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{
"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": []
}
],
"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",
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"nbformat": 4,
"nbformat_minor": 2
}
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