LLM_Engineering_OLD/week1/solutions/week1 SOLUTION.ipynb

{
 "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": null,
   "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": 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",
    "\n",
    "load_dotenv()\n",
    "openai = OpenAI()\n"
   ]
  },
  {
   "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": "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": null,
   "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": null,
   "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
   "metadata": {},
   "outputs": [],
   "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": "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
}