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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Submission for Week 1 Tasks"
]
},
{
"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/ian-kisali/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# First let's do an import. If you get an Import Error, double check that your Kernel is correct..\n",
"\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",
"# If this returns false, see the next cell!\n",
"\n",
"load_dotenv(override=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check the key - if you're not using DeepSeek, check whichever key you're using! Ollama doesn't need a key.\n",
"\n",
"import os\n",
"deepseek_api_key = os.getenv('DEEPSEEK_API_KEY')\n",
"\n",
"if deepseek_api_key:\n",
" print(f\"DeepSeek API Key exists and begins {deepseek_api_key[:8]}\")\n",
"else:\n",
" print(\"DeepSeek API Key not set - please head to the troubleshooting guide in the setup folder\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# And now - the all important import statement\n",
"# If you get an import error - head over to troubleshooting in the Setup folder\n",
"\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"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 (guide 6)!\n",
"# If you get a NameError - head over to the guides folder (guide 6)to learn about NameErrors - always instantly fixable\n",
"# If you're not using DeepSeek, you just need to slightly modify this - precise instructions are in the AI APIs guide (guide 9)\n",
"\n",
"deepseek_client = OpenAI(api_key=deepseek_api_key, base_url=\"https://api.deepseek.com\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Models existing in DeepSeek\n",
"print(deepseek_client.models.list())"
]
},
{
"cell_type": "code",
"execution_count": null,
"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",
"# This uses deepseek-chat, the incredibly cheap model\n",
"# If you get a NameError, head to the guides folder (guide 6) to learn about NameErrors - always instantly fixable\n",
"\n",
"response = deepseek_client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=messages\n",
")\n",
"\n",
"print(response.choices[0].message.content)\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}]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ask it - this uses deepseek-chat, the incredibly cheap model\n",
"\n",
"response = deepseek_client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=messages\n",
")\n",
"\n",
"question = response.choices[0].message.content\n",
"\n",
"print(question)"
]
},
{
"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 it again\n",
"response = deepseek_client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=messages\n",
")\n",
"\n",
"answer = response.choices[0].message.content\n",
"print(answer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Markdown, display\n",
"\n",
"display(Markdown(answer))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 1 Business Idea Submission\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. <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 and first call for picking business ideas:\n",
"question = \"Pick a business idea that might be ripe for an Agentic AI solution. The idea should be challenging and interesting and focusing on DevOps or SRE.\"\n",
"messages = [{\"role\": \"user\", \"content\": question}]\n",
"\n",
"response = deepseek_client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=messages\n",
")\n",
"business_ideas = response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# LLM call 2 to get the pain point in the business idea that might be ripe for an Agentic solution\n",
"pain_point_question = f\"Present a pain-point in the {business_ideas} - something challenging that might be ripe for an Agentic solution.\"\n",
"messages = [{\"role\": \"user\", \"content\": pain_point_question}]\n",
"\n",
"response = deepseek_client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=messages\n",
")\n",
"pain_point = response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# LLM Call 3 to propose the exact Agentic AI Solution\n",
"business_idea = f\"The business idea is {business_ideas} and the pain point is {pain_point}. Please propose an Agentic AI solution to the pain point. Respond only with the solution.\"\n",
"messages = [{\"role\": \"user\", \"content\": business_idea}]\n",
"\n",
"response = deepseek_client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=messages\n",
")\n",
"\n",
"agentic_ai_solution = response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(agentic_ai_solution)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display(Markdown(agentic_ai_solution))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
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