{ "cells": [ { "cell_type": "markdown", "id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5", "metadata": {}, "source": [ "# End of week 1 solution\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!\n", "\n", "After week 2 you'll be able to add a User Interface to this tool, giving you a valuable application." ] }, { "cell_type": "code", "execution_count": 2, "id": "c1070317-3ed9-4659-abe3-828943230e03", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "from dotenv import load_dotenv\n", "from IPython.display import Markdown, display, update_display\n", "from openai import OpenAI\n", "# import ollama" ] }, { "cell_type": "code", "execution_count": 3, "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": [], "source": [ "# set up environment\n", "\n", "load_dotenv()\n", "openai = OpenAI()\n" ] }, { "cell_type": "code", "execution_count": 5, "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": 6, "id": "8595807b-8ae2-4e1b-95d9-e8532142e8bb", "metadata": {}, "outputs": [], "source": [ "# prompts\n", "\n", "system_prompt = \"You are a helpful technical tutor who answers questions about python code, software engineering, data science and LLMs\"\n", "user_prompt = \"Please give a detailed explanation to the following question: \" + question" ] }, { "cell_type": "code", "execution_count": 7, "id": "9605cbb6-3d3f-4969-b420-7f4cae0b9328", "metadata": {}, "outputs": [], "source": [ "# messages\n", "\n", "messages = [\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": user_prompt}\n", "]" ] }, { "cell_type": "code", "execution_count": 8, "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "Certainly! Let's break down the code snippet you've provided to understand what it does and why it operates that way.\n", "\n", "The code snippet is:\n", "\n", "python\n", "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n", "\n", "\n", "### Explanation of Components\n", "\n", "1. **Set Comprehension**:\n", " - `{book.get(\"author\") for book in books if book.get(\"author\")}`\n", " - This is a set comprehension, which is a concise way to create a set in Python.\n", " - **Iterates**: It iterates over a collection named `books`.\n", " - **Extraction**: `book.get(\"author\")` attempts to retrieve the value associated with the key `\"author\"` from each `book` dictionary.\n", " - **Filtering**: The `if book.get(\"author\")` condition filters out any books that do not have an `\"author\"` key or where the value is `None` or an empty string. This means only those books that have a valid author will be included in the set.\n", "\n", "2. **Set**:\n", " - The output of the set comprehension is a set of unique author names from the `books` collection. \n", " - Sets automatically handle duplicates, so if multiple books have the same author, their name will only appear once in the resulting set.\n", "\n", "3. **Yielding with `yield from`**:\n", " - `yield from` is a syntax used in Python to delegate part of a generator’s operations to another generator.\n", " - In this context, it means that each element obtained from the set (the unique authors) will be yielded one by one.\n", "\n", "### Overall Functionality\n", "\n", "- This code essentially extracts all unique authors from the list of `books` (where each `book` is presumably a dictionary containing various attributes) and yields each author one at a time from a generator function.\n", "\n", "### Practical Implications\n", "\n", "- When this line of code is executed within a generator function, it allows the generator to yield each unique author efficiently, enabling the caller to iterate over them.\n", "- Utilizing a set ensures that authors are only returned once, even if they appear multiple times across different books.\n", "\n", "### Summary\n", "\n", "To summarize, the code snippet:\n", "\n", "1. Extracts authors from a list of book dictionaries.\n", "2. Filters out any entries without an author.\n", "3. Collects unique authors in a set.\n", "4. Uses `yield from` to yield each author one at a time.\n", "\n", "This approach is efficient and concise, leveraging the power of Python's set comprehension and generator functions to handle potentially large data sets with an emphasis on uniqueness and iteration simplicity." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get gpt-4o-mini to answer, with streaming\n", "\n", "stream = openai.chat.completions.create(model=MODEL_GPT, messages=messages,stream=True)\n", " \n", "response = \"\"\n", "display_handle = display(Markdown(\"\"), display_id=True)\n", "for chunk in stream:\n", " response += chunk.choices[0].delta.content or ''\n", " response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n", " update_display(Markdown(response), display_id=display_handle.display_id)" ] }, { "cell_type": "code", "execution_count": null, "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", "metadata": {}, "outputs": [], "source": [ "# Get Llama 3.2 to answer\n", "\n", "response = ollama.chat(model=MODEL_LLAMA, messages=messages)\n", "reply = response['message']['content']\n", "display(Markdown(reply))" ] }, { "cell_type": "markdown", "id": "7e14bcdb-b928-4b14-961e-9f7d8c7335bf", "metadata": {}, "source": [ "# Congratulations!\n", "\n", "You could make it better by taking in the question using \n", "`my_question = input(\"Please enter your question:\")`\n", "\n", "And then creating the prompts and making the calls interactively." ] }, { "cell_type": "code", "execution_count": null, "id": "da663d73-dd2a-4fff-84df-2209cf2b330b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "llms", "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": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }