{ "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": null, "id": "c1070317-3ed9-4659-abe3-828943230e03", "metadata": {}, "outputs": [], "source": [ "import os\n", "import ollama\n", "from dotenv import load_dotenv\n", "from IPython.display import Markdown, display, update_display\n", "from openai import OpenAI" ] }, { "cell_type": "code", "execution_count": null, "id": "4a456906-915a-4bfd-bb9d-57e505c5093f", "metadata": {}, "outputs": [], "source": [ "# constants\n", "\n", "load_dotenv(override=True)\n", "api_key = os.getenv('OPENAI_API_KEY')\n", "\n", "if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n", " print(\"API key looks good so far\")\n", "else:\n", " print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n", "\n", "MODEL_GPT = 'gpt-4o-mini'\n", "MODEL_LLAMA = 'llama3.2'\n", "\n", "openai = OpenAI()" ] }, { "cell_type": "code", "execution_count": null, "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", "metadata": {}, "outputs": [], "source": [ "# set up environment\n", "system_prompt = \"You are an assistant that analyzes a technical question \\\n", "and responds with a short, clear, structured explanation. Response in markdown\"\n", "\n", "def get_user_prompt(question):\n", " user_prompt = f\"You are looking at a technical questions as following: \\n\"\n", " user_prompt += question\n", " user_prompt += f\"\\nPlease response answer with a logical explanation\"\n", " return user_prompt" ] }, { "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": null, "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", "metadata": {}, "outputs": [], "source": [ "# Get gpt-4o-mini to answer, with streaming\n", "def stream_QA(question):\n", " stream = openai.chat.completions.create(\n", " model= MODEL_GPT,\n", " messages = [\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": get_user_prompt(question)}\n", " ],\n", " stream=True\n", " )\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)\n", "\n", "stream_QA(question)" ] }, { "cell_type": "code", "execution_count": null, "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538", "metadata": {}, "outputs": [], "source": [ "# Get Llama 3.2 to answer\n", "\n", "def ollama_QA(question):\n", " response = ollama.chat(model=MODEL_LLAMA, messages = [\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": get_user_prompt(question)}\n", " ])\n", " return Markdown(response['message']['content'])\n", "\n", "ollama_QA(question)" ] }, { "cell_type": "code", "execution_count": null, "id": "6b484100-e5cf-40db-827c-d5618b154654", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 5 }