{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Welcome to the start of your adventure in Agentic AI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Are you ready for action??

\n", " Have you completed all the setup steps in the setup folder?
\n", " Have you checked out the guides in the guides folder?
\n", " Well in that case, you're ready!!\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

This code is a live resource - keep an eye out for my updates

\n", " I push updates regularly. As people ask questions or have problems, I add more examples and improve explanations. As a result, the code below might not be identical to the videos, as I've added more steps and better comments. Consider this like an interactive book that accompanies the lectures.

\n", " I try to send emails regularly with important updates related to the course. You can find this in the 'Announcements' section of Udemy in the left sidebar. You can also choose to receive my emails via your Notification Settings in Udemy. I'm respectful of your inbox and always try to add value with my emails!\n", " \n", "
" ] }, { "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/eddonner/\n", "\n", "\n", "### New to Notebooks like this one? Head over to the guides folder!\n", "\n", "Just to check you've already added the Python and Jupyter extensions to Cursor, if not already installed:\n", "- Open extensions (View >> extensions)\n", "- Search for python, and when the results show, click on the ms-python one, and Install it if not already installed\n", "- Search for jupyter, and when the results show, click on the Microsoft one, and Install it if not already installed \n", "Then View >> Explorer to bring back the File Explorer.\n", "\n", "And then:\n", "1. Click where it says \"Select Kernel\" near the top right, and select the option called `.venv (Python 3.12.9)` or similar, which should be the first choice or the most prominent choice. You may need to choose \"Python Environments\" first.\n", "2. Click in each \"cell\" below, starting with the cell immediately below this text, and press Shift+Enter to run\n", "3. Enjoy!\n", "\n", "After you click \"Select Kernel\", if there is no option like `.venv (Python 3.12.9)` then please do the following: \n", "1. On Mac: From the Cursor menu, choose Settings >> VS Code Settings (NOTE: be sure to select `VSCode Settings` not `Cursor Settings`); \n", "On Windows PC: From the File menu, choose Preferences >> VS Code Settings(NOTE: be sure to select `VSCode Settings` not `Cursor Settings`) \n", "2. In the Settings search bar, type \"venv\" \n", "3. In the field \"Path to folder with a list of Virtual Environments\" put the path to the project root, like C:\\Users\\username\\projects\\agents (on a Windows PC) or /Users/username/projects/agents (on Mac or Linux). \n", "And then try again.\n", "\n", "Having problems with missing Python versions in that list? Have you ever used Anaconda before? It might be interferring. Quit Cursor, bring up a new command line, and make sure that your Anaconda environment is deactivated: \n", "`conda deactivate` \n", "And if you still have any problems with conda and python versions, it's possible that you will need to run this too: \n", "`conda config --set auto_activate_base false` \n", "and then from within the Agents directory, you should be able to run `uv python list` and see the Python 3.12 version." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# First let's do an import\n", "from dotenv import load_dotenv\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Next it's time to load the API keys into environment variables\n", "\n", "load_dotenv(override=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Wait, did that just output `False`??\n", "\n", "If so, the most common reason is that you didn't save your `.env` file after adding the key! Be sure to have saved.\n", "\n", "Also, make sure the `.env` file is named precisely `.env` and is in the project root directory (`agents`)\n", "\n", "By the way, your `.env` file should have a stop symbol next to it in Cursor on the left, and that's actually a good thing: that's Cursor saying to you, \"hey, I realize this is a file filled with secret information, and I'm not going to send it to an external AI to suggest changes, because your keys should not be shown to anyone else.\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Final reminders

\n", " 1. If you're not confident about Environment Variables or Web Endpoints / APIs, please read Topics 3 and 5 in this technical foundations guide.
\n", " 2. If you want to use AIs other than OpenAI, like Gemini, DeepSeek or Ollama (free), please see the first section in this AI APIs guide.
\n", " 3. If you ever get a Name Error in Python, you can always fix it immediately; see the last section of this Python Foundations guide and follow both tutorials and exercises.
\n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OpenAI API Key exists and begins sk-proj-\n" ] } ], "source": [ "# Check the key - if you're not using OpenAI, check whichever key you're using! Ollama doesn't need a key.\n", "\n", "import os\n", "openai_api_key = os.getenv('OPENAI_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" ] }, { "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 in the Setup folder\n", "# If you get a Name Error - head over to Python Foundations guide (guide 6)\n", "\n", "from openai import OpenAI" ] }, { "cell_type": "code", "execution_count": 5, "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 OpenAI, you just need to slightly modify this - precise instructions are in the AI APIs guide (guide 9)\n", "\n", "openai = OpenAI()" ] }, { "cell_type": "code", "execution_count": 6, "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": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 + 2 equals 4.\n" ] } ], "source": [ "# And now call it! Any problems, head to the troubleshooting guide\n", "# This uses GPT 4.1 nano, the incredibly cheap model\n", "# The APIs guide (guide 9) has exact instructions for using even cheaper or free alternatives to OpenAI\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-nano\",\n", " messages=messages\n", ")\n", "\n", "print(response.choices[0].message.content)\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'role': 'user', 'content': \"Please propose a hard, challenging question to assess someone's IQ. Respond only with the question.\"}]\n" ] } ], "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", "print(messages)\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "If seven people can complete a task in five hours working together, and fourteen people can complete the same task in two hours working together, how long would it take for ten people to complete the task working at the same rate?\n" ] } ], "source": [ "# ask it - this uses GPT 4.1 mini, still cheap but more powerful than nano\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-mini\",\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": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "If two trains start from the same point but travel in different directions—one heading north at 60 mph and the other heading east at 80 mph—how far apart will they be after 3 hours? Now, if the northbound train increases its speed by 10 mph every hour and the eastbound train decreases its speed by 5 mph every hour, what will be the distance between the two trains after 3 hours?\n" ] } ], "source": [ "# Ask it again\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4.1-mini\",\n", " messages=messages\n", ")\n", "\n", "answer = response.choices[0].message.content\n", "print(answer)\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "Let's analyze the problem:\n", "\n", "- Five machines take five minutes to make five widgets.\n", "- This implies that **each machine makes one widget in five minutes**.\n", "\n", "Therefore:\n", "\n", "- One machine makes 1 widget in 5 minutes.\n", "- So, 100 machines working simultaneously will each make 1 widget in 5 minutes.\n", "- Therefore, 100 machines make 100 widgets in **5 minutes**.\n", "\n", "**Answer:** 5 minutes." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import Markdown, display\n", "\n", "display(Markdown(answer))\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Congratulations!\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": 28, "metadata": {}, "outputs": [], "source": [ "# First create the messages:\n", "\n", "messages = [{\"role\": \"user\", \"content\": \"Something here\"}]\n", "\n", "# Then make the first call:\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4o-mini\",\n", " messages=messages,\n", ")\n", "\n", "# Then read the business idea:\n", "\n", "business_idea = response.choices[0].message.content\n", "\n", "# And repeat! In the next message, include the business idea within the message\n", "\n", "messages = [{\"role\": \"user\", \"content\": f\"Here's a business idea: {business_idea}. Pick a pain point related to this business idea.\"}]\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4o-mini\",\n", " messages=messages,\n", ")\n", "\n", "pain_point = response.choices[0].message.content\n", "\n", "# Finally propose a solution to the pain point\n", "\n", "messages = [{\"role\": \"user\", \"content\": f\"Here's a pain point: {pain_point}. Propose a solution to this pain point.\"}]\n", "\n", "response = openai.chat.completions.create(\n", " model=\"gpt-4o\",\n", " messages=messages,\n", ")\n", "\n", "solution = response.choices[0].message.content" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "To effectively address the pain point of \"Lack of Time for Meal Preparation,\" a comprehensive solution would involve creating a subscription-based meal kit service designed specifically for busy professionals. Here’s a detailed proposal to tackle this issue:\n", "\n", "### Solution Proposal:\n", "\n", "1. **Subscription Tiers:**\n", " - Offer different subscription plans (e.g., weekly, bi-weekly) allowing customers to choose how often they want deliveries based on their schedules and needs.\n", "\n", "2. **Streamlined Meal Selection:**\n", " - Implement a user-friendly online platform where customers can easily select and modify their meal choices each week, with recommendations based on past selections and dietary goals.\n", " \n", "3. **Time-Saving Innovation:**\n", " - Include ready-to-cook components like pre-chopped vegetables and partially prepared sauces to further reduce prep time. Provide meals that can be cooked in one pot or pan to simplify cleanup.\n", "\n", "4. **Flexible Delivery Options:**\n", " - Provide options for specific delivery days and time windows, ensuring that customers can receive their kits at their convenience, even if they are not home.\n", "\n", "5. **AI-Personalized Meal Suggestions:**\n", " - Use AI technology to analyze customers’ preferences and provide personalized meal kit suggestions to make the decision process faster and more tailored to their tastes.\n", "\n", "6. **Health and Dietary Integration:**\n", " - Collaborate with nutritionists to offer meal plans aligned with specific health goals (e.g., weight loss, muscle gain) and integrate with fitness apps to track nutritional intake directly.\n", "\n", "7. **Sustainability Commitment:**\n", " - Utilize minimal, recyclable, or biodegradable packaging and provide a simple way for customers to return packaging for reuse to minimize environmental impact.\n", "\n", "8. **Community and Engagement:**\n", " - Build an online community platform where users can share experiences, recipe adjustments, and cooking tips. Offer webinars or live cooking demonstrations to foster engagement and learning.\n", "\n", "9. **Value-Added Services:**\n", " - Offer occasional surprises like free samples of new products or exclusive discounts with partner health brands to enhance customer satisfaction and loyalty.\n", "\n", "By providing a service that seamlessly fits into the hectic lifestyles of busy professionals while promoting healthy eating and sustainability, this meal kit service can effectively address the pain point and offer a comprehensive solution.\n" ] } ], "source": [ "print(solution)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "To effectively address the pain point of \"Lack of Time for Meal Preparation,\" a comprehensive solution would involve creating a subscription-based meal kit service designed specifically for busy professionals. Here’s a detailed proposal to tackle this issue:\n", "\n", "### Solution Proposal:\n", "\n", "1. **Subscription Tiers:**\n", " - Offer different subscription plans (e.g., weekly, bi-weekly) allowing customers to choose how often they want deliveries based on their schedules and needs.\n", "\n", "2. **Streamlined Meal Selection:**\n", " - Implement a user-friendly online platform where customers can easily select and modify their meal choices each week, with recommendations based on past selections and dietary goals.\n", " \n", "3. **Time-Saving Innovation:**\n", " - Include ready-to-cook components like pre-chopped vegetables and partially prepared sauces to further reduce prep time. Provide meals that can be cooked in one pot or pan to simplify cleanup.\n", "\n", "4. **Flexible Delivery Options:**\n", " - Provide options for specific delivery days and time windows, ensuring that customers can receive their kits at their convenience, even if they are not home.\n", "\n", "5. **AI-Personalized Meal Suggestions:**\n", " - Use AI technology to analyze customers’ preferences and provide personalized meal kit suggestions to make the decision process faster and more tailored to their tastes.\n", "\n", "6. **Health and Dietary Integration:**\n", " - Collaborate with nutritionists to offer meal plans aligned with specific health goals (e.g., weight loss, muscle gain) and integrate with fitness apps to track nutritional intake directly.\n", "\n", "7. **Sustainability Commitment:**\n", " - Utilize minimal, recyclable, or biodegradable packaging and provide a simple way for customers to return packaging for reuse to minimize environmental impact.\n", "\n", "8. **Community and Engagement:**\n", " - Build an online community platform where users can share experiences, recipe adjustments, and cooking tips. Offer webinars or live cooking demonstrations to foster engagement and learning.\n", "\n", "9. **Value-Added Services:**\n", " - Offer occasional surprises like free samples of new products or exclusive discounts with partner health brands to enhance customer satisfaction and loyalty.\n", "\n", "By providing a service that seamlessly fits into the hectic lifestyles of busy professionals while promoting healthy eating and sustainability, this meal kit service can effectively address the pain point and offer a comprehensive solution." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import Markdown, display\n", "\n", "display(Markdown(response.choices[0].message.content))" ] }, { "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 2 }