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{
"cells": [
{
"cell_type": "markdown",
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
"metadata": {},
"source": [
"# End of week 1 exercise\n",
"\n",
"To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question, \n",
"and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"import json\n",
"from typing import List\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display, update_display\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
"metadata": {},
"outputs": [],
"source": [
"# constants\n",
"\n",
"MODEL_GPT = 'gpt-4o-mini'\n",
"MODEL_LLAMA = 'llama3.2'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"API key found and looks good so far!\n"
]
}
],
"source": [
"# set up environment\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"# Check the key\n",
"\n",
"if not api_key:\n",
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
"elif not api_key.startswith(\"sk-proj-\"):\n",
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
"elif api_key.strip() != api_key:\n",
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
"else:\n",
" print(\"API key found and looks good so far!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
"metadata": {},
"outputs": [],
"source": [
"# here is the question; type over this to ask something new\n",
"\n",
"question = \"\"\"\n",
"Please explain what this code does and why:\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"Certainly! This line of code is using a combination of Python set comprehension and the `yield from` statement. Let's break it down step-by-step:\n",
"\n",
"1. **Set Comprehension**: \n",
" - `{book.get(\"author\") for book in books if book.get(\"author\")}` is a set comprehension. It constructs a set containing the authors from the `books` collection.\n",
" - `books` is assumed to be an iterable, such as a list or tuple, of dictionaries, where each dictionary represents a book with various attributes, including an \"author\".\n",
" - `book.get(\"author\")` attempts to retrieve the value associated with the \"author\" key from each book dictionary.\n",
" - The `if book.get(\"author\")` part ensures that only books with a non-None value for the \"author\" key are included in the set. This helps in filtering out entries where the \"author\" key doesn't exist or is set to `None`.\n",
"\n",
"2. **Yield From**: \n",
" - `yield from` is a statement in Python that delegates part of a generator's operations to another iterable.\n",
" - `yield from` in this context is used to yield each element from the set constructed by the set comprehension one at a time.\n",
"\n",
"3. **Purpose**:\n",
" - The combination of set comprehension and `yield from` results in yielding each distinct author found in the list of books, but importantly, without duplicates, since sets naturally eliminate duplicates.\n",
" - This might be part of a generator function, which is a function allowing iteration over the unique authors.\n",
"\n",
"In summary, this code is used to retrieve and yield each unique author's name from a list of book dictionaries, one by one, eliminating any duplicates. The use of `yield from` makes it efficient for building generators that process data as an iterable sequence."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get gpt-4o-mini to answer, with streaming\n",
"from IPython.display import display, Markdown\n",
"import openai\n",
"\n",
"# Your model\n",
"MODEL = \"gpt-4o\" \n",
"# System prompt (sets the assistant's role)\n",
"system_prompt = \"You are a helpful Python expert who explains code clearly and concisely.\"\n",
"\n",
"# The user question\n",
"question = \"\"\"\n",
"Please explain what this code does and why:\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\"\"\"\n",
"\n",
"# Streaming function for explanation\n",
"def stream_code_explanation(code_question):\n",
" stream = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": code_question}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" \n",
" for chunk in stream:\n",
" content = chunk.choices[0].delta.content or \"\"\n",
" response += content\n",
" response_cleaned = response.replace(\"```\", \"\").replace(\"markdown\", \"\")\n",
" display_handle.update(Markdown(response_cleaned))\n",
"\n",
"# Run it!\n",
"stream_code_explanation(question)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
"metadata": {},
"outputs": [],
"source": [
"# Get Llama 3.2 to answer"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.0rc2"
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"nbformat": 4,
"nbformat_minor": 5
}