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generate-data and some eval

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Kyle Corbitt
2023-08-24 18:43:42 +00:00
parent 12d01cd3d5
commit 13bac46e0b
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook I'm using the OpenPipe client to capture a set of calls to the OpenAI API.\n",
"\n",
"For this example I'll blithely throw engineering best practices to the wind and use the notebook itself to manage dependencies. 😁"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: openpipe==3.0.3 in /usr/local/lib/python3.10/dist-packages (3.0.3)\n",
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"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
"\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.10 -m pip install --upgrade pip\u001b[0m\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install openpipe==3.0.3 python-dotenv==1.0.0 joblib==1.3.2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When working with remote datasets (or any data, really), it's a good idea to visually inspect some samples to make sure it looks like you expect. I'll print a recipe."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recipe dataset:\n"
]
},
{
"data": {
"text/plain": [
"Dataset({\n",
" features: ['recipe'],\n",
" num_rows: 5000\n",
"})"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"First recipe:\n",
" Shrimp Creole\n",
"\n",
"Ingredients:\n",
"- 20 shrimp (8 oz.)\n",
"- 2 c. (16 oz. can) tomato sauce\n",
"- 1 small onion, chopped\n",
"- 1 celery stalk, chopped\n",
"- 1/4 green bell pepper, diced\n",
"- 1/4 c. sliced mushrooms\n",
"- 3 Tbsp. parsley\n",
"- 1/2 tsp. pepper\n",
"- 1 to 1-1/2 c. brown rice, prepared according to pkg. directions (not included in exchanges)\n",
"\n",
"Directions:\n",
"- Peel, devein and wash shrimp; set aside.\n",
"- (If shrimp are frozen, let thaw first in the refrigerator.)\n",
"- Simmer tomato sauce, onion, celery, green pepper, mushrooms, parsley and pepper in skillet for 30 minutes.\n",
"- Add shrimp and cook 10 to 15 minutes more, until shrimp are tender.\n",
"- Serve over brown rice.\n",
"- Serves 2.\n"
]
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"recipes = load_dataset(\"corbt/unlabeled-recipes\")[\"train\"]\n",
"print(\"Recipe dataset shape:\\n------------------\")\n",
"display(recipes)\n",
"print(\"First recipe:\\n------------------\", recipes[\"recipe\"][0])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mm, delicious. Anyway, we need to generate a training dataset. We'll call GPT-4 on each of our examples.\n",
"\n",
"We'll use [OpenPipe](https://github.com/openpipe/openpipe) to track our calls and form a training dataset. Create an account and a project, then copy your API key from https://app.openpipe.ai/project/settings into a file called `.env`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'has_non_fish_meat': False,\n",
" 'requires_oven': True,\n",
" 'requires_stove': True,\n",
" 'cook_time_over_30_mins': False,\n",
" 'main_dish': False}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from openpipe import openai, configure_openpipe\n",
"import json\n",
"import os\n",
"import dotenv\n",
"\n",
"dotenv.load_dotenv()\n",
"\n",
"configure_openpipe(api_key=os.environ[\"OPENPIPE_API_KEY\"])\n",
"\n",
"openai.api_key = os.environ[\"OPENAI_API_KEY\"]\n",
"\n",
"\n",
"def classify_recipe(recipe: str):\n",
" completion = openai.ChatCompletion.create(\n",
" model=\"gpt-4\",\n",
" messages=[\n",
" {\n",
" \"role\": \"system\",\n",
" \"content\": \"Your goal is to classify a recipe along several dimensions.Pay attention to the instructions.\",\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": recipe,\n",
" },\n",
" ],\n",
" functions=[\n",
" {\n",
" \"name\": \"classify\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"has_non_fish_meat\": {\n",
" \"type\": \"boolean\",\n",
" \"description\": \"True if the recipe contains any meat or meat products (eg. chicken broth) besides fish\",\n",
" },\n",
" \"requires_oven\": {\n",
" \"type\": \"boolean\",\n",
" \"description\": \"True if the recipe requires an oven\",\n",
" },\n",
" \"requires_stove\": {\n",
" \"type\": \"boolean\",\n",
" \"description\": \"True if the recipe requires a stove\",\n",
" },\n",
" \"cook_time_over_30_mins\": {\n",
" \"type\": \"boolean\",\n",
" \"description\": \"True if the recipe takes over 30 minutes to prepare and cook, including waiting time\",\n",
" },\n",
" \"main_dish\": {\n",
" \"type\": \"boolean\",\n",
" \"description\": \"True if the recipe can be served as a main dish\",\n",
" },\n",
" },\n",
" \"required\": [\n",
" \"has_non_fish_meat\",\n",
" \"requires_oven\",\n",
" \"requires_stove\",\n",
" \"cook_time_over_30_mins\",\n",
" \"main_course\",\n",
" ],\n",
" },\n",
" }\n",
" ],\n",
" function_call={\n",
" \"name\": \"classify\",\n",
" },\n",
" openpipe={\"tags\": {\"prompt_id\": \"classify-recipe\"}, \"cache\": True},\n",
" )\n",
" return json.loads(completion.choices[0].message.function_call.arguments)\n",
"\n",
"\n",
"classify_recipe(recipes[\"recipe\"][-1])\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Classifying recipe 0/5000: Shrimp Creole\n",
"Classifying recipe 100/5000: Spoon Bread\n",
"Classifying recipe 200/5000: Quadrangle Grille'S Pumpkin-Walnut Cheesecake\n",
"Classifying recipe 300/5000: Broccoli Casserole\n",
"Error reporting to OpenPipe: 520 is not a valid HTTPStatus\n",
"520 is not a valid HTTPStatus\n",
"Classifying recipe 400/5000: Paal Payasam (3-Ingredient Rice Pudding)\n",
"Classifying recipe 500/5000: Dirt Dessert\n",
"Classifying recipe 600/5000: Dolma, Stuffed Dried Peppers And Eggplants\n",
"Classifying recipe 700/5000: Party Pecan Pies\n",
"Classifying recipe 800/5000: Pie Crust\n",
"Classifying recipe 900/5000: Russian Dressing(Salad Dressing) \n",
"Classifying recipe 1000/5000: O'Brien Potatoes\n",
"Classifying recipe 1100/5000: Monster Cookies\n",
"Classifying recipe 1200/5000: Striped Fruit Pops\n",
"Classifying recipe 1300/5000: Cute Heart-Shaped Fried Egg\n",
"Classifying recipe 1400/5000: Steak Marinade\n",
"Classifying recipe 1500/5000: Bbq Sauce For Fish Recipe\n",
"Classifying recipe 1600/5000: Barbecue Ranch Salad\n",
"Classifying recipe 1700/5000: White Fudge\n",
"Classifying recipe 1800/5000: Seaton Chocolate Chip Cookies\n",
"Classifying recipe 1900/5000: Beef Stroganoff\n",
"Classifying recipe 2000/5000: Lemon Delight\n",
"Classifying recipe 2100/5000: Cream Cheese Chicken Chili\n",
"Classifying recipe 2200/5000: Bean Salad\n",
"Classifying recipe 2300/5000: Green Beans Almondine\n",
"Classifying recipe 2400/5000: Radish-And-Avocado Salad\n",
"Classifying recipe 2500/5000: Salsa Rojo\n",
"Classifying recipe 2600/5000: Pepperoni Bread\n",
"Classifying recipe 2700/5000: Sabzi Polow\n",
"Classifying recipe 2800/5000: Italian Vegetable Pizzas\n",
"Error classifying recipe 2801: Bad gateway. {\"error\":{\"code\":502,\"message\":\"Bad gateway.\",\"param\":null,\"type\":\"cf_bad_gateway\"}} 502 {'error': {'code': 502, 'message': 'Bad gateway.', 'param': None, 'type': 'cf_bad_gateway'}} {'Date': 'Thu, 24 Aug 2023 15:44:45 GMT', 'Content-Type': 'application/json', 'Content-Length': '84', 'Connection': 'keep-alive', 'X-Frame-Options': 'SAMEORIGIN', 'Referrer-Policy': 'same-origin', 'Cache-Control': 'private, max-age=0, no-store, no-cache, must-revalidate, post-check=0, pre-check=0', 'Expires': 'Thu, 01 Jan 1970 00:00:01 GMT', 'Server': 'cloudflare', 'CF-RAY': '7fbca943df684de1-MCI', 'alt-svc': 'h3=\":443\"; ma=86400'}\n",
"Classifying recipe 2900/5000: Hot Fudge Sauce, Soda Shop Style\n",
"Classifying recipe 3000/5000: Meatball Soup With Vegetables And Brown Rice\n",
"Classifying recipe 3100/5000: Herbed Potatoes And Onions\n",
"Classifying recipe 3200/5000: Apple Crunch Pie (2 Extra Servings)\n",
"Classifying recipe 3300/5000: Pineapple-Orange Punch\n",
"Classifying recipe 3400/5000: Turkey Veggie Burgers With Avocado Mayo\n",
"Error reporting to OpenPipe: 520 is not a valid HTTPStatus\n",
"520 is not a valid HTTPStatus\n",
"Classifying recipe 3500/5000: Pear & Goat Cheese Salad\n",
"Classifying recipe 3600/5000: Triple Chocolate Cookies\n",
"Classifying recipe 3700/5000: Strawberry Banana Yogurt Pops\n",
"Error classifying recipe 3779: Request timed out: HTTPSConnectionPool(host='api.openai.com', port=443): Read timed out. (read timeout=600)\n",
"Classifying recipe 3800/5000: Chicken Croquettes\n",
"Classifying recipe 3900/5000: Mushroom Casserole\n"
]
}
],
"source": [
"for i, recipe in enumerate(recipes[\"recipe\"]):\n",
" if i % 100 == 0:\n",
" recipe_title = recipe.split(\"\\n\")[0]\n",
" print(f\"Classifying recipe {i}/{len(recipes)}: {recipe_title}\")\n",
" try:\n",
" classify_recipe(recipe)\n",
" except Exception as e:\n",
" print(f\"Error classifying recipe {i}: {e}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Ok, we have our "
]
}
],
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