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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Implementing Notebook 1 using various LLMs via Groq"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from dotenv import load_dotenv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"load_dotenv(override=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"groq_api_key = os.getenv('GROQ_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",
"if groq_api_key:\n",
" print(f\"Groq API Key exists and begins {groq_api_key[:2]}\")\n",
"else:\n",
" print(\"Groq API Key not set - please head to the troubleshooting guide in the setup folder\")\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"openai = OpenAI(\n",
" base_url=\"https://api.groq.com/openai/v1\",\n",
" api_key=groq_api_key\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"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}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# openai/gpt-oss-120b\n",
"\n",
"response = openai.chat.completions.create(\n",
" model=\"openai/gpt-oss-120b\",\n",
" messages=messages\n",
")\n",
"\n",
"print(response.choices[0].message.content)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# moonshotai/kimi-k2-instruct\n",
"\n",
"response = openai.chat.completions.create(\n",
" model=\"moonshotai/kimi-k2-instruct\",\n",
" messages=messages\n",
")\n",
"\n",
"question = response.choices[0].message.content\n",
"\n",
"print(question)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# form a new messages list\n",
"messages = [{\"role\": \"user\", \"content\": question}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Ask meta-llama/llama-guard-4-12b\n",
"\n",
"response = openai.chat.completions.create(\n",
" model=\"llama-3.1-8b-instant\",\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(question))\n",
"display(Markdown(answer))"
]
},
{
"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. <br/>\n",
" We will cover this at up-coming labs, so don't worry if you're unsure.. just give it a try!\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# First create the messages:\n",
"\n",
"messages = [{\"role\": \"user\", \"content\": \"Pick a business area that is worth exploring for a Gen-Z audience, that can be an agentic-ai opportunity. \\\n",
" Somehwere where the concept of agentisation can be applied commerically. Respond only with the business idea.\"}]\n",
"\n",
"# Then make the first call: \n",
"\n",
"response = openai.chat.completions.create(\n",
" model = \"qwen/qwen3-32b\",\n",
" messages = messages\n",
")\n",
"\n",
"# Then read the business idea:\n",
"\n",
"business_idea = response.choices[0].message.content\n",
"print(business_idea)\n",
"\n",
"# And repeat! In the next message, include the business idea within the message\n",
"\n",
"user_prompt_pain_point = f\"What is the pain point of the Gen-Z audience in the business area of {business_idea}?, that can be solved by an agentic-ai solution? Give a brief answer\"\n",
"\n",
"response = openai.chat.completions.create(\n",
" model = \"gemma2-9b-it\",\n",
" messages = [{\"role\": \"user\", \"content\": user_prompt_pain_point}]\n",
")\n",
"\n",
"pain_point = response.choices[0].message.content\n",
"print(pain_point)\n",
"\n",
"user_prompt_solution = f\"What is the solution to the pain point {pain_point} of the Gen-Z audience in the business area of {business_idea}?, that can be solved by an agentic-ai solution? Provide a step-by-step breakdown\"\n",
"\n",
"response = openai.chat.completions.create(\n",
" model = \"deepseek-r1-distill-llama-70b\",\n",
" messages = [{\"role\": \"user\", \"content\": user_prompt_solution}]\n",
")\n",
"\n",
"business_solution = response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display(Markdown(business_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",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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