{ "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": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Exercise

\n", " Now try this commercial application:
\n", " First ask the LLM to pick a business area that might be worth exploring for an Agentic AI opportunity.
\n", " Then ask the LLM to present a pain-point in that industry - something challenging that might be ripe for an Agentic solution.
\n", " Finally have 3 third LLM call propose the Agentic AI solution.
\n", " We will cover this at up-coming labs, so don't worry if you're unsure.. just give it a try!\n", " \n", "
" ] }, { "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": [] } ], "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": "3.12.1" } }, "nbformat": 4, "nbformat_minor": 2 }