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   "cell_type": "markdown",
   "id": "a2b4e2f0-0850-4930-b9e6-1eb66a35e8a1",
   "metadata": {},
   "source": [
    "# OpenAI Price Calculator\n",
    "\n",
    "[OpenAI API](https://platform.openai.com/) の価格を計算するための Notebook です。正確なトークン数を得るために、公式の Tokenizer である [tiktoken](https://github.com/openai/tiktoken) を使用します。 API の単価は変わるため、 [Pricing](https://openai.com/pricing) で最新の単価をご確認ください。本ノートブックの計算結果は入力値に依存するため、自らの責任でもってご利用ください。\n",
    "\n",
    "Notebook を動かす前に、 OpenAI API の API KEY と Organization ID の確認が必要です。いずれも Manage Account のメニューから確認できます。取得した API KEY と Organization ID は `os.environ` などを使い事前に設定しておいてください。これらは機微な情報のため、 GitHub などに誤ってコミットしないよう注意してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0fcac1e-2d71-4915-b3dc-19debdfd7e59",
   "metadata": {
    "tags": []
   },
   "source": [
    "!pip install tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e85b8404-4a2d-4254-895e-5c4e916b6b06",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "JAQKET_TRAIN_DATASET = (\n",
    "    \"https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_02/aio_02_train.jsonl\"\n",
    ")\n",
    "JAQKET_DEV_DATASET = (\n",
    "    \"https://jaqket.s3.ap-northeast-1.amazonaws.com/data/aio_02/aio_02_dev_v1.0.jsonl\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b54038d4-32df-471d-b758-6eb3bffd8b30",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def read_jaqket_dataset(dataset_url: str) -> pd.DataFrame:\n",
    "    file_name = os.path.basename(dataset_url)\n",
    "    location = os.path.join(f\"data/{file_name}\")\n",
    "    if not os.path.exists(location):\n",
    "        response = requests.get(dataset_url)\n",
    "        with open(location, mode=\"wb\") as f:\n",
    "            f.write(response.content)\n",
    "\n",
    "    return pd.read_json(location, lines=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f9839fc-eda0-47df-a1f6-eeb4deaf8150",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train = read_jaqket_dataset(JAQKET_TRAIN_DATASET)\n",
    "df_dev = read_jaqket_dataset(JAQKET_DEV_DATASET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdf69e21-4e37-41cf-b548-e06a96a1350b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_train.question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85a5cce3-8441-490f-b80b-bae33dbffa7b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tiktoken\n",
    "\n",
    "\n",
    "def calculate_price(model_name: str, input_unit_price: float, output_unit_price: float,\n",
    "                    quiz_df: pd.DataFrame) -> dict:\n",
    "    tokenizer = tiktoken.encoding_for_model(model_name)\n",
    "    num_input_tokens = quiz_df.question.map(lambda q: len(tokenizer.encode(q))).sum()\n",
    "    num_output_tokens = quiz_df.answers.map(lambda a: len(tokenizer.encode(a[0]))).sum()\n",
    "    input_price = num_input_tokens / 1000 * input_unit_price\n",
    "    output_price = num_output_tokens / 1000 * output_unit_price\n",
    "    result = {\n",
    "        \"model_name\": model_name,\n",
    "        \"num_input_tokens\": num_input_tokens,\n",
    "        \"num_output_tokens\": num_output_tokens,\n",
    "        \"input_price\": input_price,\n",
    "        \"output_price\": output_price,\n",
    "        \"total\": input_price + output_price\n",
    "    }\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7766b019-f5ae-4dcc-8f9f-04ebfe73f570",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "calculate_price(\"text-davinci-003\", 0.0200, 0.0200, df_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "966a2de0-0513-4af6-9bc0-430117123209",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "calculate_price(\"gpt-3.5-turbo\", 0.0015, 0.002, df_dev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8947c4cb-c39f-43fe-9b7c-465b04c8d1da",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "calculate_price(\"curie\", 0.003, 0.003, df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c978956-c9f0-4627-acfb-d1c9506201b5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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