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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# First Agentic AI workflow with OPENAI"
]
},
{
"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/muhammad-mudassar-a65645192/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import re\n",
"from openai import OpenAI\n",
"from dotenv import load_dotenv\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"load_dotenv(override=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"openai_api_key=os.getenv(\"OPENAI_API_KEY\")\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 gui\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Workflow with OPENAI"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"openai=OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"message = [{'role':'user','content':\"what is 2+3?\"}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = openai.chat.completions.create(model=\"gpt-4o-mini\",messages=message)\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"question = \"Please propose a hard, challenging question to assess someone's IQ. Respond only with the question.\"\n",
"message=[{'role':'user','content':question}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response=openai.chat.completions.create(model=\"gpt-4o-mini\",messages=message)\n",
"question=response.choices[0].message.content\n",
"print(f\"Answer: {question}\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"message=[{'role':'user','content':question}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response=openai.chat.completions.create(model=\"gpt-4o-mini\",messages=message)\n",
"answer = response.choices[0].message.content\n",
"print(f\"Answer: {answer}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# convert \\[ ... \\] to $$ ... $$, to properly render Latex\n",
"converted_answer = re.sub(r'\\\\[\\[\\]]', '$$', answer)\n",
"display(Markdown(converted_answer))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise"
]
},
{
"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",
" <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": 42,
"metadata": {},
"outputs": [],
"source": [
"message = [{'role':'user','content':\"give me a business area related to ecommerce that might be worth exploring for a agentic opportunity.\"}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = openai.chat.completions.create(model=\"gpt-4o-mini\",messages=message)\n",
"business_area = response.choices[0].message.content\n",
"business_area"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"message = business_area + \"present a pain-point in that industry - something challenging that might be ripe for an agentic solutions.\"\n",
"message"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"message = [{'role': 'user', 'content': message}]\n",
"response = openai.chat.completions.create(model=\"gpt-4o-mini\",messages=message)\n",
"question=response.choices[0].message.content\n",
"question"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"message=[{'role':'user','content':question}]\n",
"response=openai.chat.completions.create(model=\"gpt-4o-mini\",messages=message)\n",
"answer=response.choices[0].message.content\n",
"print(answer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display(Markdown(answer))"
]
}
],
"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": "3.12.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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