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https://github.com/kennethreitz/instructor.git
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Ivan
596 lines
23 KiB
Plaintext
596 lines
23 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "8bb7d0d0-2b7f-4e9e-8565-467dc5c6fd22",
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"metadata": {},
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"source": [
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"# General Tips on Prompting\n",
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"\n",
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"Before we get into some big applications of schema engineering I want to equip you with the tools for success. \n",
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"\n",
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"This notebook is to share some general advice when using prompts to get the most of your models. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "8a785c25-b08d-4ab4-bbd7-22e3b090c2ed",
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"metadata": {},
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"source": [
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"## Classification\n",
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"\n",
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"For classification we've found theres generally two methods of modeling.\n",
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"\n",
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"1. using Enums\n",
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"2. using Literals\n",
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"\n",
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"\n",
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"Use an enum in Python when you need a set of named constants that are related and you want to ensure type safety, readability, and prevent invalid values. Enums are helpful for grouping and iterating over these constants.\n",
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"\n",
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"Use literals when you have a small, unchanging set of values that you don't need to group or iterate over, and when type safety and preventing invalid values is less of a concern. Literals are simpler and more direct for basic, one-off values."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "fdf5e1d9-31ad-4e8a-a55e-e2e70fff598d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'age': 17, 'name': 'Harry Potter', 'house': <House.Gryffindor: 'gryffindor'>}"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from enum import Enum\n",
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"from pydantic import BaseModel,Field\n",
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"from typing import List,Literal\n",
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"import instructor\n",
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"from openai import OpenAI\n",
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"\n",
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"client = instructor.patch(OpenAI())\n",
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"\n",
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"# Tip: Do not use auto() as they cast to 1,2,3,4 \n",
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"class House(Enum):\n",
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" Gryffindor = \"gryffindor\"\n",
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" Hufflepuff = \"hufflepuff\"\n",
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" Ravenclaw = \"ravenclaw\"\n",
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" Slytherin = \"slytherin\"\n",
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"\n",
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"class Character(BaseModel):\n",
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" age: int\n",
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" name: str\n",
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" house: House\n",
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" \n",
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"resp = client.chat.completions.create(\n",
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" model=\"gpt-4-1106-preview\",\n",
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" messages=[\n",
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" {\n",
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" \"role\": \"user\", \n",
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" \"content\": \"Harry Potter\"\n",
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" }\n",
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" ],\n",
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" response_model=Character\n",
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")\n",
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"resp.model_dump()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "03db160c-81e9-4373-bfec-7a107224b6dd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'age': 17, 'name': 'Harry Potter', 'house': 'Gryffindor'}"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"class Character(BaseModel):\n",
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" age: int\n",
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" name: str\n",
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" house: Literal[\"Gryffindor\", \"Hufflepuff\", \"Ravenclaw\", \"Slytherin\"]\n",
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" \n",
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"resp = client.chat.completions.create(\n",
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" model=\"gpt-4-1106-preview\",\n",
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" messages=[\n",
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" {\n",
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" \"role\": \"user\", \n",
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" \"content\": \"Harry Potter\"\n",
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" }\n",
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" ],\n",
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" response_model=Character\n",
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")\n",
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"resp.model_dump()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "803e0ce6-6e7e-4d86-a7a8-49ebaad0a40b",
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"metadata": {},
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"source": [
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"## Arbitrary long properties\n",
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"\n",
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"Often times there are long properties that you might want to extract from data that we can not specify in advanced. We can get around this by defining an arbitrary key value store like so:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "0e7938b8-4666-4df4-bd80-f53e8baf7550",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'age': 38,\n",
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" 'name': 'Severus Snape',\n",
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" 'house': 'Slytherin',\n",
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" 'properties': [{'key': 'role', 'value': 'Professor of Potions'},\n",
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" {'key': 'loyalty', 'value': 'Dumbledore'},\n",
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" {'key': 'patronus', 'value': 'Doe'},\n",
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" {'key': 'played_by', 'value': 'Alan Rickman'}]}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from typing import List\n",
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"\n",
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"class Property(BaseModel):\n",
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" key: str = Field(description=\"Must be snake case\")\n",
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" value: str\n",
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"\n",
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"class Character(BaseModel):\n",
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" age: int\n",
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" name: str\n",
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" house: Literal[\"Gryffindor\", \"Hufflepuff\", \"Ravenclaw\", \"Slytherin\"]\n",
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" properties: List[Property]\n",
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"\n",
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"resp = client.chat.completions.create(\n",
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" model=\"gpt-4-1106-preview\",\n",
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" messages=[\n",
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" {\n",
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" \"role\": \"user\", \n",
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" \"content\": \"Snape from Harry Potter\"\n",
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" }\n",
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" ],\n",
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" response_model=Character\n",
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")\n",
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"resp.model_dump()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b3e62f68-a79f-4f65-9c1f-726e4e2d340a",
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"metadata": {},
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"source": [
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"## Limiting the length of lists \n",
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"\n",
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"In later chapters we'll talk about how to use validators to assert the length of lists but we can also use prompting tricks to enumerate values. Here we'll define a index to count the properties.\n",
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"\n",
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"In this following example instead of extraction we're going to work on generation instead."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "69a58d01-ab6f-41b6-bc0c-b0e55fdb6fe4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'age': 38,\n",
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" 'name': 'Severus Snape',\n",
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" 'house': 'Slytherin',\n",
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" 'properties': [{'index': '1',\n",
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" 'key': 'Occupation',\n",
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" 'value': 'Potions Master, Defense Against the Dark Arts Professor, Headmaster'},\n",
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" {'index': '2',\n",
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" 'key': 'Loyalty',\n",
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" 'value': 'Hogwarts, Order of the Phoenix, Albus Dumbledore, Lily Potter'},\n",
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" {'index': '3',\n",
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" 'key': 'Skills',\n",
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" 'value': 'Potions, Occlumency, Legilimency, Spell creation'},\n",
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" {'index': '4', 'key': 'Patronus', 'value': 'Doe'},\n",
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" {'index': '5', 'key': 'Actor', 'value': 'Alan Rickman'}]}"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"class Property(BaseModel):\n",
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" index: str = Field(..., description=\"Monotonically increasing ID\")\n",
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" key: str\n",
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" value: str\n",
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"\n",
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"class Character(BaseModel):\n",
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" age: int\n",
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" name: str\n",
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" house: Literal[\"Gryffindor\", \"Hufflepuff\", \"Ravenclaw\", \"Slytherin\"]\n",
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" properties: List[Property] = Field(..., description=\"Numbered list of arbitrary extracted properties, should be exactly 5\")\n",
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"\n",
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"resp = client.chat.completions.create(\n",
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" model=\"gpt-4-1106-preview\",\n",
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" messages=[\n",
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" {\n",
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" \"role\": \"user\", \n",
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" \"content\": \"Snape from Harry Potter\"\n",
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" }\n",
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" ],\n",
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" response_model=Character\n",
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")\n",
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"resp.model_dump()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bbc1d900-617a-4e4d-a401-6d10a5153cda",
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"metadata": {},
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"source": [
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"## Defining Multiple Entities\n",
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"\n",
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"Now that we see a single entity with many properties we can continue to nest them into many users"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "1f2a2b14-a956-4f96-90c9-e11ca04ab7d1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'users': [{'age': 38,\n",
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" 'name': 'Severus Snape',\n",
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" 'house': 'Slytherin',\n",
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" 'properties': [{'index': '1',\n",
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" 'key': 'Occupation',\n",
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" 'value': 'Potions Master, Defense Against the Dark Arts teacher, Headmaster'},\n",
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" {'index': '2',\n",
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" 'key': 'Loyalty',\n",
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" 'value': 'Hogwarts School, Order of the Phoenix, Albus Dumbledore'},\n",
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" {'index': '3',\n",
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" 'key': 'Skills',\n",
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" 'value': 'Potions, Occlumency, Legilimency'},\n",
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" {'index': '4', 'key': 'Patronus', 'value': 'Doe'},\n",
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" {'index': '5', 'key': 'Played by', 'value': 'Alan Rickman'}]},\n",
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" {'age': 115,\n",
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" 'name': 'Albus Dumbledore',\n",
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" 'house': 'Gryffindor',\n",
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" 'properties': [{'index': '1',\n",
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" 'key': 'Occupation',\n",
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" 'value': 'Headmaster, Founder of the Order of the Phoenix'},\n",
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" {'index': '2',\n",
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" 'key': 'Loyalty',\n",
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" 'value': 'Hogwarts School, Order of the Phoenix'},\n",
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" {'index': '3', 'key': 'Skills', 'value': 'Transfiguration, Alchemy'},\n",
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" {'index': '4', 'key': 'Patronus', 'value': 'Phoenix'},\n",
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" {'index': '5',\n",
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" 'key': 'Played by',\n",
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" 'value': 'Richard Harris (films 1-2), Michael Gambon (films 3-8)'}]}]}"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"class Characters(BaseModel):\n",
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" users: List[Character]\n",
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"\n",
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"resp = client.chat.completions.create(\n",
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" model=\"gpt-4-1106-preview\",\n",
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" messages=[\n",
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" {\n",
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" \"role\": \"user\", \n",
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" \"content\": \"Snape and Dumbledore from Harry Potter\"\n",
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" }\n",
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" ],\n",
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" response_model=Characters\n",
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")\n",
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"resp.model_dump()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f6ed3144-bde1-4033-9c94-a6926fa079d2",
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"metadata": {},
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"source": [
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"## Defining Relationships \n",
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"\n",
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"Now only can we define lists of users, with list of properties one of the more interesting things I've learned about prompting is that we can also easily define lists of references."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "6de8768e-b36a-4a51-9cf9-940d178552f6",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'users': [{'id': 1, 'name': 'Harry Potter', 'friends': [2, 3, 4, 5, 6, 7]},\n",
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" {'id': 2, 'name': 'Ron Weasley', 'friends': [1, 3, 5, 6, 7]},\n",
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" {'id': 3, 'name': 'Hermione Granger', 'friends': [1, 2, 5, 6, 7]},\n",
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" {'id': 4, 'name': 'Draco Malfoy', 'friends': [1, 7]},\n",
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" {'id': 5, 'name': 'Neville Longbottom', 'friends': [1, 2, 3]},\n",
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" {'id': 6, 'name': 'Luna Lovegood', 'friends': [1, 2, 3]},\n",
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" {'id': 7, 'name': 'Ginny Weasley', 'friends': [1, 2, 3]},\n",
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" {'id': 8, 'name': 'Fred Weasley', 'friends': [2, 7]},\n",
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" {'id': 9, 'name': 'George Weasley', 'friends': [2, 7]}]}"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"class Character(BaseModel):\n",
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" id: int\n",
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" name: str\n",
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" friends: List[int]\n",
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"\n",
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"class Characters(BaseModel):\n",
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" users: List[Character]\n",
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"\n",
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"resp = client.chat.completions.create(\n",
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" model=\"gpt-4-1106-preview\",\n",
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" messages=[\n",
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" {\n",
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" \"role\": \"user\", \n",
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" \"content\": \"The kids from from Harry Potter\"\n",
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" }\n",
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" ],\n",
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" response_model=Characters\n",
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")\n",
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"resp.model_dump()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "b31e10d7-ebd2-49b4-b2c4-20dd67ca135d",
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"metadata": {},
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"outputs": [
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"<text text-anchor=\"middle\" x=\"304.67\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">Luna Lovegood</text>\n",
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"</g>\n",
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"</g>\n",
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"</svg>\n"
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],
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"text/plain": [
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"<graphviz.graphs.Digraph at 0x109081f90>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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"from graphviz import Digraph\n",
|
|
"from IPython.display import display\n",
|
|
"from typing import List\n",
|
|
"\n",
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"dot = Digraph()\n",
|
|
"\n",
|
|
"# Create nodes for each user\n",
|
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"for user in resp.users:\n",
|
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" dot.node(str(user.id), user.name)\n",
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"\n",
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"# Create edges for friends\n",
|
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"for user in resp.users:\n",
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" for friend_id in user.friends:\n",
|
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" # To avoid duplicating edges, only add an edge if the friend ID is greater than the user ID\n",
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|
" if friend_id > user.id:\n",
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" dot.edge(str(user.id), str(friend_id))\n",
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" \n",
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"\n",
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"# Render the graph to a file\n",
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"display(dot)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "523b5797-71a5-4a96-a4b7-21280fb73015",
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"metadata": {},
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"source": [
|
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"With the tools we've discussed, we can find numerous real-world applications in production settings. These include extracting action items from transcripts, generating fake data, filling out forms, and creating objects that correspond to generative UI. These simple tricks will be highly useful."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "instructor-env",
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"language": "python",
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"name": "instructor-env"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"name": "python",
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}
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},
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}
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