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"import os\n",
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"import openai\n",
"def call(context):\n",
" try:\n",
" response = openai.Completion.create(\n",
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"cell_type": "code",
"execution_count": 36,
"metadata": {
"id": "XOGiyCQb-QoM"
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"outputs": [],
"source": [
"data = ''' \n",
"Preference\t Red_Wine\t White_Wine\t Recommendation\n",
"Red\t Light-Bodied\t\tNone Pinot Noir\n",
"Red\t Full-Bodied\t\t None Shiraz or Zinfandel\n",
"White\t\t None Dry Sauvignon Blanc\n",
"White\t\t None None Sweet\tGewurztraminer\n",
"Red-Fruity\t None\t\t None Pinot Noir\n",
"Red-Earthy\t None\t\t None Chianti\n",
"White-Crisp\t None\t\t None Sauvignon Blanc\n",
"White-Creamy None\t\t\t None Chardonnay\n",
"Red-Spicy\t\t None\t None Shiraz or Zinfandel\n",
"Red-Rich\t\t None \t None Cabernet Sauvignon\n",
"White-Floral None\t\t\t None Gewurztraminer\n",
"White-Citrus None\t\t\t None Riesling\n",
"Red\t\t\t None None Pinot Noir\n",
"Red\t\t\t None None Chianti\n",
"White\t\t\t None None Sauvignon Blanc\n",
"White\t\t\t None None Chardonnay\n",
"Red\t\t\t None None Shiraz or Zinfandel\n",
"Red\t\t\t None None Cabernet Sauvignon\n",
"White\t\t\t None None Gewurztraminer\n",
"White\t\t\t None None Riesling\n",
"Red-Fruity\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Fruity\t Full-Bodied\t\t None Shiraz or Zinfandel\n",
"Red-Earthy\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Earthy\t Full-Bodied\t\t None Cabernet Sauvignon\n",
"White-Crisp\t Dry\t\t None Sauvignon Blanc\n",
"White-Crisp\t Sweet\t\t None Pinot Noir\n",
"White-Creamy Dry\t\t None Pinot Noir\n",
"White-Creamy Sweet\t\t None Chardonnay\n",
"Red-Spicy\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Spicy\t Full-Bodied\t\t None Shiraz or Zinfandel\n",
"Red-Rich\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Rich\t Full-Bodied\t\t None Cabernet Sauvignon\n",
"White-Floral\tDry\t\t None Pinot Noir\n",
"White-Floral\tSweet\t\t None Gewurztraminer\n",
"White-Citrus\tDry\t\t None Sauvignon Blanc\n",
"White-Citrus\tSweet\t\t None Riesling\n",
"Red-Fruity\t\tNone\t Sweet Pinot Noir\n",
"Red-Fruity\t\tNone\t Dry Pinot Noir\n",
"Red-Earthy\t\tNone\t Sweet Chianti\n",
"Red-Earthy\t\tNone\t Dry Pinot Noir\n",
"\n",
"\n",
"Based on the data generate 10 different choices and recommended based on the choices\n",
"'''\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NBVKb7ZOBTtg",
"outputId": "98da1540-213a-4221-c866-ca0f880aaf05"
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"1. Pinot Noir (Red-Fruity, Light-Bodied, Sweet) \n",
"2. Sauvignon Blanc (White-Crisp, Dry)\n",
"3. Chardonnay (White-Creamy, Sweet)\n",
"4. Chianti (Red-Earthy, Sweet) \n",
"5. Riesling (White-Citrus, Sweet)\n",
"6. Shiraz or Zinfandel (Red-Spicy, Full-Bodied)\n",
"7. Cabernet Sauvignon (Red-Rich, Full-Bodied)\n",
"8. Gewurztraminer (White-Floral, Sweet) \n",
"9. Pinot Noir (Red-Earthy, Light-Bodied)\n",
"10. Pinot Noir (Red-Fruity, Dry)\n"
]
}
],
"source": [
"print(call(data))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"id": "hvcX7UWqBblL"
},
"outputs": [],
"source": [
"data1 = '''\n",
"Preference\t Red_Wine\t White_Wine\t Recommendation\n",
"Red\t Light-Bodied\t\tNone Pinot Noir\n",
"Red\t Full-Bodied\t\t None Shiraz or Zinfandel\n",
"White\t\t None Dry Sauvignon Blanc\n",
"White\t\t None None Sweet\tGewurztraminer\n",
"Red-Fruity\t None\t\t None Pinot Noir\n",
"Red-Earthy\t None\t\t None Chianti\n",
"White-Crisp\t None\t\t None Sauvignon Blanc\n",
"White-Creamy None\t\t\t None Chardonnay\n",
"Red-Spicy\t\t None\t None Shiraz or Zinfandel\n",
"Red-Rich\t\t None \t None Cabernet Sauvignon\n",
"White-Floral None\t\t\t None Gewurztraminer\n",
"White-Citrus None\t\t\t None Riesling\n",
"Red\t\t\t None None Pinot Noir\n",
"Red\t\t\t None None Chianti\n",
"White\t\t\t None None Sauvignon Blanc\n",
"White\t\t\t None None Chardonnay\n",
"Red\t\t\t None None Shiraz or Zinfandel\n",
"Red\t\t\t None None Cabernet Sauvignon\n",
"White\t\t\t None None Gewurztraminer\n",
"White\t\t\t None None Riesling\n",
"Red-Fruity\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Fruity\t Full-Bodied\t\t None Shiraz or Zinfandel\n",
"Red-Earthy\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Earthy\t Full-Bodied\t\t None Cabernet Sauvignon\n",
"White-Crisp\t Dry\t\t None Sauvignon Blanc\n",
"White-Crisp\t Sweet\t\t None Pinot Noir\n",
"White-Creamy Dry\t\t None Pinot Noir\n",
"White-Creamy Sweet\t\t None Chardonnay\n",
"Red-Spicy\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Spicy\t Full-Bodied\t\t None Shiraz or Zinfandel\n",
"Red-Rich\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Rich\t Full-Bodied\t\t None Cabernet Sauvignon\n",
"White-Floral\tDry\t\t None Pinot Noir\n",
"White-Floral\tSweet\t\t None Gewurztraminer\n",
"White-Citrus\tDry\t\t None Sauvignon Blanc\n",
"White-Citrus\tSweet\t\t None Riesling\n",
"Red-Fruity\t\tNone\t Sweet Pinot Noir\n",
"Red-Fruity\t\tNone\t Dry Pinot Noir\n",
"Red-Earthy\t\tNone\t Sweet Chianti\n",
"Red-Earthy\t\tNone\t Dry Pinot Noir\n",
"\n",
"\n",
"Customer 1: Red,Dry?\n",
"Customer 2: White?\n",
"Customer 3: White-Earthry?\n",
"Customer 4: White-Floral,Light-Bodied?\n",
"Customer 5: Red-Spicy,Dry?\n",
"Customer 6: Red-Rich,Full-Bodied?\n",
"Customer 7: White-Creamy?\n",
"Customer 8: White-Crisp,Light-Bodied?\n",
"Customer 9: White-Crisp,Full-Bodied?\n",
"Customer 10: White-Crisp,Dry?\n",
"'''\n",
"result = call(data1)"
]
},
{
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"height": 54
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"'\\nRecommendation\\nCustomer 1: Pinot Noir\\nCustomer 2: Sauvignon Blanc\\nCustomer 3: Pinot Noir\\nCustomer 4: Pinot Noir \\nCustomer 5: Shiraz or Zinfandel\\nCustomer 6: Cabernet Sauvignon\\nCustomer 7: Chardonnay\\nCustomer 8: Sauvignon Blanc\\nCustomer 9: Pinot Noir\\nCustomer 10: Sauvignon Blanc'"
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"metadata": {},
"execution_count": 41
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{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"id": "WQ0o-f8ZDorN"
},
"outputs": [],
"source": [
"import re\n",
"results = re.split('Customer |[0-9]|:|\\n|Recommendation',result)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"id": "D1NJZE5zHcHE"
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"outputs": [],
"source": [
"fin_res = [res for res in results if len(res)>0]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"id": "GSW3EX85DraE"
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"outputs": [],
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"data = {\n",
" 'Query':['Red,Dry?','White?','White-Earthry?','White-Floral,Light-Bodied?','Red-Spicy,Dry?','Red-Rich,Full-Bodied?','White-Creamy?','White-Crisp,Light-Bodied?','White-Crisp,Full-Bodied?','White-Crisp,Dry?'],\n",
" 'Recommendation': fin_res\n",
"}"
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"{'Query': ['Red,Dry?',\n",
" 'White?',\n",
" 'White-Earthry?',\n",
" 'White-Floral,Light-Bodied?',\n",
" 'Red-Spicy,Dry?',\n",
" 'Red-Rich,Full-Bodied?',\n",
" 'White-Creamy?',\n",
" 'White-Crisp,Light-Bodied?',\n",
" 'White-Crisp,Full-Bodied?',\n",
" 'White-Crisp,Dry?'],\n",
" 'Recommendation': [' Pinot Noir',\n",
" ' Sauvignon Blanc',\n",
" ' Pinot Noir',\n",
" ' Pinot Noir ',\n",
" ' Shiraz or Zinfandel',\n",
" ' Cabernet Sauvignon',\n",
" ' Chardonnay',\n",
" ' Sauvignon Blanc',\n",
" ' Pinot Noir',\n",
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" Query Recommendation\n",
"0 Red,Dry? Pinot Noir\n",
"1 White? Sauvignon Blanc\n",
"2 White-Earthry? Pinot Noir\n",
"3 White-Floral,Light-Bodied? Pinot Noir \n",
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" }\n",
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" const buttonEl =\n",
" document.querySelector('#df-bec9c883-0031-471d-8d7c-d9712f409ff6 button.colab-df-convert');\n",
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"\n",
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" const element = document.querySelector('#df-bec9c883-0031-471d-8d7c-d9712f409ff6');\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",
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" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 46
}
],
"source": [
"import pandas as pd\n",
"df = pd.DataFrame(data)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"id": "hZJksWOkH6VS"
},
"outputs": [],
"source": [
"data_4 = ''' \n",
"Preference\t Red_Wine\t White_Wine\t Recommendation\n",
"Red\t Light-Bodied\t\tNone Pinot Noir\n",
"Red\t Full-Bodied\t\t None Shiraz or Zinfandel\n",
"White\t\t None Dry Sauvignon Blanc\n",
"White\t\t None None Sweet\tGewurztraminer\n",
"Red-Fruity\t None\t\t None Pinot Noir\n",
"Red-Earthy\t None\t\t None Chianti\n",
"White-Crisp\t None\t\t None Sauvignon Blanc\n",
"White-Creamy None\t\t\t None Chardonnay\n",
"Red-Spicy\t\t None\t None Shiraz or Zinfandel\n",
"Red-Rich\t\t None \t None Cabernet Sauvignon\n",
"White-Floral None\t\t\t None Gewurztraminer\n",
"White-Citrus None\t\t\t None Riesling\n",
"Red\t\t\t None None Pinot Noir\n",
"Red\t\t\t None None Chianti\n",
"White\t\t\t None None Sauvignon Blanc\n",
"White\t\t\t None None Chardonnay\n",
"Red\t\t\t None None Shiraz or Zinfandel\n",
"Red\t\t\t None None Cabernet Sauvignon\n",
"White\t\t\t None None Gewurztraminer\n",
"White\t\t\t None None Riesling\n",
"Red-Fruity\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Fruity\t Full-Bodied\t\t None Shiraz or Zinfandel\n",
"Red-Earthy\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Earthy\t Full-Bodied\t\t None Cabernet Sauvignon\n",
"White-Crisp\t Dry\t\t None Sauvignon Blanc\n",
"White-Crisp\t Sweet\t\t None Pinot Noir\n",
"White-Creamy Dry\t\t None Pinot Noir\n",
"White-Creamy Sweet\t\t None Chardonnay\n",
"Red-Spicy\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Spicy\t Full-Bodied\t\t None Shiraz or Zinfandel\n",
"Red-Rich\t Light-Bodied\t\tNone Pinot Noir\n",
"Red-Rich\t Full-Bodied\t\t None Cabernet Sauvignon\n",
"White-Floral\tDry\t\t None Pinot Noir\n",
"White-Floral\tSweet\t\t None Gewurztraminer\n",
"White-Citrus\tDry\t\t None Sauvignon Blanc\n",
"White-Citrus\tSweet\t\t None Riesling\n",
"Red-Fruity\t\tNone\t Sweet Pinot Noir\n",
"Red-Fruity\t\tNone\t Dry Pinot Noir\n",
"Red-Earthy\t\tNone\t Sweet Chianti\n",
"Red-Earthy\t\tNone\t Dry Pinot Noir\n",
"\n",
"\n",
"Based on the data generate 20 different choices and recommended based on the choices\n",
"'''\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"id": "VF7kwoFuYIT4"
},
"outputs": [],
"source": [
"Questions = call(data_4)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 109
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"id": "zSwiyj-QYNYo",
"outputId": "042d5f15-2dc9-4856-e2bc-2e59b43643a9"
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'\\n1. Pinot Noir (Red-Fruity, Dry)\\n2. Shiraz or Zinfandel (Red-Fruity, Full-Bodied)\\n3. Sauvignon Blanc (White-Crisp, Dry)\\n4. Sweet Gewurztraminer (White, None)\\n5. Chianti (Red-Earthy, None)\\n6. Chardonnay (White-Creamy, Sweet)\\n7. Cabernet Sauvignon (Red-Rich, Full-Bodied)\\n8. Pinot Noir (Red, Light-Bodied)\\n9. Shiraz or Zinfandel (Red-Spicy, Light-Bodied)\\n10. Pinot Noir (Red, None)\\n11. Chianti (Red, None)\\n12. Sauvignon Blanc (White, None)\\n13. Chardonnay (White, None)\\n14. Shiraz or Zinfandel (Red, None)\\n15. Cabernet Sauvignon (Red, None)\\n16. Gewurztraminer (White-Floral, Sweet)\\n17. Riesling (White-Citrus, Sweet)\\n18. Pinot Noir (Red-Earthy, Light-Bodied)\\n19. Cabernet Sauvignon (Red-Rich, Light-Bodied)\\n20. Pinot Noir (White-Crisp, Sweet)'"
],
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"type": "string"
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"metadata": {},
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"source": [
"Questions"
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},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"id": "YaP0OU8MYtJ4"
},
"outputs": [],
"source": [
"temp = re.split('[0-9]|\\n|\\.',Questions)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pGeywgUbZC1w",
"outputId": "6d3fff60-6c31-4a24-d50b-7f7e8c9a1a54"
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{
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"data": {
"text/plain": [
"[' Pinot Noir (Red-Fruity, Dry)',\n",
" ' Shiraz or Zinfandel (Red-Fruity, Full-Bodied)',\n",
" ' Sauvignon Blanc (White-Crisp, Dry)',\n",
" ' Sweet Gewurztraminer (White, None)',\n",
" ' Chianti (Red-Earthy, None)',\n",
" ' Chardonnay (White-Creamy, Sweet)',\n",
" ' Cabernet Sauvignon (Red-Rich, Full-Bodied)',\n",
" ' Pinot Noir (Red, Light-Bodied)',\n",
" ' Shiraz or Zinfandel (Red-Spicy, Light-Bodied)',\n",
" ' Pinot Noir (Red, None)',\n",
" ' Chianti (Red, None)',\n",
" ' Sauvignon Blanc (White, None)',\n",
" ' Chardonnay (White, None)',\n",
" ' Shiraz or Zinfandel (Red, None)',\n",
" ' Cabernet Sauvignon (Red, None)',\n",
" ' Gewurztraminer (White-Floral, Sweet)',\n",
" ' Riesling (White-Citrus, Sweet)',\n",
" ' Pinot Noir (Red-Earthy, Light-Bodied)',\n",
" ' Cabernet Sauvignon (Red-Rich, Light-Bodied)',\n",
" ' Pinot Noir (White-Crisp, Sweet)']"
]
},
"metadata": {},
"execution_count": 52
}
],
"source": [
"QA = [tem for tem in temp if len(tem)>0]\n",
"QA"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"id": "LtK-srHkaHU4"
},
"outputs": [],
"source": [
"ques = []\n",
"ans = []\n",
"\n",
"for item in QA:\n",
" a = item\n",
" b = re.split('[()]|\\|',a)\n",
" ans.append(b[0])\n",
" j = 0\n",
" str_temp = \"(\"\n",
" for i in b:\n",
" if j==0:\n",
" j = -1\n",
" continue\n",
" elif len(i):\n",
" str_temp += i\n",
" str_temp += \"|\"\n",
"\n",
" str_temp = str_temp.rstrip(\"|\")\n",
" str_temp += \")\" \n",
" ques.append(str_temp)\n",
" str_temp = \"\"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KpOLQ105an9v",
"outputId": "e3f94de1-e03b-48ae-fc9e-809221f0b111"
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['(Red-Fruity, Dry)',\n",
" '(Red-Fruity, Full-Bodied)',\n",
" '(White-Crisp, Dry)',\n",
" '(White, None)',\n",
" '(Red-Earthy, None)',\n",
" '(White-Creamy, Sweet)',\n",
" '(Red-Rich, Full-Bodied)',\n",
" '(Red, Light-Bodied)',\n",
" '(Red-Spicy, Light-Bodied)',\n",
" '(Red, None)',\n",
" '(Red, None)',\n",
" '(White, None)',\n",
" '(White, None)',\n",
" '(Red, None)',\n",
" '(Red, None)',\n",
" '(White-Floral, Sweet)',\n",
" '(White-Citrus, Sweet)',\n",
" '(Red-Earthy, Light-Bodied)',\n",
" '(Red-Rich, Light-Bodied)',\n",
" '(White-Crisp, Sweet)']"
]
},
"metadata": {},
"execution_count": 54
}
],
"source": [
"ques"
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{
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"id": "SBRw37z7cQ9I",
"outputId": "78c414b2-397f-41f6-8cb4-793bd72eed7f"
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[' Pinot Noir ',\n",
" ' Shiraz or Zinfandel ',\n",
" ' Sauvignon Blanc ',\n",
" ' Sweet Gewurztraminer ',\n",
" ' Chianti ',\n",
" ' Chardonnay ',\n",
" ' Cabernet Sauvignon ',\n",
" ' Pinot Noir ',\n",
" ' Shiraz or Zinfandel ',\n",
" ' Pinot Noir ',\n",
" ' Chianti ',\n",
" ' Sauvignon Blanc ',\n",
" ' Chardonnay ',\n",
" ' Shiraz or Zinfandel ',\n",
" ' Cabernet Sauvignon ',\n",
" ' Gewurztraminer ',\n",
" ' Riesling ',\n",
" ' Pinot Noir ',\n",
" ' Cabernet Sauvignon ',\n",
" ' Pinot Noir ']"
]
},
"metadata": {},
"execution_count": 55
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],
"source": [
"ans"
]
},
{
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" Question Answer\n",
"0 (Red-Fruity, Dry) Pinot Noir \n",
"1 (Red-Fruity, Full-Bodied) Shiraz or Zinfandel \n",
"2 (White-Crisp, Dry) Sauvignon Blanc \n",
"3 (White, None) Sweet Gewurztraminer \n",
"4 (Red-Earthy, None) Chianti "
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]
},
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" Question Answer\n",
"0 (Red-Fruity, Dry) Pinot Noir \n",
"1 (Red-Fruity, Full-Bodied) Shiraz or Zinfandel \n",
"2 (White-Crisp, Dry) Sauvignon Blanc \n",
"3 (White, None) Sweet Gewurztraminer \n",
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" <th>12</th>\n",
" <td>(White, None)</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>(Red, None)</td>\n",
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" <th>19</th>\n",
" <td>(White-Crisp, Sweet)</td>\n",
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{
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"id": "C02VOrRdsJF1"
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"execution_count": 72,
"outputs": []
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{
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" Question Answer\n",
"0 (Red-Fruity, Dry) Pinot Noir \n",
"1 (Red-Fruity, Full-Bodied) Shiraz or Zinfandel \n",
"2 (White-Crisp, Dry) Sauvignon Blanc \n",
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{
"cell_type": "code",
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{
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{
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" Question Answer\n",
"0 (Red-Fruity, Dry) Pinot Noir \n",
"1 (Red-Fruity, Full-Bodied) Shiraz or Zinfandel \n",
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"5 (White-Creamy, Sweet) Chardonnay \n",
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"10 (Red, None) Chianti \n",
"11 (White, None) Sauvignon Blanc \n",
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"13 (Red, None) Shiraz or Zinfandel \n",
"14 (Red, None) Cabernet Sauvignon \n",
"15 (White-Floral, Sweet) Gewurztraminer \n",
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"17 (Red-Earthy, Light-Bodied) Pinot Noir \n",
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" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
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" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-f5f06bd4-4a09-41df-8eaa-9d27ae1f1aae 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-f5f06bd4-4a09-41df-8eaa-9d27ae1f1aae');\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": 79
}
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"id": "z3ghh7CWd8KC"
},
"outputs": [],
"source": [
"df1.columns = ['prompt', 'completion']"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "gjDCSFdleC5n",
"outputId": "1b73d9ea-a7e4-43ff-82dc-813e84eb1c49"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" prompt completion\n",
"0 Question: \\n\\nAnswer:\\n\\n END\n",
"1 Question: (Red-Fruity, Full-Bodied) Shiraz or Zinfandel \n",
"2 Question: (White-Crisp, Dry) Sauvignon Blanc \n",
"3 Question: (White, None) Sweet Gewurztraminer \n",
"4 Question: (Red-Earthy, None) Chianti "
],
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" <td>Chianti</td>\n",
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" document.querySelector('#df-bbb306c0-7700-4037-a5aa-a539042b7b61 button.colab-df-convert');\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-bbb306c0-7700-4037-a5aa-a539042b7b61');\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": 81
}
],
"source": [
"df1.head()"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"id": "bUCjN6UVeERH"
},
"outputs": [],
"source": [
"df1.to_json('qa_sample.jsonl', orient='records', lines=True)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"id": "5pNltIc1eKpP"
},
"outputs": [],
"source": [
"import os\n",
"os.environ['OPENAI_API_KEY'] = \"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\""
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qMKRiq8jeVN6",
"outputId": "68747b92-83c8-47c2-a7f5-690c0475df48"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Analyzing...\n",
"\n",
"- Your file contains 20 prompt-completion pairs. In general, we recommend having at least a few hundred examples. We've found that performance tends to linearly increase for every doubling of the number of examples\n",
"- Your data does not contain a common separator at the end of your prompts. Having a separator string appended to the end of the prompt makes it clearer to the fine-tuned model where the completion should begin. See https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset for more detail and examples. If you intend to do open-ended generation, then you should leave the prompts empty\n",
"- All prompts start with prefix `Question: `\n",
"- Your data does not contain a common ending at the end of your completions. Having a common ending string appended to the end of the completion makes it clearer to the fine-tuned model where the completion should end. See https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset for more detail and examples.\n",
"- The completion should start with a whitespace character (` `). This tends to produce better results due to the tokenization we use. See https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset for more details\n",
"\n",
"Based on the analysis we will perform the following actions:\n",
"- [Recommended] Add a suffix separator `\\n\\n###\\n\\n` to all prompts [Y/n]: Y\n",
"- [Recommended] Add a suffix ending `\\n` to all completions [Y/n]: Y\n",
"- [Recommended] Add a whitespace character to the beginning of the completion [Y/n]: Y\n",
"\n",
"\n",
"Your data will be written to a new JSONL file. Proceed [Y/n]: Y\n",
"\n",
"Wrote modified file to `qa_sample_prepared.jsonl`\n",
"Feel free to take a look!\n",
"\n",
"Now use that file when fine-tuning:\n",
"> openai api fine_tunes.create -t \"qa_sample_prepared.jsonl\"\n",
"\n",
"After you’ve fine-tuned a model, remember that your prompt has to end with the indicator string `\\n\\n###\\n\\n` for the model to start generating completions, rather than continuing with the prompt. Make sure to include `stop=[\"\\n\"]` so that the generated texts ends at the expected place.\n",
"Once your model starts training, it'll approximately take 2.72 minutes to train a `curie` model, and less for `ada` and `babbage`. Queue will approximately take half an hour per job ahead of you.\n"
]
}
],
"source": [
"!openai tools fine_tunes.prepare_data -f qa_sample.jsonl"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rB6S552FeNmn",
"outputId": "df95cacd-e10e-4f12-fd7d-b93047e03ed8"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\rUpload progress: 0% 0.00/1.72k [00:00<?, ?it/s]\rUpload progress: 100% 1.72k/1.72k [00:00<00:00, 2.53Mit/s]\n",
"Uploaded file from qa_sample_prepared.jsonl: file-EJzP9CiMe5CeL9ub20pEVPDr\n",
"Created fine-tune: ft-rtDJX5c4gZGQsathmhgkAdMZ\n",
"Streaming events until fine-tuning is complete...\n",
"\n",
"(Ctrl-C will interrupt the stream, but not cancel the fine-tune)\n",
"[2023-03-23 18:29:02] Created fine-tune: ft-rtDJX5c4gZGQsathmhgkAdMZ\n",
"\n",
"Stream interrupted (client disconnected).\n",
"To resume the stream, run:\n",
"\n",
" openai api fine_tunes.follow -i ft-rtDJX5c4gZGQsathmhgkAdMZ\n",
"\n"
]
}
],
"source": [
"!openai api fine_tunes.create -t 'qa_sample_prepared.jsonl' -m 'davinci'"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Sav-4eTkekpn",
"outputId": "4178cdcf-c0d6-436d-90e0-6caf82cb37cb"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[2023-03-23 18:29:02] Created fine-tune: ft-rtDJX5c4gZGQsathmhgkAdMZ\n",
"[2023-03-23 18:32:38] Fine-tune costs $0.05\n",
"[2023-03-23 18:32:38] Fine-tune enqueued. Queue number: 5\n",
"[2023-03-23 18:33:28] Fine-tune is in the queue. Queue number: 4\n",
"[2023-03-23 18:33:46] Fine-tune is in the queue. Queue number: 3\n",
"[2023-03-23 18:34:34] Fine-tune is in the queue. Queue number: 2\n",
"[2023-03-23 18:36:03] Fine-tune is in the queue. Queue number: 1\n",
"[2023-03-23 18:36:46] Fine-tune is in the queue. Queue number: 0\n",
"[2023-03-23 18:36:52] Fine-tune started\n",
"[2023-03-23 18:39:01] Completed epoch 1/4\n",
"[2023-03-23 18:39:07] Completed epoch 2/4\n",
"[2023-03-23 18:39:12] Completed epoch 3/4\n",
"[2023-03-23 18:39:18] Completed epoch 4/4\n",
"[2023-03-23 18:39:56] Uploaded model: davinci:ft-global-corporate-holdings-2023-03-23-18-39-55\n",
"[2023-03-23 18:39:57] Uploaded result file: file-my0JOmf92CH1KdwI9SG6czp6\n",
"[2023-03-23 18:39:57] Fine-tune succeeded\n",
"\n",
"Job complete! Status: succeeded 🎉\n",
"Try out your fine-tuned model:\n",
"\n",
"openai api completions.create -m davinci:ft-global-corporate-holdings-2023-03-23-18-39-55 -p <YOUR_PROMPT>\n"
]
}
],
"source": [
"!openai api fine_tunes.follow -i ft-rtDJX5c4gZGQsathmhgkAdMZ"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"id": "pwdFktBKe_On",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3a817df0-479b-430b-995b-a184878aff87"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Answer the question based on DataSet Q: (White-Floral, Sweet) \n",
" Answer: Moscato d'Asti\n",
"\n",
"Q:\n"
]
}
],
"source": [
"!openai api completions.create -m davinci:ft-global-corporate-holdings-2023-03-23-18-39-55 -p \"Answer the question based on DataSet Q: (White-Floral, Sweet)\""
]
},
{
"cell_type": "code",
"source": [
"import openai\n",
"def get_answers_api(question):\n",
" try:\n",
" response = openai.Completion.create(\n",
" api_key = \"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\",\n",
" engine=\"davinci:ft-global-corporate-holdings-2023-03-23-18-39-55\",\n",
" prompt=f\"Answer the question based on DataSet Q:\\n{question}\\n\\nAnswer:\",\n",
" temperature=0,\n",
" max_tokens=900,\n",
" top_p=1,\n",
" frequency_penalty=0,\n",
" presence_penalty=0\n",
" )\n",
" return response['choices'][0]['text']\n",
" except Exception as e:\n",
" print (e)\n",
" return \"\"\n",
"\n",
"print(get_answers_api('White-Floral, Sweet'))"
],
"metadata": {
"id": "FOpiYqihxjUo",
"outputId": "87e7b8a2-c785-492c-b844-9535036aa9bc",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 99,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n",
"\n",
"The correct answer is:\n",
"\n",
"\"Chardonnay\"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import openai\n",
"openai.api_key = \"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\"\n",
"response = openai.ChatCompletion.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages= [{\n",
" \"role\": \"system\",\n",
" \"content\": \"You are a assistant\"\n",
" },\n",
"\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"Can you recommend wine based on my taste?\"\n",
" },\n",
"\n",
" {\n",
" \"content\": \"Of course! Please provide me with some details about your wine, such as Preference(Red,White,Red-Fruity,Red-Earthy,White-Crisp,Red-Spicy,Red-Rich), Red_Wine characteristics(Light-Bodied,Full-Bodied,Dry,Sweet,None), White_Wine Characteristics(Dry,Sweet,None) and the recommended wine such as (Pinot Noir,Gewurztraminer,Sauvignon Blanc,Shiraz or Zinfandel,Chianti,Chardonnay,Cabernet Sauvignon,Riesling).\",\n",
" \"role\": \"assistant\"\n",
" }, \n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"(Red,Light-Bodied,None)\"\n",
" }\n",
"\n",
" \n",
" ]\n",
" )\n",
"print(response)"
],
"metadata": {
"id": "nbEDdjmgyNLE",
"outputId": "c5b79e79-346d-4bde-b750-98e2cb58c813",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 101,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{\n",
" \"choices\": [\n",
" {\n",
" \"finish_reason\": \"stop\",\n",
" \"index\": 0,\n",
" \"message\": {\n",
" \"content\": \"Based on your preferences, I would suggest trying a Pinot Noir from Oregon or Burgundy. Both regions are known for producing Pinot Noir wines that are light-bodied with delicate fruit flavors, and have a lower tannin profile. Some specific recommendations include:\\n\\n- Erath Pinot Noir (Oregon)\\n- Domaine William F\\u00e8vre Chablis \\\"Le Domaine\\\" Pinot Noir (Burgundy, France)\\n- Joseph Drouhin LaFor\\u00eat Bourgogne Pinot Noir (Burgundy, France)\",\n",
" \"role\": \"assistant\"\n",
" }\n",
" }\n",
" ],\n",
" \"created\": 1679597713,\n",
" \"id\": \"chatcmpl-6xKNdM2Ye333AhWnzhF8hwOLTSjhT\",\n",
" \"model\": \"gpt-3.5-turbo-0301\",\n",
" \"object\": \"chat.completion\",\n",
" \"usage\": {\n",
" \"completion_tokens\": 108,\n",
" \"prompt_tokens\": 164,\n",
" \"total_tokens\": 272\n",
" }\n",
"}\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "F_H-PxFL0lzw"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"nbformat": 4,
"nbformat_minor": 0
}
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