{
"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!"
]
},
{
"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": 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 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": null,
"metadata": {},
"outputs": [],
"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": null,
"metadata": {},
"outputs": [],
"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": null,
"metadata": {},
"outputs": [],
"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": null,
"metadata": {},
"outputs": [],
"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": "markdown",
"metadata": {},
"source": [
"```\n",
"# First create the messages:\n",
"\n",
"messages = [{\"role\": \"user\", \"content\": \"Something here\"}]\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",
"# Then read the business idea:\n",
"\n",
"business_idea = response.choices[0].message.content\n",
"\n",
"# print(business_idea) \n",
"\n",
"# And repeat!\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# First exercice : ask the LLM to pick a business area that might be worth exploring for an Agentic AI opportunity.\n",
"\n",
"# First create the messages:\n",
"query = \"Pick a business area that might be worth exploring for an Agentic AI opportunity.\"\n",
"messages = [{\"role\": \"user\", \"content\": query}]\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",
"# Then read the business idea:\n",
"\n",
"business_idea = response.choices[0].message.content\n",
"\n",
"# print(business_idea) \n",
"\n",
"# from IPython.display import Markdown, display\n",
"\n",
"display(Markdown(business_idea))\n",
"\n",
"# And repeat!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Second exercice: Then ask the LLM to present a pain-point in that industry - something challenging that might be ripe for an Agentic solution.\n",
"\n",
"# First create the messages:\n",
"\n",
"prompt = f\"Please present a pain-point in that industry, something challenging that might be ripe for an Agentic solution for it in that industry: {business_idea}\"\n",
"messages = [{\"role\": \"user\", \"content\": prompt}]\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",
"# Then read the business idea:\n",
"\n",
"painpoint = response.choices[0].message.content\n",
" \n",
"# print(painpoint) \n",
"display(Markdown(painpoint))\n",
"\n",
"# And repeat!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# third exercice: Finally have 3 third LLM call propose the Agentic AI solution.\n",
"\n",
"# First create the messages:\n",
"\n",
"promptEx3 = f\"Please come up with a proposal for the Agentic AI solution to address this business painpoint: {painpoint}\"\n",
"messages = [{\"role\": \"user\", \"content\": promptEx3}]\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",
"# Then read the business idea:\n",
"\n",
"ex3_answer=response.choices[0].message.content\n",
"# print(painpoint) \n",
"display(Markdown(ex3_answer))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
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