{ "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": "51d1bbb7-d56a-4483-935f-480f8e22546f", "metadata": {}, "outputs": [], "source": [ "# imports\n", "import os\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": null, "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": null, "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1", "metadata": {}, "outputs": [], "source": [ "# set up environment\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", "openai = OpenAI()" ] }, { "cell_type": "code", "execution_count": null, "id": "bd61eb75-be6a-46d6-8aeb-84c1eeeac04f", "metadata": {}, "outputs": [], "source": [ "# read the user question\n", "def user_question_reader() -> str:\n", " input_text = \"Hello! I’m your AI Teacher, ready to help you explore any topic you’re curious about.\\n\"\n", " input_text +=\"I have access to a vast amount of knowledge and will do my best to explain things clearly, no matter your experience level.\\n\\n\"\n", " input_text +=\"What would you like to learn about today?\\n\"\n", "\n", " question = input(input_text)\n", "\n", " return question;" ] }, { "cell_type": "code", "execution_count": null, "id": "6bd9b592-e398-4637-9188-bfdf8dd6bf75", "metadata": {}, "outputs": [], "source": [ "# generate the user prompt\n", "def user_prompt_generator() -> str:\n", " question = user_question_reader()\n", " user_prompt = f\"I need you to answer to this question: {question}.\\n\"\n", " user_prompt += \" Take into account that I dont have prior knowledge about my question \\\n", " so I want the answer as complete as possible. Also please provide it in markdown\"\n", "\n", " return user_prompt" ] }, { "cell_type": "code", "execution_count": null, "id": "9ffda047-fec3-4d9e-97b0-46f428ac9313", "metadata": {}, "outputs": [], "source": [ "# define the system prompt\n", "system_prompt = \"Your job it's to be a teacher. You have access to all the knowledge \\\n", " in the internet. You will be thankful to any question given to you and \\\n", " will try to answer it the best you can. Your students might know little to nothing \\\n", " about what they ask and make mistakes so you will have to think about the meaning of their question \\\n", " before and provide an answer according to the meaning behind it.\"" ] }, { "cell_type": "code", "execution_count": null, "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4", "metadata": {}, "outputs": [], "source": [ "# define gpt-4o-mini function to answer, with streaming\n", "def gpt_teacher():\n", " stream = openai.chat.completions.create(\n", " model = MODEL_GPT,\n", " messages= [\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\" : user_prompt_generator()}\n", " ],\n", " stream=True\n", " )\n", "\n", " response = \"\"\n", " display_handle = display(Markdown(\"\"), display_id=True)\n", "\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": [ "# define Llama 3.2 function to answer\n", "def llama_teacher():\n", " response = ollama.chat(\n", " model = MODEL_LLAMA,\n", " messages=[\n", " {\"role\": \"system\", \"content\":system_prompt},\n", " {\"role\": \"user\", \"content\": user_prompt_generator()}\n", " ]\n", " )\n", "\n", " return display(Markdown(response['message']['content']))" ] }, { "cell_type": "code", "execution_count": null, "id": "20d963d4-f4ce-4979-b8c7-0db6ebcec96c", "metadata": {}, "outputs": [], "source": [ "# try the gpt teacher\n", "gpt_teacher()" ] }, { "cell_type": "code", "execution_count": null, "id": "d62ca06f-c808-43ee-9ecd-5a704ffcd5c1", "metadata": {}, "outputs": [], "source": [ "#try the ollama teacher\n", "llama_teacher()" ] } ], "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": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 5 }