AI Agents Lessons - Comprehensive Tutorial Series

🚀 Overview

Empowering the Future: Building Agentic AI Applications is a comprehensive, hands-on course that takes learners from foundational concepts of Large Language Models (LLMs) to building sophisticated multi-agent systems and deploying them into production. This repository contains practical coding exercises, examples, and projects using Python and LLM API endpoints.

📋 Table of Contents

• Prerequisites
• Quick Start
• Course Structure
• Installation
• Configuration
• Usage
• Features
• Contributing
• License

🔧 Prerequisites

Software Requirements

• Python: 3.9 or higher
• IDE: VS Code (recommended) or PyCharm Community Edition
• Docker Desktop: For containerization (required for production lessons)
• Git: For version control

Hardware Requirements

• RAM: 8GB minimum, 16GB recommended
• Storage: 50GB+ free space
• Internet: Stable connection for API calls

API Access

You'll need an API key from one of these providers:

• OpenAI API
• Google Gemini API
• Azure OpenAI Service

🚀 Quick Start

1. Clone the repository

   git clone <repository-url>
   cd ai-agents-lessons

2. Set up virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment variables

   cp .env.example .env
   # Edit .env with your API keys

5. Start with Lesson 1

   cd lesson_1_environment_setup
   # Follow the Environment-Setup.md guide

📚 Course Structure

Core Lessons

Lesson	Topic	Key Technologies	Duration
Lesson 1	Environment Setup	Python, Virtual Environments, API Configuration	1 hour
Lesson 2	Chatbot Basics	OpenAI API, Basic Prompting, Function Calling	2 hours
Lesson 3	RAG & Web Access	Brave Search API, Web Scraping, Information Retrieval	3 hours
Lesson 4	Text-to-SQL	SQLite, Natural Language to SQL, Database Integration	3 hours
Lesson 5	Document RAG	LlamaIndex, Vector Search, Document Processing	3 hours
Lesson 6	Multi-Agent Systems	Agent Orchestration, Inter-agent Communication	4 hours
Lesson 7	Evaluation Metrics	Performance Testing, Quality Metrics, Benchmarking	2 hours
Lesson 8	API Deployment	FastAPI, REST APIs, Service Architecture	3 hours
Lesson 9	Production Deployment	Docker, Cloud Deployment, Monitoring	4 hours

Additional Components

• Load Balancer: Azure OpenAI load balancing implementation
• Data: Sample datasets, databases, and test cases
• Utils: Shared utilities for API interactions, evaluation, and visualization

🛠 Installation

Method 1: Standard Installation

# Clone repository
git clone <repository-url>
cd ai-agents-lessons

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

Method 2: Using Conda

# Create conda environment
conda create -n ai-agents python=3.10
conda activate ai-agents

# Install dependencies
pip install -r requirements.txt

⚙️ Configuration

1. Create environment file

   cp .env.example .env

2. Add your API keys to .env

   OPENAI_API_KEY=your_openai_api_key_here
   BRAVE_SEARCH_API_KEY=your_brave_search_key_here
   AZURE_OPENAI_ENDPOINT=your_azure_endpoint
   AZURE_OPENAI_API_KEY=your_azure_key

3. Verify setup

   python -c "import openai; print('Setup successful!')"

💻 Usage

Running Jupyter Notebooks

# Start Jupyter Lab
jupyter lab

# Or Jupyter Notebook
jupyter notebook

Running Examples

# Lesson 3 example
cd lesson_3_rag_web_access/examples
python example_3.py

# Lesson 4 example
cd lesson_4_text_to_sql/examples
python example_4.py

Running the Multi-Agent Chatbot

cd multi_agent_chatbot
streamlit run app.py

Testing Load Balancer

cd load-balancer
python test_api.py

✨ Features

🤖 AI Agent Capabilities

• Web Search Agent: Real-time information retrieval using Brave Search
• SQL Agent: Natural language to SQL query conversion
• Document Agent: RAG-based document processing and Q&A
• Router Agent: Intelligent request routing to appropriate agents

🔧 Technical Features

• Function calling with OpenAI/Azure OpenAI
• Vector embeddings and semantic search
• Database integration (SQLite, SQL Server)
• Document processing (PDF, TXT, DOCX)
• API deployment with FastAPI
• Docker containerization
• Load balancing and scaling

📊 Evaluation & Monitoring

• Performance metrics and benchmarking
• Error handling and logging
• API monitoring and health checks
• Conversation memory management

🏗️ Project Structure

ai-agents-lessons/
├── 📁 lesson_1_environment_setup/    # Environment configuration
├── 📁 lesson_2_chatbot_basics/       # Basic chatbot implementation
├── 📁 lesson_3_rag_web_access/       # Web search and RAG
├── 📁 lesson_4_text_to_sql/          # SQL generation and execution
├── 📁 lesson_5_document_rag/         # Document processing
├── 📁 lesson_6_multi_agent_systems/  # Multi-agent orchestration
├── 📁 lesson_7_evaluation_metrics/   # Testing and evaluation
├── 📁 lesson_8_api_deployment/       # API development
├── 📁 lesson_9_production/           # Production deployment
├── 📁 load-balancer/                 # Azure OpenAI load balancer
├── 📁 data/                          # Sample data and databases
├── 📁 utils/                         # Shared utilities
├── 📄 requirements.txt               # Python dependencies
└── 📄 README.md                      # This file

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙋‍♂️ Support

• Issues: Report bugs or request features via GitHub Issues
• Documentation: Check individual lesson READMEs for detailed instructions
• Examples: Refer to the examples/ directories in each lesson

🌟 Acknowledgments

• OpenAI for GPT models and API
• LlamaIndex for RAG capabilities
• Brave Search for web search functionality
• The open-source AI community

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Ready to build the future with AI agents? Start with Lesson 1! 🚀