1_lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to the start of your adventure in Agentic AI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/stop.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#ff7800;\">Are you ready for action??</h2>\n",
    "            <span style=\"color:#ff7800;\">Have you completed all the setup steps in the <a href=\"../setup/\">setup</a> folder?<br/>\n",
    "            Have you checked out the guides in the <a href=\"../guides/01_intro.ipynb\">guides</a> folder?<br/>\n",
    "            Well in that case, you're ready!!\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/tools.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#00bfff;\">Treat these labs as a resource</h2>\n",
    "            <span style=\"color:#00bfff;\">I push updates to the code regularly. When people ask questions or have problems, I incorporate it in the code, adding more examples or improved commentary. As a result, you'll notice that the code below isn't identical to the videos. Everything from the videos is here; but in addition, I've added more steps and better explanations. Consider this like an interactive book that accompanies the lectures.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### And please do remember to contact me if I can help\n",
    "\n",
    "And I love to connect: https://www.linkedin.com/in/eddonner/\n",
    "\n",
    "\n",
    "### New to Notebooks like this one? Head over to the guides folder!\n",
    "\n",
    "Otherwise:\n",
    "1. Click where it says \"Select Kernel\" near the top right, and select the option called `.venv (Python 3.12.9)` or similar, which should be the first choice or the most prominent choice.\n",
    "2. Click in each \"cell\" below, starting with the cell immediately below this text, and press Shift+Enter to run\n",
    "3. Enjoy!\n",
    "\n",
    "After you click \"Select Kernel\", if there is no option like `.venv (Python 3.12.9)` then please do the following:  \n",
    "1. From the Cursor menu, choose Settings >> VSCode Settings (NOTE: be sure to select `VSCode Settings` not `Cursor Settings`)  \n",
    "2. In the Settings search bar, type \"venv\"  \n",
    "3. In the field \"Path to folder with a list of Virtual Environments\" put the path to the project root, like C:\\Users\\username\\projects\\agents (on a Windows PC) or /Users/username/projects/agents (on Mac or Linux).  \n",
    "And then try again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First let's do an import\n",
    "from dotenv import load_dotenv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Next it's time to load the API keys into environment variables\n",
    "\n",
    "load_dotenv(override=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenAI API Key exists and begins sk-proj-\n"
     ]
    }
   ],
   "source": [
    "# Check the keys\n",
    "\n",
    "import os\n",
    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
    "\n",
    "if openai_api_key:\n",
    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
    "else:\n",
    "    print(\"OpenAI API Key not set - please head to the troubleshooting guide in the guides folder\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now - the all important import statement\n",
    "# If you get an import error - head over to troubleshooting guide\n",
    "\n",
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now we'll create an instance of the OpenAI class\n",
    "# If you're not sure what it means to create an instance of a class - head over to the guides folder!\n",
    "# If you get a NameError - head over to the guides folder to learn about NameErrors\n",
    "\n",
    "openai = OpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of messages in the familiar OpenAI format\n",
    "\n",
    "messages = [{\"role\": \"user\", \"content\": \"What is 2+2?\"}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 + 2 equals 4.\n"
     ]
    }
   ],
   "source": [
    "# And now call it! Any problems, head to the troubleshooting guide\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4o-mini\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "print(response.choices[0].message.content)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now - let's ask for a question:\n",
    "\n",
    "question = \"Please propose a hard, challenging question to assess someone's IQ. Respond only with the question.\"\n",
    "messages = [{\"role\": \"user\", \"content\": question}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If a train leaves a station traveling at 60 miles per hour and another train leaves a different station 100 miles away traveling toward the first train at 90 miles per hour, how far from the first station will they meet, and how long will it take for them to do so?\n"
     ]
    }
   ],
   "source": [
    "# ask it\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4o-mini\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "question = response.choices[0].message.content\n",
    "\n",
    "print(question)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# form a new messages list\n",
    "messages = [{\"role\": \"user\", \"content\": question}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To determine when and where the two trains will meet, we can use relative speed and basic distance formulas.\n",
      "\n",
      "1. **Determine the speeds of the trains:**\n",
      "   - The first train travels at 60 miles per hour (mph).\n",
      "   - The second train travels at 90 mph toward the first train.\n",
      "\n",
      "2. **Calculate the relative speed:**\n",
      "   Since the two trains are moving toward each other, we can add their speeds to find the relative speed:\n",
      "   \\[\n",
      "   \\text{Relative speed} = 60 \\text{ mph} + 90 \\text{ mph} = 150 \\text{ mph}\n",
      "   \\]\n",
      "\n",
      "3. **Determine the distance between the two trains:**\n",
      "   The initial distance between the two stations is 100 miles.\n",
      "\n",
      "4. **Calculate the time until they meet:**\n",
      "   We can use the formula \\( \\text{Time} = \\frac{\\text{Distance}}{\\text{Speed}} \\).\n",
      "   \\[\n",
      "   \\text{Time} = \\frac{100 \\text{ miles}}{150 \\text{ mph}} = \\frac{2}{3} \\text{ hours}\n",
      "   \\]\n",
      "   To convert this to minutes, multiply by 60:\n",
      "   \\[\n",
      "   \\frac{2}{3} \\times 60 = 40 \\text{ minutes}\n",
      "   \\]\n",
      "\n",
      "5. **Determine how far the first train travels before they meet:**\n",
      "   We can find the distance traveled by the first train in that time:\n",
      "   \\[\n",
      "   \\text{Distance (first train)} = \\text{Speed} \\times \\text{Time} = 60 \\text{ mph} \\times \\frac{2}{3} \\text{ hours} = 40 \\text{ miles}\n",
      "   \\]\n",
      "\n",
      "Thus, the two trains will meet **40 miles from the first station** and it will take them **40 minutes** to do so.\n"
     ]
    }
   ],
   "source": [
    "# Ask it again\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4o-mini\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "answer = response.choices[0].message.content\n",
    "print(answer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "To determine when and where the two trains will meet, we can use relative speed and basic distance formulas.\n",
       "\n",
       "1. **Determine the speeds of the trains:**\n",
       "   - The first train travels at 60 miles per hour (mph).\n",
       "   - The second train travels at 90 mph toward the first train.\n",
       "\n",
       "2. **Calculate the relative speed:**\n",
       "   Since the two trains are moving toward each other, we can add their speeds to find the relative speed:\n",
       "   \\[\n",
       "   \\text{Relative speed} = 60 \\text{ mph} + 90 \\text{ mph} = 150 \\text{ mph}\n",
       "   \\]\n",
       "\n",
       "3. **Determine the distance between the two trains:**\n",
       "   The initial distance between the two stations is 100 miles.\n",
       "\n",
       "4. **Calculate the time until they meet:**\n",
       "   We can use the formula \\( \\text{Time} = \\frac{\\text{Distance}}{\\text{Speed}} \\).\n",
       "   \\[\n",
       "   \\text{Time} = \\frac{100 \\text{ miles}}{150 \\text{ mph}} = \\frac{2}{3} \\text{ hours}\n",
       "   \\]\n",
       "   To convert this to minutes, multiply by 60:\n",
       "   \\[\n",
       "   \\frac{2}{3} \\times 60 = 40 \\text{ minutes}\n",
       "   \\]\n",
       "\n",
       "5. **Determine how far the first train travels before they meet:**\n",
       "   We can find the distance traveled by the first train in that time:\n",
       "   \\[\n",
       "   \\text{Distance (first train)} = \\text{Speed} \\times \\text{Time} = 60 \\text{ mph} \\times \\frac{2}{3} \\text{ hours} = 40 \\text{ miles}\n",
       "   \\]\n",
       "\n",
       "Thus, the two trains will meet **40 miles from the first station** and it will take them **40 minutes** to do so."
      ],
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       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Markdown, display\n",
    "\n",
    "display(Markdown(answer))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Congratulations!\n",
    "\n",
    "That was a small, simple step in the direction of Agentic AI, with your new environment!\n",
    "\n",
    "Next time things get more interesting..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/exercise.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#ff7800;\">Exercise</h2>\n",
    "            <span style=\"color:#ff7800;\">Now try this commercial application:<br/>\n",
    "            First ask the LLM to pick a business area that might be worth exploring for an Agentic AI opportunity.<br/>\n",
    "            Then ask the LLM to present a pain-point in that industry - something challenging that might be ripe for an Agentic solution.<br/>\n",
    "            Finally have 3 third LLM call propose the Agentic AI solution.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healthcare management.\n",
      "Supply chain disruptions.\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Automated decision-making systems that empower businesses to optimize processes, reduce operational costs, and increase efficiency through advanced machine learning algorithms."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First create the messages:\n",
    "\n",
    "messages = [{\"role\": \"user\", \"content\": \"Pick a business area that might be worth exploring for an Agentic AI opportunity. Just answer with the business area, no other text.\"}]\n",
    "\n",
    "# Then make the first call:\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4o-mini\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "\n",
    "# Then read the business idea:\n",
    "\n",
    "business_idea = response.choices[0].message.content\n",
    "\n",
    "print(business_idea)\n",
    "\n",
    "# And repeat!\n",
    "\n",
    "messages = [{\"role\": \"user\", \"content\": f\"What is one of the pain points in {business_idea}? Just answer with the pain point, no other text.\"}]\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4o-mini\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "pain_point = response.choices[0].message.content\n",
    "\n",
    "print(pain_point)\n",
    "\n",
    "# And repeat!\n",
    "\n",
    "messages = [{\"role\": \"user\", \"content\": f\"What is the Agentic AI solution to the pain point you picked, which is {pain_point}? Just answer with the solution, no other text.\"}]\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4o-mini\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "agentic_solution = response.choices[0].message.content\n",
    "\n",
    "display(Markdown(agentic_solution))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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