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;\">This code is a live resource - keep an eye out for my updates</h2>\n",
    "            <span style=\"color:#00bfff;\">I push updates regularly. As people ask questions or have problems, I add more examples and improve explanations. As a result, the code below might not be identical to the videos, as I've added more steps and better comments. Consider this like an interactive book that accompanies the lectures.<br/><br/>\n",
    "            I try to send emails regularly with important updates related to the course. You can find this in the 'Announcements' section of Udemy in the left sidebar. You can also choose to receive my emails via your Notification Settings in Udemy. I'm respectful of your inbox and always try to add value with my emails!\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",
    "Just to check you've already added the Python and Jupyter extensions to Cursor, if not already installed:\n",
    "- Open extensions (View >> extensions)\n",
    "- Search for python, and when the results show, click on the ms-python one, and Install it if not already installed\n",
    "- Search for jupyter, and when the results show, click on the Microsoft one, and Install it if not already installed  \n",
    "Then View >> Explorer to bring back the File Explorer.\n",
    "\n",
    "And then:\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. You may need to choose \"Python Environments\" first.\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. On Mac: From the Cursor menu, choose Settings >> VS Code Settings (NOTE: be sure to select `VSCode Settings` not `Cursor Settings`);  \n",
    "On Windows PC: From the File menu, choose Preferences >> VS Code 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.\n",
    "\n",
    "Having problems with missing Python versions in that list? Have you ever used Anaconda before? It might be interferring. Quit Cursor, bring up a new command line, and make sure that your Anaconda environment is deactivated:    \n",
    "`conda deactivate`  \n",
    "And if you still have any problems with conda and python versions, it's possible that you will need to run this too:  \n",
    "`conda config --set auto_activate_base false`  \n",
    "and then from within the Agents directory, you should be able to run `uv python list` and see the Python 3.12 version."
   ]
  },
  {
   "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"
      ]
     },
     "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 setup 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",
    "# This uses GPT 4.1 nano, the incredibly cheap model\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-nano\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "print(response.choices[0].message.content)\n"
   ]
  },
  {
   "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": [
      "A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?\n"
     ]
    }
   ],
   "source": [
    "# ask it - this uses GPT 4.1 mini, still cheap but more powerful than nano\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-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": [
      "Let's denote the cost of the ball as \\( x \\) dollars.\n",
      "\n",
      "Given:\n",
      "- The bat costs \\( x + 1.00 \\) dollars.\n",
      "- The total cost of the bat and ball is $1.10.\n",
      "\n",
      "Set up the equation:\n",
      "\\[\n",
      "x + (x + 1.00) = 1.10\n",
      "\\]\n",
      "\n",
      "Simplify:\n",
      "\\[\n",
      "2x + 1.00 = 1.10\n",
      "\\]\n",
      "\n",
      "Subtract 1.00 from both sides:\n",
      "\\[\n",
      "2x = 0.10\n",
      "\\]\n",
      "\n",
      "Divide both sides by 2:\n",
      "\\[\n",
      "x = 0.05\n",
      "\\]\n",
      "\n",
      "**Answer:**\n",
      "The ball costs **5 cents** ($0.05).\n"
     ]
    }
   ],
   "source": [
    "# Ask it again\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-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": [
       "Let's denote the cost of the ball as \\( x \\) dollars.\n",
       "\n",
       "Given:\n",
       "- The bat costs \\( x + 1.00 \\) dollars.\n",
       "- The total cost of the bat and ball is $1.10.\n",
       "\n",
       "Set up the equation:\n",
       "\\[\n",
       "x + (x + 1.00) = 1.10\n",
       "\\]\n",
       "\n",
       "Simplify:\n",
       "\\[\n",
       "2x + 1.00 = 1.10\n",
       "\\]\n",
       "\n",
       "Subtract 1.00 from both sides:\n",
       "\\[\n",
       "2x = 0.10\n",
       "\\]\n",
       "\n",
       "Divide both sides by 2:\n",
       "\\[\n",
       "x = 0.05\n",
       "\\]\n",
       "\n",
       "**Answer:**\n",
       "The ball costs **5 cents** ($0.05)."
      ],
      "text/plain": [
       "<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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Many business areas can benefit from AI agents, including but not limited to:\n",
      "\n",
      "1. **Customer Service and Support**  \n",
      "   AI agents can handle customer inquiries, provide 24/7 support via chatbots, resolve issues quickly, and escalate complex problems to human agents.\n",
      "\n",
      "2. **Sales and Marketing**  \n",
      "   AI agents can personalize marketing campaigns, analyze customer data for targeted advertising, generate leads, and automate follow-ups.\n",
      "\n",
      "3. **Human Resources (HR)**  \n",
      "   AI can assist with resume screening, employee onboarding, answering HR-related queries, and managing performance evaluations.\n",
      "\n",
      "4. **Finance and Accounting**  \n",
      "   AI agents can automate invoicing, track expenses, detect fraud, perform financial forecasting, and assist in auditing.\n",
      "\n",
      "5. **Supply Chain and Logistics**  \n",
      "   AI can optimize inventory management, forecast demand, automate order processing, and manage delivery routes.\n",
      "\n",
      "6. **Healthcare**  \n",
      "   AI agents can assist in patient scheduling, provide preliminary diagnostics, support medical research, and manage health records.\n",
      "\n",
      "7. **IT Support and Cybersecurity**  \n",
      "   AI agents can automate routine IT tasks, monitor systems for anomalies, respond to security threats, and assist in troubleshooting.\n",
      "\n",
      "8. **Legal Services**  \n",
      "   AI can help with contract review, legal research, compliance monitoring, and document automation.\n",
      "\n",
      "9. **Manufacturing**  \n",
      "   AI can optimize production schedules, predict equipment maintenance needs, and monitor quality control.\n",
      "\n",
      "AI agents enhance efficiency, reduce costs, and improve customer experiences across these sectors by automating routine tasks and providing data-driven insights.\n"
     ]
    }
   ],
   "source": [
    "# First create the messages:\n",
    "\n",
    "messages = [{\"role\": \"user\", \"content\": \"What business area may benefit from AI agents?\"}]\n",
    "\n",
    "# Then make the first call:\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-mini\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "# Then read the business idea:\n",
    "business_idea = response.choices[0].message.content\n",
    "print(business_idea)\n",
    "# And repeat!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One significant pain point in healthcare that AI agents can help solve is **clinical documentation and administrative burden** on healthcare providers.\n",
      "\n",
      "### Explanation:\n",
      "Healthcare professionals often spend a large portion of their time—sometimes up to 50%—on documentation, data entry, and administrative tasks rather than direct patient care. This not only leads to burnout and job dissatisfaction but can also result in errors and less time spent on patients.\n",
      "\n",
      "### How AI Agents Can Help:\n",
      "- **Automated Clinical Documentation:** AI-powered speech recognition and natural language processing (NLP) can listen during patient visits and generate accurate clinical notes automatically, reducing the need for manual data entry.\n",
      "- **Intelligent Charting Assistants:** AI agents can summarize patient histories, lab results, and imaging reports to provide clinicians with concise, relevant information quickly.\n",
      "- **Administrative Task Automation:** Scheduling, billing, and compliance checks can be streamlined by AI systems, freeing up staff and reducing errors.\n",
      "\n",
      "By alleviating the administrative burden, AI agents allow healthcare providers to focus more on patient care, improve job satisfaction, and potentially lead to better health outcomes.\n"
     ]
    }
   ],
   "source": [
    "messages = [{\"role\": \"user\", \"content\": \"Whats a pain point in Healthcare that AI agents can solve?\"}]\n",
    "\n",
    "response = openai.chat.completions.create(\n",
    "    model=\"gpt-4.1-mini\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "answer = response.choices[0].message.content\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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