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Week 1 exercise with teachers getting input question

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Daniel Fernandez Colon
2025-08-31 21:43:13 +02:00
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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": 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": {
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"file_extension": ".py",
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"nbformat": 4,
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