{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "834bf7f1",
   "metadata": {},
   "source": [
    "Task: build a tool that takes a technical question and responds with an explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac41ae00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports \n",
    "\n",
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9727896",
   "metadata": {},
   "outputs": [],
   "source": [
    "openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e2ed70e",
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_LLAMA = 'llama3.2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae31ec03",
   "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": "918bc133",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"\"\"\n",
    "You are an expert software engineer.\n",
    "You are given a technical question and you need to explain what the code does and why.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9bbdcb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Llama 3.2 to answer\n",
    "from IPython.display import Markdown, update_display\n",
    "\n",
    "\n",
    "stream = openai.chat.completions.create(\n",
    "    model=MODEL_LLAMA,\n",
    "    messages=[\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": question}\n",
    "      ],\n",
    "    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",
    "    update_display(Markdown(response), display_id=display_handle.display_id)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}