{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Agentic AI workflow with Groq and Llama-3.3 LLM(Free of cost) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First let's do an import\n",
    "from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next it's time to load the API keys into environment variables\n",
    "\n",
    "load_dotenv(override=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the Groq API key\n",
    "\n",
    "import os\n",
    "groq_api_key = os.getenv('GROQ_API_KEY')\n",
    "\n",
    "if groq_api_key:\n",
    "    print(f\"GROQ API Key exists and begins {groq_api_key[:8]}\")\n",
    "else:\n",
    "    print(\"GROQ API Key not set\")\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 groq import Groq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Groq instance\n",
    "groq = Groq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of messages in the familiar Groq format\n",
    "\n",
    "messages = [{\"role\": \"user\", \"content\": \"What is 2+2?\"}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now call it!\n",
    "\n",
    "response = groq.chat.completions.create(model='llama-3.3-70b-versatile', messages=messages)\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ask it\n",
    "response = groq.chat.completions.create(\n",
    "    model=\"llama-3.3-70b-versatile\",\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ask it again\n",
    "\n",
    "response = groq.chat.completions.create(\n",
    "    model=\"llama-3.3-70b-versatile\",\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "answer = response.choices[0].message.content\n",
    "print(answer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Markdown, display\n",
    "\n",
    "display(Markdown(answer))\n",
    "\n"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First create the messages:\n",
    "\n",
    "messages = [{\"role\": \"user\", \"content\": \"Give me a business area that might be ripe for an Agentic AI solution.\"}]\n",
    "\n",
    "# Then make the first call:\n",
    "\n",
    "response = groq.chat.completions.create(model='llama-3.3-70b-versatile', messages=messages)\n",
    "\n",
    "# Then read the business idea:\n",
    "\n",
    "business_idea = response.choices[0].message.content\n",
    "\n",
    "\n",
    "# And repeat!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "display(Markdown(business_idea))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update the message with the business idea from previous step\n",
    "messages = [{\"role\": \"user\", \"content\": \"What is the pain point in the business area of \" + business_idea + \"?\"}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make the second call\n",
    "response = groq.chat.completions.create(model='llama-3.3-70b-versatile', messages=messages)\n",
    "# Read the pain point\n",
    "pain_point = response.choices[0].message.content\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(Markdown(pain_point))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make the third call\n",
    "messages = [{\"role\": \"user\", \"content\": \"What is the Agentic AI solution for the pain point of \" + pain_point + \"?\"}]\n",
    "response = groq.chat.completions.create(model='llama-3.3-70b-versatile', messages=messages)\n",
    "# Read the agentic solution\n",
    "agentic_solution = response.choices[0].message.content\n",
    "display(Markdown(agentic_solution))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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