controle de comorbidades: É importante controlar outras condições que podem estar presentes, como hipertensão e dislipidemia, que aumentam o risco de complicações da diabetes.\\n\\n7. Vacinação: O paciente deve estar atualizado com as vacinas, incluindo a vacina contra a gripe anualmente e a vacina pneumocócica.\\n\\n8. Cuidados com os pés: O paciente deve ser instruído sobre a importância do cuidado com os pés para prevenir úlceras e infecções.'}, {'instruction': 'Identifique os fatores de risco para doença cardiovascular presentes no histórico do paciente', 'input': 'Paciente do sexo masculino, 55 anos, fumante, com histórico de hipertensão arterial sistêmica, diabetes mellitus tipo 2 e dislipidemia. Pai faleceu de infarto agudo do miocárdio aos 60 anos.', 'output': 'Os fatores de risco para doença cardiovascular presentes no histórico do paciente incluem:\\n\\n1- Sexo masculino: Homens têm maior risco de desenvolver doenças cardiovasculares em comparação às mulheres.\\n\\n2- Idade: O risco de doença cardiovascular aumenta com a idade.\\n\\n3- Tabagismo: O fumo é um fator de risco significativo para doenças cardiovasculares, incluindo doença arterial coronariana.\\n\\n4- Hipertensão arterial sistêmica: A pressão arterial elevada pode danificar os vasos sanguíneos e o coração, aumentando o risco de doença cardiovascular.\\n\\n5- Diabetes mellitus tipo 2: O diabetes aumenta significativamente o risco de várias doenças cardiovasculares.\\n\\n6- Dislipidemia: Níveis elevados de lipídios no sangue podem levar ao acúmulo de placas nas artérias, aumentando o risco de doença cardiovascular.\\n\\n7- Histórico familiar: A história de infarto agudo do miocárdio do pai do paciente sugere uma predisposição genética para doença cardiovascular.'}, {'instruction': 'Identifique o diagnóstico do paciente', 'input': 'Paciente de 45 anos, sexo feminino, apresenta-se com dor abdominal intensa, náuseas e vômitos. A dor é localizada no quadrante superior direito do abdômen e irradia para as costas. A paciente relata que a dor piora após as refeições, especialmente após a ingestão de alimentos gordurosos. Exame físico revela sensibilidade no quadrante superior direito. Ultrassonografia abdominal mostra cálculos na vesícula biliar.', 'output': 'O diagnóstico do paciente é colecistite, causada por cálculos na vesícula biliar.'}, {'instruction': 'Escreva um resumo do histórico médico do paciente', 'input': 'Paciente de 45 anos, sexo masculino, com histórico de hipertensão arterial sistêmica e diabetes mellitus tipo 2. Faz uso regular de losartana e metformina. Não fuma, mas consome álcool socialmente. Tem histórico familiar de doença cardíaca. Apresenta-se à consulta com queixa de dor no peito e falta de ar.', 'output': 'O paciente é um homem de 45 anos com histórico médico significativo de hipertensão arterial sistêmica e diabetes mellitus tipo 2. Ele está em tratamento com losartana e metformina. O paciente não é fumante, mas consome álcool de forma social. Há um histórico familiar de doença cardíaca. Atualmente, ele está apresentando sintomas de dor no peito e falta de ar.'}, {'instruction': 'Escreva um plano de tratamento para o paciente com base em seu diagnóstico', 'input': 'Paciente com diagnóstico de hipertensão arterial e diabetes tipo 2.', 'output': 'O plano de tratamento para o paciente inclui o controle da pressão arterial e dos níveis de glicose no sangue. Isso pode ser alcançado através de uma combinação de medicamentos, dieta e exercícios. Os medicamentos podem incluir anti-hipertensivos e medicamentos para diabetes. A dieta deve ser baixa em sal e açúcar, e rica em frutas, vegetais e grãos integrais. O exercício regular também é importante, com pelo menos 30 minutos de atividade moderada na maioria dos dias da semana. Além disso, o paciente deve monitorar regularmente a pressão arterial e os níveis de glicose no sangue. Consultas regulares com o médico para monitorar a progressão da doença e ajustar o tratamento conforme necessário também são essenciais.'}, {'instruction': 'Escreva uma prescrição médica para o caso clínico', 'input': 'Paciente com diagnóstico de hipertensão arterial. Nega alergias medicamentosas.', 'output': 'Prescrição: Losartana 50mg, 1 comprimido ao dia, pela manhã. Acompanhamento regular da pressão arterial, retornar em 30 dias para reavaliação.'}, {'instruction': 'Identifique os medicamentos prescritos', 'input': 'Paciente em uso de Losartana 50mg para hipertensão. Foi prescrito Azitromicina 500mg e Ceftriaxone 2g para tratar pneumonia. No final da internação, o paciente estava tomando Enalapril 20mg.', 'output': 'Os medicamentos prescritos para o paciente foram Losartana 50mg, Azitromicina 500mg, Ceftriaxone 2g e Enalapril 20mg.'}, {'instruction': 'Explique o procedimento de uma colonoscopia', 'input': 'Colonoscopia', 'output': 'A colonoscopia é um procedimento médico que permite ao médico examinar o interior do cólon (intestino grosso). O procedimento é realizado com um instrumento chamado colonoscópio, que é um tubo longo e flexível com uma câmera na ponta que permite ao médico visualizar o interior do cólon.\\n\\nAntes do procedimento, o paciente deve seguir uma dieta líquida clara e tomar um laxante para limpar o cólon. No dia do procedimento, o paciente é geralmente sedado para ajudar a aliviar o desconforto.\\n\\nDurante a colonoscopia, o médico insere o colonoscópio no ânus e o guia suavemente pelo cólon. O médico pode inflar o cólon com ar para melhor visualização. Se o médico encontrar pólipos ou outras áreas suspeitas, ele pode remover uma amostra de tecido (biópsia) para análise posterior.\\n\\nApós o procedimento, o paciente pode sentir algum desconforto, como gases ou cólicas leves, mas geralmente pode retomar a dieta normal e as atividades diárias no mesmo dia. A colonoscopia é uma ferramenta importante para o diagnóstico e prevenção do câncer colorretal.'}, {'instruction': 'Crie um plano de tratamento para um paciente com diagnóstico de hipertensão arterial', 'input': '', 'output': 'Iniciar tratamento com anti-hipertensivo (Losartana 50mg 1x ao dia), orientar sobre a importância da adesão ao tratamento medicamentoso, orientar sobre a importância de uma dieta saudável, baixa em sal e rica em frutas, vegetais e grãos integrais, incentivar a prática regular de exercícios físicos, agendar retorno em 1 mês para reavaliação da pressão arterial e dos efeitos do tratamento.'}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def post_process_gpt4_response(num_prompt_instructions, response):\n",
        "    if response is None:\n",
        "        return []\n",
        "    raw_instructions = f\"Instruction:\" + response\n",
        "    raw_instructions = re.split(\"###\", raw_instructions)\n",
        "    instructions = []\n",
        "    for idx, inst in enumerate(raw_instructions):\n",
        "        idx += num_prompt_instructions + 1\n",
        "        splitted_data = re.split(f\"(Instruction|Input|Output):\", inst)\n",
        "        inst = splitted_data[4].strip()\n",
        "        input = splitted_data[6].strip()\n",
        "        input = \"\" if input.lower() == \"<noinput>\" else input\n",
        "        output = splitted_data[8].strip()\n",
        "        # filter based on keywords that are not suitable for language models.\n",
        "        blacklist = [\n",
        "            \"imagem\",\n",
        "            \"imagens\",\n",
        "            \"gráfico\",\n",
        "            \"gráficos\",\n",
        "            \"foto\",\n",
        "            \"fotos\",\n",
        "            \"arquivo\",\n",
        "            \"arquivos\",\n",
        "            \"mapa\",\n",
        "            \"mapas\",\n",
        "            \"desenhar\",\n",
        "            \"parcela\",\n",
        "            \"ir para\",\n",
        "            \"vídeo\",\n",
        "            \"áudio\",\n",
        "            \"música\",\n",
        "            \"fluxograma\",\n",
        "            \"diagrama\",\n",
        "        ]\n",
        "\n",
        "        blacklist += []\n",
        "        if any(find_word_in_string(word, inst) for word in blacklist):\n",
        "            continue\n",
        "        # We found that the model tends to add \"write a program\" to some existing instructions, which lead to a lot of such instructions.\n",
        "        # And it's a bit comfusing whether the model need to write a program or directly output the result.\n",
        "        # Here we filter them out.\n",
        "        # Note this is not a comprehensive filtering for all programming instructions.\n",
        "        if inst.startswith(\"Write a program\"):\n",
        "            continue\n",
        "        instructions.append({\"instruction\": inst, \"input\": input, \"output\": output})\n",
        "    return instructions\n",
        "\n",
        "def _make_w_io_base(f, mode: str):\n",
        "    if not isinstance(f, io.IOBase):\n",
        "        f_dirname = os.path.dirname(f)\n",
        "        if f_dirname != \"\":\n",
        "            os.makedirs(f_dirname, exist_ok=True)\n",
        "        f = open(f, mode=mode)\n",
        "    return f\n",
        "\n",
        "def _make_r_io_base(f, mode: str):\n",
        "    if not isinstance(f, io.IOBase):\n",
        "        f = open(f, mode=mode)\n",
        "    return f\n",
        "\n",
        "def jdump(obj, f, mode=\"w\", indent=4, default=str):\n",
        "    \"\"\"Dump a str or dictionary to a file in json format.\n",
        "\n",
        "    Args:\n",
        "        obj: An object to be written.\n",
        "        f: A string path to the location on disk.\n",
        "        mode: Mode for opening the file.\n",
        "        indent: Indent for storing json dictionaries.\n",
        "        default: A function to handle non-serializable entries; defaults to `str`.\n",
        "    \"\"\"\n",
        "    f = _make_w_io_base(f, mode)\n",
        "    if isinstance(obj, (dict, list)):\n",
        "        json.dump(obj, f, indent=indent, default=default)\n",
        "    elif isinstance(obj, str):\n",
        "        f.write(obj)\n",
        "    else:\n",
        "        raise ValueError(f\"Unexpected type: {type(obj)}\")\n",
        "    f.close()\n",
        "\n",
        "def jload(f, mode=\"r\"):\n",
        "    \"\"\"Load a .json file into a dictionary.\"\"\"\n",
        "    f = _make_r_io_base(f, mode)\n",
        "    jdict = json.load(f)\n",
        "    f.close()\n",
        "    return jdict\n",
        "\n",
        "def find_word_in_string(w, s):\n",
        "    return re.compile(r\"\\b({0})\\b\".format(w), flags=re.IGNORECASE).search(s)\n",
        "\n",
        "\n",
        "def generate_instruction_following_data(\n",
        "    api_key: str,\n",
        "    output_dir=\"./\",\n",
        "    seed_tasks_path=\"./seed_tasks.jsonl\",\n",
        "    num_instructions_to_generate=100,\n",
        "    model_name=\"gpt-4\",\n",
        "    num_prompt_instructions=3,\n",
        "    request_batch_size=5,\n",
        "    temperature=0.1,\n",
        "    max_tokens=2000,\n",
        "    frequency_penalty=0,\n",
        "    presence_penalty=0,\n",
        "    top_p=0.75,\n",
        "    num_cpus=16,\n",
        "):\n",
        "\n",
        "    prompt_tmp_txt = open(\"/content/prompt_template.txt\").read() + \"\\n\"\n",
        "\n",
        "    evidence_prompt_instruction = ''\n",
        "\n",
        "    # Initialize the OpenAI model\n",
        "    model = ChatOpenAI(\n",
        "        openai_api_key=api_key,\n",
        "        model_name=model_name,\n",
        "        temperature=temperature,\n",
        "        max_tokens=max_tokens,\n",
        "        frequency_penalty=frequency_penalty,\n",
        "        presence_penalty=presence_penalty,\n",
        "        top_p=top_p,\n",
        "        request_timeout=180\n",
        "    )\n",
        "\n",
        "    prompt_template = PromptTemplate(template=prompt_tmp_txt, input_variables=[\"text\"])\n",
        "    llm_chain = LLMChain(prompt=prompt_template, llm=model)\n",
        "\n",
        "    while len(machine_instruction_data) < num_instructions_to_generate:\n",
        "        request_idx += 1\n",
        "\n",
        "        results = []\n",
        "        request_start = time.time()\n",
        "\n",
        "        for _ in range(request_batch_size):\n",
        "            # only sampling from the seed tasks\n",
        "            prompt_instructions = random.sample(seed_instruction_data, num_prompt_instructions)\n",
        "            prompt = encode_prompt(prompt_instructions)\n",
        "            # print(prompt)\n",
        "            result = llm_chain.predict(input=prompt)\n",
        "            print(f'===Result===\\n\\n{result}\\n\\n')\n",
        "            results.append(result)\n",
        "\n",
        "        request_duration = time.time() - request_start\n",
        "\n",
        "        process_start = time.time()\n",
        "        instruction_data = []\n",
        "        for result in results:\n",
        "            new_instructions = post_process_gpt4_response(num_prompt_instructions, result)\n",
        "            instruction_data += new_instructions\n",
        "\n",
        "        total = len(instruction_data)\n",
        "        keep = 0\n",
        "        for instruction_data_entry in instruction_data:\n",
        "            # computing similarity with the pre-tokenzied instructions\n",
        "            new_instruction_tokens = scorer._tokenizer.tokenize(instruction_data_entry[\"instruction\"])\n",
        "            with Pool(num_cpus) as p:\n",
        "                rouge_scores = p.map(\n",
        "                    partial(rouge_scorer._score_lcs, new_instruction_tokens),\n",
        "                    all_instruction_tokens,\n",
        "                )\n",
        "            rouge_scores = [score.fmeasure for score in rouge_scores]\n",
        "            most_similar_instructions = {\n",
        "                all_instructions[i]: rouge_scores[i] for i in np.argsort(rouge_scores)[-10:][::-1]\n",
        "            }\n",
        "            instruction_data_entry[\"most_similar_instructions\"] = most_similar_instructions\n",
        "            instruction_data_entry[\"avg_similarity_score\"] = float(np.mean(rouge_scores))\n",
        "            machine_instruction_data.append(instruction_data_entry)\n",
        "            all_instructions.append(instruction_data_entry[\"instruction\"])\n",
        "            all_instruction_tokens.append(new_instruction_tokens)\n",
        "            progress_bar.update(1)\n",
        "            keep+=1\n",
        "        process_duration = time.time() - process_start\n",
        "        print(f\"Request {request_idx} took {request_duration:.2f}s, processing took {process_duration:.2f}s\")\n",
        "        print(f\"Generated {total} instructions, kept {keep} instructions\")\n",
        "        jdump(machine_instruction_data, os.path.join(output_dir, \"regen4.json\"))\n"
      ],
      "metadata": {
        "id": "lIKuIRulP-qR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# File path\n",
        "file_path = '/mnt/data/data.jsonl'\n",
        "\n",
        "# Read and process the file\n",
        "formatted_lines = []\n",
        "with open(file_path, 'r') as file:\n",
        "    for line in file:\n",
        "        # Load the JSON data from the line\n",
        "        data = json.loads(line)\n",
        "\n",
        "        # Extract prompt and completion\n",
        "        prompt = data[\"prompt\"]\n",
        "        completion = data[\"completion\"]\n",
        "\n",
        "        # Remove the leading \"### Questão:\" and \"### Resposta:\" from prompt and completion\n",
        "        prompt = prompt.replace(\"### Questão:\\n\", \"\").strip()\n",
        "        completion = completion.replace(\"### Resposta:\\n\", \"\").strip()\n",
        "\n",
        "        # Format the text into the specified template\n",
        "        formatted_text = (\n",
        "            f\"Considere a questão de prova de residência médica:\\n\\n\"\n",
        "            f\"\\\"\\\"\\\"\\n{text}\\n\\\"\\\"\\\"\\n\\n\"\n",
        "            f\"Por favor, escreva uma \\\"Resposta\\\" para a questão de forma a consolidar as informações das alternativas.\\n\"\n",
        "            f\"Mantenha a linguagem técnica.\\n\"\n",
        "            f\"Não mencione a questão nem as alternativas.\\n\"\n",
        "            f\"Utilize formato markdown para responder.\\n\\n\"\n",
        "            f\"\\\"\\\"\\\"\\n### Resposta:\\n\\n- {completion}\\n\\\"\\\"\\\"\"\n",
        "        )\n",
        "\n",
        "        # Add the formatted text to the list\n",
        "        formatted_lines.append({\"text\": formatted_text})\n",
        "\n",
        "# Example of the first formatted line (for verification)\n",
        "formatted_lines[0] if formatted_lines else \"No data processed\"\n"
      ],
      "metadata": {
        "id": "l5XFBQ3yG0Wv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# File path\n",
        "file_path = '/mnt/data/data.jsonl'\n",
        "\n",
        "# Read and process the file\n",
        "formatted_lines = []\n",
        "with open(file_path, 'r') as file:\n",
        "    for line in file:\n",
        "        # Load the JSON data from the line\n",
        "        data = json.loads(line)\n",
        "\n",
        "        # Extract prompt and completion\n",
        "        prompt = data[\"prompt\"]\n",
        "        completion = data[\"completion\"]\n",
        "\n",
        "        # Remove the leading \"### Questão:\" and \"### Resposta:\" from prompt and completion\n",
        "        prompt = prompt.replace(\"### Questão:\\n\", \"\").strip()\n",
        "        completion = completion.replace(\"### Resposta:\\n\", \"\").strip()\n",
        "\n",
        "        # Format the text into the specified template\n",
        "        formatted_text = (\n",
        "            f\"Considere a questão de prova de residência médica:\\n\\n\"\n",
        "            f\"\\\"\\\"\\\"\\n{text}\\n\\\"\\\"\\\"\\n\\n\"\n",
        "            f\"Por favor, escreva uma \\\"Resposta\\\" para a questão de forma a consolidar as informações das alternativas.\\n\"\n",
        "            f\"Mantenha a linguagem técnica.\\n\"\n",
        "            f\"Não mencione a questão nem as alternativas.\\n\"\n",
        "            f\"Utilize formato markdown para responder.\\n\\n\"\n",
        "            f\"\\\"\\\"\\\"\\n### Resposta:\\n\\n- {completion}\\n\\\"\\\"\\\"\"\n",
        "        )\n",
        "\n",
        "        # Add the formatted text to the list\n",
        "        formatted_lines.append({\"text\": formatted_text})\n",
        "\n",
        "# Example of the first formatted line (for verification)\n",
        "formatted_lines[0] if formatted_lines else \"No data processed\"\n"
      ],
      "metadata": {
        "id": "A0DvGOBFG3Ua"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}