{ "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": [ "" ] }, "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" } }, "nbformat": 4, "nbformat_minor": 5 }