{ "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 read the README? Many common questions are answered here!
\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": 1, "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": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "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": "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": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OpenAI API Key exists and begins sk-97368\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('DEEPSEEK_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", "# Even for other LLM providers like Gemini, you still use this OpenAI import - see Guide 9 for why\n", "\n", "from openai import OpenAI" ] }, { "cell_type": "code", "execution_count": 10, "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(\n", " \n", " api_key=os.getenv(\"DEEPSEEK_API_KEY\"),\n", " base_url=os.getenv(\"DEEPSEEK_BASE_URL\")\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "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": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Let’s break it down: \n", "\n", "We start with the number **2**. \n", "Adding **2** more gives: \n", "\n", "\\[\n", "2 + 2 = 4\n", "\\]\n", "\n", "So, the answer is **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", "# If you get a NameError, head to the guides folder (guide 6) to learn about NameErrors - always instantly fixable\n", "\n", "response = openai.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": 13, "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 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": null, "metadata": {}, "outputs": [], "source": [ "# form a new messages list\n", "messages = [{\"role\": \"user\", \"content\": question}]\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "If 5+3=28, 9+1=810, 8+6=214, 5+4=19, then what is 7+3?\n" ] } ], "source": [ "# Ask it again\n", "\n", "response = openai.chat.completions.create(\n", " model=\"deepseek-reasoner\",\n", " messages=messages\n", ")\n", "\n", "answer = response.choices[0].message.content\n", "print(answer)\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "If 5+3=28, 9+1=810, 8+6=214, 5+4=19, then what is 7+3?" ], "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": 17, "metadata": {}, "outputs": [], "source": [ "# First create the messages:\n", "\n", "messages = [{\"role\": \"user\", \"content\": \"how to find a good job\"}]\n", "\n", "# Then make the first call:\n", "\n", "response = openai.chat.completions.create(\n", " model=\"deepseek-chat\",\n", " messages=messages\n", ")\n", "\n", "# Then read the business idea:\n", "\n", "business_idea = response.choices[0].message.content\n", "# And repeat! In the next message, include the business idea within the message" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Of course. Finding a good job is a strategic process, not a matter of luck. A \"good job\" is one that aligns with your skills, values, and lifestyle goals.\\n\\nHere is a comprehensive, step-by-step guide to finding a good job.\\n\\n### Step 1: Self-Reflection (The Most Important Step)\\n\\nYou can\\'t find the right destination without knowing your starting point. Before you even look at job listings, get clear on what you want.\\n\\n* **Skills & Strengths:** What are you genuinely good at? (e.g., analytical thinking, writing, public speaking, coding, project management). Use tools like the **Gallup StrengthsFinder** for insights.\\n* **Interests & Passions:** What kind of work gets you into a state of \"flow\"? What industries or topics excite you?\\n* **Values:** What is non-negotiable for you in a workplace? (e.g., work-life balance, high impact, innovation, job security, collaborative culture, remote flexibility).\\n* **Career Goals:** Where do you want to be in 3-5 years? What kind of experience do you need to get there?\\n* **Define \"Good Job\" for YOU:** Is it a high salary? Great benefits? A short commute? A supportive manager? A mission you believe in? Rank these in order of importance.\\n\\n**Output of this step:** A personal \"job spec\" that you can use to evaluate opportunities.\\n\\n---\\n\\n### Step 2: Prepare Your Materials\\n\\nYour resume, LinkedIn profile, and portfolio (if applicable) are your marketing tools.\\n\\n* **Tailor Your Resume:** Don\\'t use a generic resume for every job.\\n * **Keywords:** Carefully read the job description and mirror its language. Many companies use Applicant Tracking Systems (ATS) to scan for keywords.\\n * **Quantify Achievements:** Instead of \"Responsible for sales,\" write \"Increased sales in the region by 25% over 6 months.\"\\n * **Focus on Impact:** Show how you made a difference in your previous roles.\\n* **Optimize Your LinkedIn Profile:**\\n * Use a professional, friendly photo.\\n * Write a compelling headline that states your value, not just your job title (e.g., \"Marketing Manager | Driving Growth Through Data-Driven SEO & Content Strategy\").\\n * Your \"About\" section should tell your story and incorporate your keywords.\\n * Detail your experience and ask for recommendations.\\n* **Prepare Your Portfolio:** For creative, tech, or project-based roles, a portfolio (on a personal website, GitHub, Behance, etc.) is essential. Showcase your best work.\\n\\n---\\n\\n### Step 3: The Job Search Strategy\\n\\nCast a wide net, but do it strategically.\\n\\n* **Leverage Your Network (The #1 Most Effective Method):**\\n * Let people in your network know you\\'re looking. Be specific about the kind of role you\\'re targeting.\\n * Reach out for \"informational interviews\" with people in companies or roles that interest you. The goal is to learn, not to ask for a job directly.\\n * **Statistically, a huge percentage of jobs are filled through networking and referrals.**\\n\\n* **Use Job Boards Intelligently:**\\n * **General Boards:** Indeed, LinkedIn Jobs, Glassdoor.\\n * **Niche Boards:** For specific industries (e.g., Dice for tech, Idealist for non-profits, Mediabistro for media).\\n * **Company Career Pages:** If you have a dream company, go directly to their website and sign up for job alerts.\\n\\n* **Work with Recruiters:**\\n * Connect with recruiters on LinkedIn who specialize in your industry.\\n * Good recruitment agencies can provide access to unlisted opportunities.\\n\\n---\\n\\n### Step 4: The Application & Interview Process\\n\\nThis is where you convert opportunities into offers.\\n\\n* **The Customized Cover Letter:** If you\\'re applying for a role you\\'re truly excited about, write a cover letter. Explain *why* you are interested in *that specific company* and *that specific role*, and connect your experience to their needs.\\n* **Ace the Interview:**\\n * **Research:** Know the company, its products, its competitors, its culture, and your interviewers.\\n * **Prepare Stories:** Use the **STAR method** (Situation, Task, Action, Result) to structure answers to behavioral questions (\"Tell me about a time when...\").\\n * **Prepare Your Own Questions:** This is critical. Ask about:\\n * \"What does success look like in this role in the first 6 months?\"\\n * \"Can you describe the team culture?\"\\n * \"What are the biggest challenges someone in this position would face?\"\\n * **Practice Aloud:** Do mock interviews with a friend or record yourself.\\n\\n---\\n\\n### Step 5: Evaluate the Offer & Company\\n\\nYou\\'ve got an offer! Now, make sure the job is as \"good\" as it seems.\\n\\n* **Analyze the Compensation Package:**\\n * Look beyond the base salary. Consider bonuses, equity, health insurance, retirement plans (401k match), PTO, and other perks.\\n* **Assess the Company Culture:**\\n * Use sites like Glassdoor and Blind for reviews, but take them with a grain of salt (often only the very satisfied or very dissatisfied post).\\n * Your impressions from the interview are key. Did people seem happy? Stressed? Collaborative?\\n* **Consider Your Potential Manager:** Your relationship with your direct manager is one of the biggest factors in job satisfaction. Did they seem supportive, clear, and like someone you could learn from?\\n* **Evaluate Growth Opportunities:** Are there clear paths for advancement or professional development?\\n\\n---\\n\\n### Step 6: Negotiate and Accept\\n\\nYou almost always have room to negotiate.\\n\\n* **Know Your Worth:** Use sites like **Salary.com, Levels.fyi (for tech), and Glassdoor** to research salary ranges for that role in that location.\\n* **Negotiate Professionally:** Be polite and frame your request around the value you bring to the company.\\n* **Negotiate More Than Salary:** If the salary is fixed, you can often negotiate for a signing bonus, more vacation days, remote work flexibility, or professional development funds.\\n\\n### Final Checklist for a \"Good Job\":\\n\\n* [ ] **The Work:** It uses my strengths and interests me.\\n* [ ] **The People:** The manager seems supportive and the team seems collaborative.\\n* [ ] **The Company:** The culture and mission align with my values.\\n* [ ] **The Compensation:** The total package (salary, benefits, etc.) meets my financial needs.\\n* [ ] **The Growth:** There are opportunities to learn and advance my career.\\n* [ ] **The Lifestyle:** The commute/hours/work model (remote/hybrid/onsite) fits my life.\\n\\nFinding a good job is a marathon, not a sprint. Stay organized, be persistent, and don\\'t settle for a role that doesn\\'t feel right. Good luck'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "business_idea" ] }, { "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": "3.12.11" } }, "nbformat": 4, "nbformat_minor": 2 }