├── images └── banner.png ├── Lectures └── 01 Introduction to Agentic AI │ ├── images │ ├── agent.jpg │ ├── agent.webp │ ├── banner.png │ ├── basic-llm.png │ ├── agent-memory.png │ ├── llm-timeline.png │ ├── memory-types.png │ ├── transformers.png │ ├── context-window.png │ ├── llm-rag-agent.webp │ ├── word-embedding.jpg │ ├── encoder-decoder.png │ ├── working-of-agentic-ai.webp │ └── thought-action-observation.png │ ├── 2. Understanding Agent Architecture.ipynb │ ├── 3. Introduction to LlamaIndex Framework.ipynb │ └── 1. 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For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | .DS_Store 162 | 163 | test.ipynb 164 | convert_md_to_notebook.py 165 | Lectures/**/*.md 166 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ![GitHub last commit](https://img.shields.io/github/last-commit/pytopia/agentic-ai) 4 | ![GitHub repo size](https://img.shields.io/github/repo-size/pytopia/agentic-ai) 5 | ![GitHub code size in bytes](https://img.shields.io/github/languages/code-size/pytopia/agentic-ai) 6 | ![GitHub Repo stars](https://img.shields.io/github/stars/pytopia/agentic-ai) 7 | ![GitHub top language](https://img.shields.io/github/languages/top/pytopia/agentic-ai) 8 | [![Website](https://img.shields.io/badge/Visit-Website-blue)](https://www.pytopia.ai) 9 | [![Telegram](https://img.shields.io/badge/Join-Telegram-blue)](https://t.me/pytopia_ai) 10 | [![Instagram](https://img.shields.io/badge/Follow-Instagram-red)](https://instagram.com/pytopia.ai) 11 | [![YouTube](https://img.shields.io/badge/Subscribe-YouTube-red)](https://www.youtube.com/c/pytopia) 12 | [![LinkedIn](https://img.shields.io/badge/Follow-LinkedIn-blue)](https://linkedin.com/company/pytopia) 13 | [![Twitter](https://img.shields.io/badge/Follow-Twitter-blue)](https://twitter.com/pytopia_ai) 14 | 15 | Welcome to our comprehensive **Agentic AI with LlamaIndex** Course Repository. This cutting-edge course was designed for AI enthusiasts, developers, and professionals who want to master the art of building autonomous AI agents. From understanding the foundational concepts to deploying production-ready agentic systems, this course provides a complete journey into the world of autonomous artificial intelligence. 16 | 17 | Our course is meticulously structured into eleven comprehensive modules: 18 | 19 | - **Introduction to Agentic AI**: Understanding the evolution from LLMs to autonomous agents, core agent architectures, and the LlamaIndex framework. 20 | - **Fundamentals of LlamaIndex**: Deep dive into LlamaIndex components, LLM integration, data ingestion pipelines, and vector embeddings. 21 | - **Building Blocks of AI Agents**: Mastering tools, function calling, agent types, memory management, and human-in-the-loop patterns. 22 | - **Designing Agent Workflows**: Creating sophisticated workflows with branching, loops, concurrent execution, and comprehensive observability. 23 | - **Knowledge Retrieval for Agents**: Building RAG pipelines, advanced retrieval techniques, agentic RAG patterns, and vector database integration. 24 | - **Tool Use and Function Calling**: Advanced tool creation, multi-tool coordination, and external API integration for enhanced agent capabilities. 25 | - **Conversational Agents with LlamaIndex**: Developing chat engines, conversation management, and deploying conversational systems at scale. 26 | - **Multi-Agent Systems**: Architecting systems with multiple agents, coordination patterns, and advanced multi-agent workflows. 27 | - **Agent Evaluation and Testing**: Comprehensive evaluation frameworks, performance analysis, and quality assurance for production agents. 28 | - **Advanced Agent Architectures**: Specialized agents for structured data extraction, code generation, and domain-specific applications. 29 | - **Capstone Project**: Building a full-featured AI agent from planning to deployment, integrating all course concepts. 30 | 31 | ## 🤖 What You'll Build 32 | 33 | Throughout this course, you'll develop practical skills by building: 34 | 35 | - **Autonomous Research Agents** that can investigate topics and compile comprehensive reports 36 | - **Customer Service Agents** capable of handling complex multi-step inquiries 37 | - **Data Analysis Agents** that can process, analyze, and visualize information 38 | - **Code Review Agents** for automated code quality assessment 39 | - **Multi-Agent Systems** that coordinate to solve complex problems 40 | - **Production-Ready Agents** with proper monitoring, evaluation, and deployment 41 | 42 | ## 🎯 Learning Outcomes 43 | 44 | By the end of this course, you will: 45 | 46 | - ✅ Master the LlamaIndex framework for building sophisticated AI agents 47 | - ✅ Understand the historical evolution and theoretical foundations of agentic AI 48 | - ✅ Implement the Thought-Action-Observation cycle in real applications 49 | - ✅ Build agents that can use tools, access external APIs, and interact with databases 50 | - ✅ Design and deploy multi-agent systems for complex problem-solving 51 | - ✅ Create production-ready agents with proper evaluation and monitoring 52 | - ✅ Apply best practices for agent security, reliability, and scalability 53 | 54 | ## 📋 Prerequisites 55 | 56 | - **Python Programming**: Solid understanding of Python (intermediate level) 57 | - **Basic AI/ML Knowledge**: Familiarity with machine learning concepts 58 | - **API Experience**: Understanding of REST APIs and web services 59 | - **Command Line**: Comfortable with terminal/command line operations 60 | 61 | ## 🛠️ Technologies Covered 62 | 63 | - **LlamaIndex**: Primary framework for building AI agents 64 | - **OpenAI GPT Models**: Integration with state-of-the-art language models 65 | - **Vector Databases**: Pinecone, Chroma, Weaviate for knowledge storage 66 | - **Python Libraries**: AsyncIO, FastAPI, Streamlit for agent deployment 67 | - **Monitoring Tools**: Weights & Biases, LangSmith for agent observability 68 | - **Cloud Platforms**: Docker, AWS/GCP for production deployment 69 | 70 | # 📚 Learn with Us! 71 | 72 | We also offer a [comprehensive course on Agentic AI](https://www.pytopia.ai/courses/agentic-ai) where learners can interact with peers and instructors, participate in live coding sessions, and get personalized feedback on their agent implementations. By registering for the course, you also gain access to our dedicated AI community and expert mentorship. Enroll now and start building the future of AI! Here are some useful links: 73 | 74 | - [Agentic AI with LlamaIndex Course](https://www.pytopia.ai/courses/agentic-ai) 75 | - [Pytopia AI Community Telegram Group](https://t.me/pytopia_ai) 76 | - [Pytopia Website](https://www.pytopia.ai/) 77 | 78 | [](https://www.pytopia.ai/courses/agentic-ai) 79 | 80 | # 🚦 Getting Started 81 | 82 | To start building AI agents with this repository: 83 | 84 | 1. **Clone the repository**: 85 | ```bash 86 | git clone https://github.com/pytopia/agentic-ai.git 87 | cd agentic-ai 88 | ``` 89 | 90 | 2. **Set up your environment**: 91 | ```bash 92 | python -m venv venv 93 | source venv/bin/activate # On Windows: venv\Scripts\activate 94 | pip install -r requirements.txt 95 | ``` 96 | 97 | 3. **Configure API keys**: 98 | ```bash 99 | cp .env.example .env 100 | # Edit .env with your OpenAI API key and other credentials 101 | ``` 102 | 103 | 4. **Start with Chapter 1**: Navigate to `Lectures/01 Introduction to Agentic AI/` and begin your journey! 104 | 105 | 5. **Follow the course structure**: Each chapter builds upon the previous one, so we recommend following the sequence. 106 | 107 | ## 📁 Repository Structure 108 | 109 | ``` 110 | Agentic-AI/ 111 | ├── Lectures/ # All course content 112 | │ ├── 01 Introduction to Agentic AI/ 113 | │ ├── 02 Fundamentals of LlamaIndex/ 114 | │ ├── 03 Building Blocks of AI Agents/ 115 | │ ├── 04 Designing Agent Workflows/ 116 | │ ├── 05 Knowledge Retrieval for Agents/ 117 | │ ├── 06 Tool Use and Function Calling/ 118 | │ ├── 07 Conversational Agents with LlamaIndex/ 119 | │ ├── 08 Multi-Agent Systems/ 120 | │ ├── 09 Agent Evaluation and Testing/ 121 | │ ├── 10 Advanced Agent Architectures/ 122 | │ └── 11 Capstone Project - Building a Full-featured AI Agent/ 123 | ├── images/ # Course images and diagrams 124 | ├── convert_md_to_notebook.py # Utility for converting markdown to notebooks 125 | ├── requirements.txt # Python dependencies 126 | ├── .env.example # Environment variables template 127 | ├── COURSE_STRUCTURE.md # Detailed course outline 128 | └── README.md # This file 129 | ``` 130 | 131 | ## 🎓 Course Highlights 132 | 133 | - **🕐 21 Hours of Content**: Comprehensive coverage from basics to advanced topics 134 | - **📖 39 Interactive Lectures**: Each designed for optimal learning (10-45 minutes) 135 | - **💻 Hands-on Projects**: Practical implementations in every chapter 136 | - **🏗️ Production Focus**: Real-world deployment and scaling strategies 137 | - **📚 Historical Context**: Understanding the evolution of AI agents 138 | - **🔬 Latest Techniques**: Cutting-edge approaches and best practices 139 | 140 | ## 🤝 Contributing 141 | 142 | We welcome contributions from the AI community! If you have improvements, additional examples, or new agent implementations you'd like to share: 143 | 144 | 1. Fork the repository 145 | 2. Create a feature branch (`git checkout -b feature/amazing-agent`) 146 | 3. Commit your changes (`git commit -m 'Add amazing agent implementation'`) 147 | 4. Push to the branch (`git push origin feature/amazing-agent`) 148 | 5. Open a Pull Request 149 | 150 | Please ensure your contributions follow our coding standards and include appropriate documentation. 151 | 152 | ## 🏆 Community Showcase 153 | 154 | Share your agent creations with the community! We love seeing what our students build: 155 | 156 | - Tag us on social media with your agent demos 157 | - Join our Telegram group to share your projects 158 | - Contribute your best agents back to the repository 159 | - Participate in our monthly agent challenges 160 | 161 | ## 📄 License 162 | 163 | This course content is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. 164 | 165 | ## 🙏 Acknowledgments 166 | 167 | Special thanks to: 168 | - **Jerry Liu** and the LlamaIndex team for creating an amazing framework 169 | - **OpenAI** for advancing the field of language models 170 | - **The AI research community** for continuous innovation 171 | - **Our students and contributors** who make this course better every day 172 | 173 | ## 📞 Contact Information 174 | 175 | Ready to start your journey into Agentic AI? Reach out to us! 176 | 177 | - 🌐 Website: [pytopia.ai](https://www.pytopia.ai) 178 | - 💬 Telegram: [pytopia_ai](https://t.me/pytopia_ai) 179 | - 🎥 YouTube: [pytopia](https://www.youtube.com/c/pytopia) 180 | - 📸 Instagram: [pytopia.ai](https://www.instagram.com/pytopia.ai) 181 | - 🎓 LinkedIn: [pytopia](https://www.linkedin.com/company/pytopia) 182 | - 🐦 Twitter: [pytopia_ai](https://twitter.com/pytopia_ai) 183 | - 📧 Email: [pytopia.ai@gmail.com](mailto:pytopia.ai@gmail.com) 184 | 185 | --- 186 | 187 | **Ready to build the future with AI agents? Let's get started! 🚀** 188 | -------------------------------------------------------------------------------- /Lectures/01 Introduction to Agentic AI/2. Understanding Agent Architecture.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "# Understanding Agent Architecture\n", 15 | "\n", 16 | "\n", 17 | "Welcome to our deep dive into the fundamental architecture that powers AI agents! In this lecture, we'll explore the core components that transform static language models into dynamic, interactive systems capable of reasoning, planning, and taking action in the real world.\n", 18 | "\n", 19 | "Understanding agent architecture is essential for building effective AI systems. Just as a building requires a solid foundation and well-designed structure, AI agents need carefully crafted components that work together harmoniously. We'll examine how these components interact, communicate, and coordinate to create intelligent behavior that goes far beyond simple text generation.\n", 20 | "\n", 21 | "By the end of this lecture, you'll have a clear mental model of how AI agents are structured internally and how this architecture enables the sophisticated behaviors we observe in modern agentic systems." 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "**Table of contents** \n", 29 | "- [Core Components of AI Agents](#toc1_) \n", 30 | " - [The Reasoning Engine: The Agent's Brain](#toc1_1_) \n", 31 | " - [Memory Systems: Preserving Context and Learning](#toc1_2_) \n", 32 | " - [Perception Module: Understanding the Environment](#toc1_3_) \n", 33 | " - [Action Execution System: Interfacing with the World](#toc1_4_) \n", 34 | "- [Agent Communication Patterns](#toc2_) \n", 35 | " - [Message Structure and Protocols](#toc2_1_) \n", 36 | " - [Token Management and Context Windows](#toc2_2_) \n", 37 | " - [Special Tokens and Agent Communication](#toc2_3_) \n", 38 | "- [Memory Systems and State Management](#toc3_) \n", 39 | " - [Types of Agent Memory](#toc3_1_) \n", 40 | " - [State Persistence Strategies](#toc3_2_) \n", 41 | "- [Agent vs. Chatbot: Architectural Distinctions](#toc4_) \n", 42 | " - [Architectural Complexity](#toc4_1_) \n", 43 | " - [Behavioral Patterns](#toc4_2_) \n", 44 | " - [Memory and State Management Differences](#toc4_3_) \n", 45 | "- [Real-world Architecture Examples](#toc5_) \n", 46 | " - [Personal Assistant Agents](#toc5_1_) \n", 47 | " - [Research and Analysis Agents](#toc5_2_) \n", 48 | "- [Conclusion](#toc6_) \n", 49 | "\n", 50 | "\n", 57 | "" 58 | ] 59 | }, 60 | { 61 | "cell_type": "markdown", 62 | "metadata": {}, 63 | "source": [ 64 | "## [Core Components of AI Agents](#toc0_)\n", 65 | "\n", 66 | "AI agents are sophisticated systems composed of several interconnected components that work together to perceive, reason, and act in their environment. Unlike traditional software programs that follow predetermined paths, agents must dynamically adapt their behavior based on changing conditions and new information.\n", 67 | "\n", 68 | "The architecture of an AI agent can be understood through four fundamental components that form the backbone of intelligent behavior. These components don't operate in isolation but rather form an integrated system where each component influences and supports the others.\n", 69 | "\n", 70 | "### [The Reasoning Engine: The Agent's Brain](#toc0_)\n", 71 | "\n", 72 | "The **reasoning engine** serves as the central processing unit of an AI agent, much like the brain in biological systems. This component is typically powered by a Large Language Model (LLM) that has been specifically configured and prompted to think like an autonomous agent rather than just a conversational AI.\n", 73 | "\n", 74 | "The reasoning engine's primary responsibility is to interpret situations, analyze available information, and determine the best course of action. When faced with a problem, it doesn't just generate a response—it engages in a structured thinking process that considers multiple factors:\n", 75 | "\n", 76 | "**Situational Analysis**: The reasoning engine evaluates the current state of the environment, identifying relevant facts, constraints, and opportunities. It processes both explicit information provided directly and implicit context that must be inferred.\n", 77 | "\n", 78 | "**Goal-Oriented Thinking**: Unlike general-purpose LLMs that respond to prompts, agent reasoning engines maintain awareness of specific objectives and work systematically toward achieving them. They can break down complex goals into manageable sub-tasks and prioritize actions accordingly.\n", 79 | "\n", 80 | "**Strategic Planning**: The engine doesn't just react to immediate situations but can think several steps ahead, considering the consequences of different actions and selecting strategies that optimize for long-term success." 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": null, 86 | "metadata": {}, 87 | "outputs": [], 88 | "source": [ 89 | "# Simplified representation of reasoning engine decision-making\n", 90 | "class ReasoningEngine:\n", 91 | " def analyze_situation(self, current_state, goal):\n", 92 | " # Evaluate current conditions\n", 93 | " situation_analysis = self.llm.analyze(current_state)\n", 94 | "\n", 95 | " # Identify possible actions\n", 96 | " possible_actions = self.identify_actions(situation_analysis)\n", 97 | "\n", 98 | " # Evaluate each action's potential outcomes\n", 99 | " action_evaluations = []\n", 100 | " for action in possible_actions:\n", 101 | " outcome_prediction = self.predict_outcome(action, current_state)\n", 102 | " goal_alignment = self.evaluate_goal_alignment(outcome_prediction, goal)\n", 103 | " action_evaluations.append((action, goal_alignment))\n", 104 | "\n", 105 | " # Select best action\n", 106 | " return max(action_evaluations, key=lambda x: x[1])[0]" 107 | ] 108 | }, 109 | { 110 | "cell_type": "markdown", 111 | "metadata": {}, 112 | "source": [ 113 | "### [Memory Systems: Preserving Context and Learning](#toc0_)\n", 114 | "\n", 115 | "Memory systems in AI agents serve multiple crucial functions, from maintaining conversation context to learning from past experiences. Unlike human memory, which is often imperfect and selective, agent memory systems can be designed to be both comprehensive and strategically focused.\n", 116 | "\n", 117 | "**Short-term Memory (Working Memory)**: This component maintains immediate context during active tasks. It includes the current conversation history, recent observations, and temporary variables needed for ongoing operations. Short-term memory typically has limited capacity but provides fast access to immediately relevant information.\n", 118 | "\n", 119 | "**Long-term Memory (Persistent Storage)**: This system stores information that persists across sessions and interactions. It includes learned facts, successful strategies, user preferences, and historical interaction patterns. Long-term memory allows agents to build upon past experiences and provide personalized responses.\n", 120 | "\n", 121 | "**Episodic Memory**: Some advanced agents implement episodic memory, which stores specific experiences and events in chronological order. This enables agents to recall \"when did I last help this user with a similar problem?\" or \"what approach worked best in this type of situation?\"\n", 122 | "\n", 123 | "The integration of these memory types allows agents to maintain coherent, contextual interactions while continuously learning and improving their performance.\n", 124 | "\n", 125 | "### [Perception Module: Understanding the Environment](#toc0_)\n", 126 | "\n", 127 | "The perception module serves as the agent's sensory system, responsible for gathering, processing, and interpreting information from the environment. This component transforms raw data from various sources into structured, actionable insights that the reasoning engine can utilize.\n", 128 | "\n", 129 | "**Multi-modal Input Processing**: Modern agents can process various types of input including text, images, audio, and structured data. The perception module normalizes these different input types into a common representation that the reasoning engine can work with effectively.\n", 130 | "\n", 131 | "**Context Extraction**: Beyond simply receiving data, the perception module actively extracts relevant context, identifies patterns, and filters noise. It determines what information is important for the current task and what can be safely ignored.\n", 132 | "\n", 133 | "**Real-time Monitoring**: In dynamic environments, the perception module continuously monitors for changes, new information, or events that might require the agent's attention. This enables proactive rather than purely reactive behavior.\n", 134 | "\n", 135 | "### [Action Execution System: Interfacing with the World](#toc0_)\n", 136 | "\n", 137 | "The action execution system transforms the agent's decisions into concrete actions that affect the environment. This component serves as the bridge between the agent's internal reasoning and external reality.\n", 138 | "\n", 139 | "**Tool Integration**: Modern agents can utilize various tools and APIs to extend their capabilities. The execution system manages these integrations, handling authentication, error recovery, and result processing. Tools might include web search, calculators, databases, or custom business applications.\n", 140 | "\n", 141 | "**Action Validation**: Before executing actions, this system performs safety checks and validation to ensure the proposed action is appropriate, authorized, and likely to succeed. This includes checking permissions, validating parameters, and considering potential side effects.\n", 142 | "\n", 143 | "**Feedback Loop Management**: The execution system doesn't just perform actions—it monitors the results and feeds this information back to the reasoning engine. This creates a continuous feedback loop that allows the agent to adapt its strategy based on real-world outcomes." 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "## [Agent Communication Patterns](#toc0_)\n", 151 | "\n", 152 | "Effective communication is fundamental to agent architecture, encompassing both how agents interact with users and how they coordinate with other systems. The communication patterns an agent employs significantly influence its effectiveness and user experience.\n", 153 | "\n", 154 | "### [Message Structure and Protocols](#toc0_)\n", 155 | "\n", 156 | "AI agents utilize structured communication protocols that go beyond simple question-and-answer exchanges. These protocols enable rich, contextual interactions that can span multiple turns and complex workflows.\n", 157 | "\n", 158 | "**Structured Messaging**: Agent communications often follow specific formats that include metadata about intent, context, and expected response types. This structure helps maintain clarity and enables more sophisticated interaction patterns.\n", 159 | "\n", 160 | "**Protocol Adaptation**: Sophisticated agents can adapt their communication style based on the context, user preferences, and the type of task being performed. They might use formal language for business communications and casual language for creative tasks.\n", 161 | "\n", 162 | "**Multi-turn Coordination**: Unlike single-turn interactions, agents must manage complex conversations that evolve over time, maintaining context while adapting to new information and changing requirements.\n", 163 | "\n", 164 | "### [Token Management and Context Windows](#toc0_)\n", 165 | "\n", 166 | "Understanding token management is crucial for agent architecture because it directly impacts the agent's ability to maintain context and process information effectively.\n", 167 | "\n", 168 | "**Context Window Limitations**: Most current LLMs have fixed context windows (ranging from 4K to 1M+ tokens), which constrains how much information an agent can actively consider. Effective agents implement strategies to work within these limitations while maximizing the utility of available context.\n", 169 | "\n", 170 | "**Dynamic Context Management**: Advanced agents employ techniques like context compression, selective attention, and hierarchical summarization to make the most efficient use of their context windows. They prioritize the most relevant information while maintaining awareness of broader context.\n", 171 | "\n", 172 | "**Token Optimization**: Agents must balance comprehensive context with computational efficiency, making strategic decisions about what information to include in each interaction with the underlying LLM.\n", 173 | "\n", 174 | "### [Special Tokens and Agent Communication](#toc0_)\n", 175 | "\n", 176 | "Modern agents utilize special tokens and structured formats to enhance communication clarity and enable more sophisticated behaviors.\n", 177 | "\n", 178 | "**System Tokens**: These special markers help delineate different types of content, such as distinguishing between user input, agent reasoning, and tool outputs. They provide structure that helps the agent maintain clarity about different information sources.\n", 179 | "\n", 180 | "**Function Calling Tokens**: When agents need to use tools or call functions, they employ specific token patterns that clearly indicate their intent and provide necessary parameters in a structured format.\n", 181 | "\n", 182 | "**Metadata Embedding**: Agents often embed metadata within their communications to track conversation state, maintain task progress, and coordinate complex workflows." 183 | ] 184 | }, 185 | { 186 | "cell_type": "markdown", 187 | "metadata": {}, 188 | "source": [ 189 | "## [Memory Systems and State Management](#toc0_)\n", 190 | "\n", 191 | "The ability to maintain state and manage memory effectively distinguishes sophisticated agents from simple chatbots. Memory systems enable agents to learn, adapt, and provide personalized experiences over time.\n", 192 | "\n", 193 | "### [Types of Agent Memory](#toc0_)\n", 194 | "\n", 195 | "Agent memory systems are typically organized into multiple layers, each serving different purposes and operating at different time scales.\n", 196 | "\n", 197 | "**Immediate Context Memory**: This includes the current conversation, recent observations, and active task state. It's fast-access memory that directly influences immediate decision-making.\n", 198 | "\n", 199 | "**Session Memory**: Information that persists throughout a single interaction session but may not be retained long-term. This includes user preferences expressed during the current session and intermediate results from ongoing tasks.\n", 200 | "\n", 201 | "**Persistent Memory**: Long-term storage that maintains information across sessions, including user profiles, learned preferences, successful strategies, and historical interaction patterns.\n", 202 | "\n", 203 | "**Shared Memory**: In multi-agent systems, shared memory spaces allow different agents to coordinate and share information, enabling collaborative problem-solving.\n", 204 | "\n", 205 | "### [State Persistence Strategies](#toc0_)\n", 206 | "\n", 207 | "Maintaining consistent state across interactions requires sophisticated strategies that balance performance, accuracy, and resource utilization.\n", 208 | "\n", 209 | "**Checkpoint Systems**: Agents periodically save their state, allowing them to resume interrupted tasks or recover from failures without losing progress.\n", 210 | "\n", 211 | "**Incremental Updates**: Rather than storing complete state snapshots, agents often use incremental update mechanisms that track changes over time, reducing storage requirements while maintaining complete state history.\n", 212 | "\n", 213 | "**Selective Persistence**: Not all information needs to be permanently stored. Effective agents implement policies that determine what information is worth persisting based on relevance, frequency of use, and storage constraints." 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "metadata": {}, 219 | "source": [ 220 | "## [Agent vs. Chatbot: Architectural Distinctions](#toc0_)\n", 221 | "\n", 222 | "While agents and chatbots may appear similar on the surface, their underlying architectures reveal fundamental differences in capability and design philosophy.\n", 223 | "\n", 224 | "### [Architectural Complexity](#toc0_)\n", 225 | "\n", 226 | "**Chatbots** typically follow a relatively simple architecture: receive input, process through a language model, generate response, and output result. They're designed primarily for conversational interaction and information retrieval.\n", 227 | "\n", 228 | "**Agents**, by contrast, implement complex architectures with multiple interconnected systems. They maintain state, plan actions, utilize tools, and can operate autonomously over extended periods. Their architecture supports goal-oriented behavior rather than just responsive conversation.\n", 229 | "\n", 230 | "### [Behavioral Patterns](#toc0_)\n", 231 | "\n", 232 | "The architectural differences manifest in distinctly different behavioral patterns:\n", 233 | "\n", 234 | "**Reactive vs. Proactive**: Chatbots are inherently reactive, responding to user inputs without independent initiative. Agents can be proactive, taking actions based on their understanding of goals and environmental conditions.\n", 235 | "\n", 236 | "**Task Completion vs. Conversation**: Chatbots excel at maintaining engaging conversations but may struggle with complex, multi-step task completion. Agents are designed specifically for task completion, with conversation being just one of many interaction modalities.\n", 237 | "\n", 238 | "**Learning and Adaptation**: While chatbots may remember conversation context within a session, agents implement sophisticated learning mechanisms that allow them to improve performance over time and adapt to user preferences.\n", 239 | "\n", 240 | "💡 **Tip**: When designing an agent system, start by clearly defining whether you need conversational interaction (chatbot-like) or autonomous task completion (agent-like) capabilities. This fundamental decision will guide your architectural choices.\n", 241 | "\n", 242 | "### [Memory and State Management Differences](#toc0_)\n", 243 | "\n", 244 | "The most significant architectural distinction lies in how these systems handle memory and state:\n", 245 | "\n", 246 | "**Chatbot Memory**: Typically limited to conversation history within the current session, with minimal persistent state management.\n", 247 | "\n", 248 | "**Agent Memory**: Implements sophisticated memory hierarchies with short-term, long-term, and episodic memory systems that enable learning and personalization over time.\n", 249 | "\n", 250 | "**State Complexity**: Chatbots maintain minimal state (current conversation), while agents manage complex state including goals, plans, tool states, and environmental awareness." 251 | ] 252 | }, 253 | { 254 | "cell_type": "markdown", 255 | "metadata": {}, 256 | "source": [ 257 | "## [Real-world Architecture Examples](#toc0_)\n", 258 | "\n", 259 | "Understanding agent architecture becomes clearer when we examine how these principles apply in real-world systems.\n", 260 | "\n", 261 | "### [Personal Assistant Agents](#toc0_)\n", 262 | "\n", 263 | "Consider a personal assistant agent designed to help with daily task management. Its architecture might include:\n", 264 | "\n", 265 | "- **Reasoning Engine**: Powered by an LLM fine-tuned for task planning and prioritization\n", 266 | "- **Memory System**: Stores user preferences, calendar information, task history, and learned patterns\n", 267 | "- **Perception Module**: Integrates with email, calendar, and task management systems\n", 268 | "- **Action System**: Can send emails, schedule meetings, create reminders, and interact with various productivity tools\n", 269 | "\n", 270 | "This architecture enables the agent to not just respond to requests but to proactively suggest optimizations, remind users of upcoming deadlines, and learn from past interactions to improve future assistance.\n", 271 | "\n", 272 | "### [Research and Analysis Agents](#toc0_)\n", 273 | "\n", 274 | "A research agent designed for academic or business analysis demonstrates different architectural priorities:\n", 275 | "\n", 276 | "- **Enhanced Perception**: Sophisticated web scraping, document analysis, and data extraction capabilities\n", 277 | "- **Specialized Memory**: Maintains research context, tracks source credibility, and builds knowledge graphs\n", 278 | "- **Advanced Reasoning**: Implements critical thinking patterns, bias detection, and evidence evaluation\n", 279 | "- **Collaborative Action**: Can coordinate with other agents, share findings, and contribute to larger research projects\n", 280 | "\n", 281 | "❗️ **Important Note**: The specific architecture of an agent should always align with its intended use case. A customer service agent will have different architectural priorities than a code generation agent or a creative writing assistant." 282 | ] 283 | }, 284 | { 285 | "cell_type": "markdown", 286 | "metadata": {}, 287 | "source": [ 288 | "## [Conclusion](#toc0_)\n", 289 | "\n", 290 | "Understanding agent architecture provides the foundation for building effective AI systems that can reason, remember, and act autonomously. The four core components—reasoning engine, memory systems, perception module, and action execution system—work together to create intelligent behavior that goes far beyond simple text generation.\n", 291 | "\n", 292 | "The key insight is that effective agents require careful architectural design that considers the specific requirements of their intended use case. Whether building a personal assistant, research agent, or specialized business application, the architectural decisions you make will fundamentally determine the agent's capabilities and limitations.\n", 293 | "\n", 294 | "As we continue through this course, we'll dive deeper into each of these components, exploring how to implement them using LlamaIndex and how to optimize their performance for real-world applications. The architectural principles we've covered today will serve as your guide for making informed design decisions throughout your agent development journey.\n", 295 | "\n", 296 | "In our next lecture, we'll get hands-on with LlamaIndex, exploring how this powerful framework implements these architectural concepts and enables rapid development of sophisticated AI agents." 297 | ] 298 | } 299 | ], 300 | "metadata": { 301 | "kernelspec": { 302 | "display_name": "Python 3", 303 | "language": "python", 304 | "name": "python3" 305 | }, 306 | "language_info": { 307 | "codemirror_mode": { 308 | "name": "ipython", 309 | "version": 3 310 | }, 311 | "file_extension": ".py", 312 | "mimetype": "text/x-python", 313 | "name": "python", 314 | "nbconvert_exporter": "python", 315 | "pygments_lexer": "ipython3", 316 | "version": "3.8.0" 317 | } 318 | }, 319 | "nbformat": 4, 320 | "nbformat_minor": 4 321 | } 322 | -------------------------------------------------------------------------------- /Lectures/01 Introduction to Agentic AI/3. Introduction to LlamaIndex Framework.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "# Introduction to LlamaIndex Framework\n", 15 | "\n", 16 | "\n", 17 | "Welcome to your first hands-on exploration of LlamaIndex, the powerful framework that will be your primary tool for building sophisticated AI agents throughout this course. LlamaIndex has emerged as one of the most comprehensive and developer-friendly frameworks for creating data-aware applications with Large Language Models, making it the perfect foundation for our journey into agentic AI.\n", 18 | "\n", 19 | "In this lecture, we'll discover why LlamaIndex has become the go-to choice for developers building production-ready AI agents. We'll explore its rich ecosystem, understand its core philosophy, and get our hands dirty with our first simple agent. By the end of this session, you'll have a solid foundation to build upon as we dive deeper into advanced agentic patterns in the coming lectures." 20 | ] 21 | }, 22 | { 23 | "cell_type": "markdown", 24 | "metadata": {}, 25 | "source": [ 26 | "**Table of contents** \n", 27 | "- [What is LlamaIndex?](#toc1_) \n", 28 | " - [The Genesis Story](#toc1_1_) \n", 29 | " - [Core Philosophy: Data-Aware AI](#toc1_2_) \n", 30 | "- [Why Choose LlamaIndex for Agentic AI?](#toc2_) \n", 31 | " - [Comprehensive Agent Support](#toc2_1_) \n", 32 | " - [Rich Ecosystem and Community](#toc2_2_) \n", 33 | " - [Developer Experience Excellence](#toc2_3_) \n", 34 | "- [LlamaIndex Ecosystem Overview](#toc3_) \n", 35 | " - [Core Components](#toc3_1_) \n", 36 | " - [LlamaHub: The Community Engine](#toc3_2_) \n", 37 | " - [LlamaCloud: Enterprise Infrastructure](#toc3_3_) \n", 38 | "- [Setting Up Your Development Environment](#toc4_) \n", 39 | " - [Installation and Setup](#toc4_1_) \n", 40 | " - [Environment Configuration](#toc4_2_) \n", 41 | " - [Basic Configuration Setup](#toc4_3_) \n", 42 | "- [Your First Simple Agent](#toc5_) \n", 43 | " - [Creating a Basic Conversational Agent](#toc5_1_) \n", 44 | " - [Interacting with Your Agent](#toc5_2_) \n", 45 | " - [Understanding Agent Behavior](#toc5_3_) \n", 46 | " - [Adding Memory and Persistence](#toc5_4_) \n", 47 | "- [Understanding LlamaIndex's Agent Architecture](#toc6_) \n", 48 | " - [The Agent Execution Loop](#toc6_1_) \n", 49 | " - [Key Architectural Components](#toc6_2_) \n", 50 | " - [Agent Types in LlamaIndex](#toc6_3_) \n", 51 | "- [Next Steps and Course Preview](#toc7_) \n", 52 | " - [What We've Accomplished](#toc7_1_) \n", 53 | " - [Looking Ahead](#toc7_2_) \n", 54 | " - [Hands-on Practice](#toc7_3_) \n", 55 | " - [Key Takeaways](#toc7_4_) \n", 56 | "\n", 57 | "\n", 64 | "" 65 | ] 66 | }, 67 | { 68 | "cell_type": "markdown", 69 | "metadata": {}, 70 | "source": [ 71 | "## [What is LlamaIndex?](#toc0_)\n", 72 | "\n", 73 | "LlamaIndex is a comprehensive data framework specifically designed to help you build context-aware AI applications with Large Language Models. Originally created by **Jerry Liu** in **November 2022** (initially called GPT Index), LlamaIndex emerged during the early days of the ChatGPT revolution as developers quickly realized the need for better tools to connect LLMs with private data and external systems.\n", 74 | "\n", 75 | "### [The Genesis Story](#toc0_)\n", 76 | "\n", 77 | "The framework was born out of a practical need that many developers faced in late 2022: while ChatGPT was incredibly powerful, it was essentially a \"closed box\" that couldn't access your specific data, documents, or real-time information. Jerry Liu, then at Uber, recognized this gap and created the first version of what would become LlamaIndex to solve the fundamental problem of making LLMs truly useful in real-world applications.\n", 78 | "\n", 79 | "**Timeline of LlamaIndex Evolution:**\n", 80 | "- **November 2022**: Initial release as \"GPT Index\"\n", 81 | "- **February 2023**: Rebranded to \"LlamaIndex\" and gained significant community traction\n", 82 | "- **March 2023**: Introduction of agent capabilities and tool integrations\n", 83 | "- **June 2023**: Launch of LlamaHub, the community-driven ecosystem of data connectors\n", 84 | "- **September 2023**: Release of LlamaIndex v0.8 with enhanced agent workflows\n", 85 | "- **2024**: Continued evolution with advanced agentic patterns and enterprise features\n", 86 | "\n", 87 | "### [Core Philosophy: Data-Aware AI](#toc0_)\n", 88 | "\n", 89 | "LlamaIndex is built around a simple but powerful philosophy: **AI applications should be able to reason over your data, not just generate text in isolation**. This means your AI agents should be able to:\n", 90 | "\n", 91 | "- **Connect** to various data sources (documents, databases, APIs)\n", 92 | "- **Index** and organize information for efficient retrieval\n", 93 | "- **Query** data intelligently using natural language\n", 94 | "- **Reason** over multiple pieces of information to provide comprehensive answers\n", 95 | "- **Act** based on the insights derived from your data\n", 96 | "\n", 97 | "This philosophy makes LlamaIndex particularly well-suited for building AI agents that need to work with real-world data and perform meaningful tasks beyond simple conversation." 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": {}, 103 | "source": [ 104 | "## [Why Choose LlamaIndex for Agentic AI?](#toc0_)\n", 105 | "\n", 106 | "LlamaIndex stands out in the crowded landscape of AI frameworks for several compelling reasons that make it ideal for building sophisticated AI agents.\n", 107 | "\n", 108 | "### [Comprehensive Agent Support](#toc0_)\n", 109 | "\n", 110 | "Unlike frameworks that treat agents as an afterthought, LlamaIndex was designed from the ground up to support agentic patterns. The framework provides:\n", 111 | "\n", 112 | "**Built-in Agent Types**: Ready-to-use agent implementations including ReAct agents, OpenAI function agents, and custom agent patterns that you can extend for your specific needs.\n", 113 | "\n", 114 | "**Tool Integration**: Seamless integration with hundreds of tools and APIs, allowing your agents to interact with the real world through web searches, database queries, file operations, and custom business logic.\n", 115 | "\n", 116 | "**Memory Management**: Sophisticated memory systems that allow agents to maintain context across conversations, remember previous interactions, and build upon past experiences.\n", 117 | "\n", 118 | "### [Rich Ecosystem and Community](#toc0_)\n", 119 | "\n", 120 | "LlamaIndex has cultivated one of the most vibrant ecosystems in the AI space:\n", 121 | "\n", 122 | "**LlamaHub**: A community-driven repository with over 100+ data connectors, allowing you to easily connect to everything from Google Docs and Notion to complex enterprise databases and APIs.\n", 123 | "\n", 124 | "**Active Development**: With thousands of GitHub stars and regular releases, LlamaIndex maintains rapid development cycles and stays current with the latest AI research and best practices.\n", 125 | "\n", 126 | "**Enterprise Ready**: Used by companies ranging from startups to Fortune 500 enterprises, LlamaIndex has proven its scalability and reliability in production environments.\n", 127 | "\n", 128 | "### [Developer Experience Excellence](#toc0_)\n", 129 | "\n", 130 | "The framework prioritizes developer productivity and ease of use:\n", 131 | "\n", 132 | "**Pythonic Design**: Clean, intuitive APIs that feel natural to Python developers, with clear abstractions that don't hide complexity when you need to dive deeper.\n", 133 | "\n", 134 | "**Extensive Documentation**: Comprehensive guides, tutorials, and examples that make it easy to get started and scale up to complex use cases.\n", 135 | "\n", 136 | "**Flexible Architecture**: Modular design that allows you to use only the components you need, while providing clear upgrade paths as your requirements grow.\n", 137 | "\n", 138 | "💡 **Tip:** LlamaIndex's modular architecture means you can start simple and gradually add complexity as your understanding and requirements evolve." 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "## [LlamaIndex Ecosystem Overview](#toc0_)\n", 146 | "\n", 147 | "The LlamaIndex ecosystem is vast and growing, encompassing everything you need to build production-ready AI agents. Let's explore the key components that make this ecosystem so powerful.\n", 148 | "\n", 149 | "### [Core Components](#toc0_)\n", 150 | "\n", 151 | "**LlamaIndex Core**: The foundational library that provides essential building blocks including document loaders, text splitters, vector stores, query engines, and the basic agent framework.\n", 152 | "\n", 153 | "**LlamaIndex Integrations**: A collection of specialized packages that extend the core functionality with integrations for specific LLM providers (OpenAI, Anthropic, Cohere), vector databases (Pinecone, Weaviate, Chroma), and cloud platforms (AWS, GCP, Azure).\n", 154 | "\n", 155 | "**LlamaIndex Experimental**: Cutting-edge features and experimental components where new agentic patterns are first introduced before being moved to the core library.\n", 156 | "\n", 157 | "### [LlamaHub: The Community Engine](#toc0_)\n", 158 | "\n", 159 | "LlamaHub represents one of the most valuable aspects of the LlamaIndex ecosystem - a community-driven collection of data connectors and tools:\n", 160 | "\n", 161 | "**Data Loaders**: Connect to virtually any data source including:\n", 162 | "- Document systems (Google Drive, SharePoint, Notion)\n", 163 | "- Databases (PostgreSQL, MongoDB, Elasticsearch)\n", 164 | "- Web sources (websites, APIs, RSS feeds)\n", 165 | "- File formats (PDF, DOCX, CSV, JSON)\n", 166 | "\n", 167 | "**Tool Integrations**: Pre-built tools for common agent tasks:\n", 168 | "- Web search (Google, Bing, DuckDuckGo)\n", 169 | "- Code execution and analysis\n", 170 | "- Email and communication platforms\n", 171 | "- Business applications (Salesforce, HubSpot, Slack)\n", 172 | "\n", 173 | "### [LlamaCloud: Enterprise Infrastructure](#toc0_)\n", 174 | "\n", 175 | "For production deployments, LlamaCloud provides managed infrastructure and enterprise features:\n", 176 | "\n", 177 | "**Managed Parsing**: Advanced document parsing capabilities that handle complex formats and extract structured information from unstructured documents.\n", 178 | "\n", 179 | "**Scalable Indexing**: Cloud-based indexing and embedding services that can handle large-scale data processing without managing your own infrastructure.\n", 180 | "\n", 181 | "**Enterprise Security**: SOC 2 compliance, data encryption, and enterprise-grade security features for sensitive applications." 182 | ] 183 | }, 184 | { 185 | "cell_type": "markdown", 186 | "metadata": {}, 187 | "source": [ 188 | "## [Setting Up Your Development Environment](#toc0_)\n", 189 | "\n", 190 | "Before we can build our first agent, we need to set up a proper development environment. This setup will serve as the foundation for all our future work with LlamaIndex.\n", 191 | "\n", 192 | "### [Installation and Setup](#toc0_)\n", 193 | "\n", 194 | "Let's start by installing LlamaIndex and the essential dependencies. We'll set up a clean environment that's ready for serious agent development." 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": null, 200 | "metadata": {}, 201 | "outputs": [], 202 | "source": [ 203 | "# Install core LlamaIndex\n", 204 | "pip install llama-index\n", 205 | "\n", 206 | "# Install additional integrations we'll use throughout the course\n", 207 | "pip install llama-index-embeddings-openai\n", 208 | "pip install llama-index-llms-openai\n", 209 | "pip install llama-index-vector-stores-chroma\n", 210 | "pip install llama-index-readers-file\n", 211 | "\n", 212 | "# For development and debugging\n", 213 | "pip install jupyter notebook python-dotenv" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "metadata": {}, 219 | "source": [ 220 | "### [Environment Configuration](#toc0_)\n", 221 | "\n", 222 | "Create a `.env` file in your project root to securely manage API keys and configuration:" 223 | ] 224 | }, 225 | { 226 | "cell_type": "code", 227 | "execution_count": null, 228 | "metadata": {}, 229 | "outputs": [], 230 | "source": [ 231 | "# OpenAI Configuration\n", 232 | "OPENAI_API_KEY=your_openai_api_key_here\n", 233 | "\n", 234 | "# Optional: Other LLM providers\n", 235 | "ANTHROPIC_API_KEY=your_anthropic_key_here\n", 236 | "COHERE_API_KEY=your_cohere_key_here\n", 237 | "\n", 238 | "# Development settings\n", 239 | "ENVIRONMENT=development\n", 240 | "LOG_LEVEL=INFO" 241 | ] 242 | }, 243 | { 244 | "cell_type": "markdown", 245 | "metadata": {}, 246 | "source": [ 247 | "### [Basic Configuration Setup](#toc0_)\n", 248 | "\n", 249 | "Create a configuration module that will handle our LlamaIndex settings:" 250 | ] 251 | }, 252 | { 253 | "cell_type": "code", 254 | "execution_count": null, 255 | "metadata": {}, 256 | "outputs": [], 257 | "source": [ 258 | "import os\n", 259 | "from dotenv import load_dotenv\n", 260 | "from llama_index.core import Settings\n", 261 | "from llama_index.llms.openai import OpenAI\n", 262 | "from llama_index.embeddings.openai import OpenAIEmbedding\n", 263 | "\n", 264 | "# Load environment variables\n", 265 | "load_dotenv()\n", 266 | "\n", 267 | "def configure_llama_index():\n", 268 | " \"\"\"Configure LlamaIndex with our preferred settings.\"\"\"\n", 269 | "\n", 270 | " # Set up the LLM\n", 271 | " Settings.llm = OpenAI(\n", 272 | " model=\"gpt-3.5-turbo\",\n", 273 | " temperature=0.1,\n", 274 | " api_key=os.getenv(\"OPENAI_API_KEY\")\n", 275 | " )\n", 276 | "\n", 277 | " # Set up embeddings\n", 278 | " Settings.embed_model = OpenAIEmbedding(\n", 279 | " model=\"text-embedding-ada-002\",\n", 280 | " api_key=os.getenv(\"OPENAI_API_KEY\")\n", 281 | " )\n", 282 | "\n", 283 | " # Configure chunk size for document processing\n", 284 | " Settings.chunk_size = 512\n", 285 | " Settings.chunk_overlap = 50\n", 286 | "\n", 287 | " print(\"✅ LlamaIndex configured successfully!\")\n", 288 | "\n", 289 | "# Initialize configuration\n", 290 | "configure_llama_index()" 291 | ] 292 | }, 293 | { 294 | "cell_type": "markdown", 295 | "metadata": {}, 296 | "source": [ 297 | "❗️ **Important Note:** Never commit API keys to version control. Always use environment variables or secure key management systems in production." 298 | ] 299 | }, 300 | { 301 | "cell_type": "markdown", 302 | "metadata": {}, 303 | "source": [ 304 | "## [Your First Simple Agent](#toc0_)\n", 305 | "\n", 306 | "Now that our environment is set up, let's build our first AI agent using LlamaIndex. This simple agent will demonstrate the fundamental concepts we'll build upon throughout the course.\n", 307 | "\n", 308 | "### [Creating a Basic Conversational Agent](#toc0_)\n", 309 | "\n", 310 | "We'll start with a simple conversational agent that can maintain context and provide helpful responses:" 311 | ] 312 | }, 313 | { 314 | "cell_type": "code", 315 | "execution_count": null, 316 | "metadata": {}, 317 | "outputs": [], 318 | "source": [ 319 | "from llama_index.core.agent import ReActAgent\n", 320 | "from llama_index.core.tools import FunctionTool\n", 321 | "from llama_index.core import Settings\n", 322 | "import datetime\n", 323 | "\n", 324 | "def get_current_time() -> str:\n", 325 | " \"\"\"Get the current date and time.\"\"\"\n", 326 | " return f\"Current date and time: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\"\n", 327 | "\n", 328 | "def calculate_simple_math(expression: str) -> str:\n", 329 | " \"\"\"Safely evaluate simple mathematical expressions.\"\"\"\n", 330 | " try:\n", 331 | " # Basic safety check - only allow numbers and basic operators\n", 332 | " allowed_chars = set('0123456789+-*/()., ')\n", 333 | " if not all(c in allowed_chars for c in expression):\n", 334 | " return \"Error: Only basic mathematical operations are allowed\"\n", 335 | "\n", 336 | " result = eval(expression)\n", 337 | " return f\"The result of {expression} is: {result}\"\n", 338 | " except Exception as e:\n", 339 | " return f\"Error calculating {expression}: {str(e)}\"\n", 340 | "\n", 341 | "# Create tools from our functions\n", 342 | "time_tool = FunctionTool.from_defaults(fn=get_current_time)\n", 343 | "math_tool = FunctionTool.from_defaults(fn=calculate_simple_math)\n", 344 | "\n", 345 | "# Create our first agent\n", 346 | "agent = ReActAgent.from_tools(\n", 347 | " tools=[time_tool, math_tool],\n", 348 | " llm=Settings.llm,\n", 349 | " verbose=True\n", 350 | ")\n", 351 | "\n", 352 | "print(\"🤖 Your first LlamaIndex agent is ready!\")" 353 | ] 354 | }, 355 | { 356 | "cell_type": "markdown", 357 | "metadata": {}, 358 | "source": [ 359 | "### [Interacting with Your Agent](#toc0_)\n", 360 | "\n", 361 | "Let's see our agent in action with some example interactions:" 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": null, 367 | "metadata": {}, 368 | "outputs": [], 369 | "source": [ 370 | "# Test basic conversation\n", 371 | "response = agent.chat(\"Hello! What can you help me with?\")\n", 372 | "print(\"Agent:\", response.response)\n", 373 | "\n", 374 | "# Test tool usage - time\n", 375 | "response = agent.chat(\"What time is it right now?\")\n", 376 | "print(\"Agent:\", response.response)\n", 377 | "\n", 378 | "# Test tool usage - math\n", 379 | "response = agent.chat(\"Can you calculate 25 * 4 + 10?\")\n", 380 | "print(\"Agent:\", response.response)\n", 381 | "\n", 382 | "# Test reasoning and tool combination\n", 383 | "response = agent.chat(\"If it's currently past 3 PM, calculate 15 * 8, otherwise just tell me the time\")\n", 384 | "print(\"Agent:\", response.response)" 385 | ] 386 | }, 387 | { 388 | "cell_type": "markdown", 389 | "metadata": {}, 390 | "source": [ 391 | "### [Understanding Agent Behavior](#toc0_)\n", 392 | "\n", 393 | "When you run this code, you'll notice several important behaviors that distinguish agents from simple LLMs:\n", 394 | "\n", 395 | "**Tool Selection**: The agent automatically determines when and which tools to use based on your query. It doesn't just generate text - it takes action.\n", 396 | "\n", 397 | "**Reasoning Process**: With `verbose=True`, you can see the agent's thought process as it decides what actions to take.\n", 398 | "\n", 399 | "**Context Maintenance**: The agent remembers the conversation context and can reference previous interactions.\n", 400 | "\n", 401 | "**Error Handling**: The agent gracefully handles errors and provides meaningful feedback when tools fail.\n", 402 | "\n", 403 | "### [Adding Memory and Persistence](#toc0_)\n", 404 | "\n", 405 | "Let's enhance our agent with memory capabilities:" 406 | ] 407 | }, 408 | { 409 | "cell_type": "code", 410 | "execution_count": null, 411 | "metadata": {}, 412 | "outputs": [], 413 | "source": [ 414 | "from llama_index.core.memory import ChatMemoryBuffer\n", 415 | "\n", 416 | "# Create a memory buffer for conversation history\n", 417 | "memory = ChatMemoryBuffer.from_defaults(token_limit=2000)\n", 418 | "\n", 419 | "# Create an agent with memory\n", 420 | "agent_with_memory = ReActAgent.from_tools(\n", 421 | " tools=[time_tool, math_tool],\n", 422 | " llm=Settings.llm,\n", 423 | " memory=memory,\n", 424 | " verbose=True\n", 425 | ")\n", 426 | "\n", 427 | "# Test memory functionality\n", 428 | "print(\"=== Testing Agent Memory ===\")\n", 429 | "agent_with_memory.chat(\"My name is Alex and I'm learning about AI agents\")\n", 430 | "agent_with_memory.chat(\"What's my name?\")\n", 431 | "agent_with_memory.chat(\"Calculate 100 / 5\")\n", 432 | "response = agent_with_memory.chat(\"What was the result of my last calculation?\")\n", 433 | "print(\"Final response:\", response.response)" 434 | ] 435 | }, 436 | { 437 | "cell_type": "markdown", 438 | "metadata": {}, 439 | "source": [ 440 | "## [Understanding LlamaIndex's Agent Architecture](#toc0_)\n", 441 | "\n", 442 | "Now that we've built our first agent, let's understand the architectural principles that make LlamaIndex agents so powerful and flexible.\n", 443 | "\n", 444 | "### [The Agent Execution Loop](#toc0_)\n", 445 | "\n", 446 | "LlamaIndex agents follow a sophisticated execution pattern that mirrors human problem-solving:\n", 447 | "\n", 448 | "**1. Observation**: The agent receives input (user query, environment state, or tool results)\n", 449 | "**2. Reasoning**: The agent analyzes the situation and determines what action to take\n", 450 | "**3. Action**: The agent executes tools, queries data, or generates responses\n", 451 | "**4. Reflection**: The agent evaluates the results and decides whether to continue or provide a final answer\n", 452 | "\n", 453 | "This loop continues until the agent believes it has sufficiently addressed the user's request.\n", 454 | "\n", 455 | "### [Key Architectural Components](#toc0_)\n", 456 | "\n", 457 | "**Agent Core**: The central reasoning engine that orchestrates the entire process, typically powered by a sophisticated LLM that can understand instructions, reason about problems, and make decisions.\n", 458 | "\n", 459 | "**Tool Registry**: A collection of available tools and functions that the agent can invoke, each with clear descriptions and parameter specifications that help the agent understand when and how to use them.\n", 460 | "\n", 461 | "**Memory System**: Manages conversation history, context, and learned information across interactions, allowing agents to build upon previous conversations and maintain state.\n", 462 | "\n", 463 | "**Planning Module**: Handles complex multi-step reasoning and task decomposition, breaking down complex requests into manageable subtasks.\n", 464 | "\n", 465 | "### [Agent Types in LlamaIndex](#toc0_)\n", 466 | "\n", 467 | "LlamaIndex provides several pre-built agent types, each optimized for different use cases:\n", 468 | "\n", 469 | "**ReActAgent**: Implements the Reasoning and Acting pattern, excellent for general-purpose tasks that require tool use and multi-step reasoning.\n", 470 | "\n", 471 | "**OpenAIAgent**: Leverages OpenAI's function calling capabilities for efficient and reliable tool execution.\n", 472 | "\n", 473 | "**QueryPlannerAgent**: Specialized for complex data querying tasks that require sophisticated query planning and execution." 474 | ] 475 | }, 476 | { 477 | "cell_type": "markdown", 478 | "metadata": {}, 479 | "source": [ 480 | "## [Next Steps and Course Preview](#toc0_)\n", 481 | "\n", 482 | "Congratulations! You've just taken your first steps into the world of LlamaIndex and built your first AI agent. This simple agent demonstrates the fundamental concepts that we'll expand upon throughout our course.\n", 483 | "\n", 484 | "### [What We've Accomplished](#toc0_)\n", 485 | "\n", 486 | "In this lecture, we've established a solid foundation by:\n", 487 | "- Understanding LlamaIndex's philosophy and ecosystem\n", 488 | "- Setting up a professional development environment\n", 489 | "- Building and testing our first functional agent\n", 490 | "- Exploring the architectural principles behind LlamaIndex agents\n", 491 | "\n", 492 | "### [Looking Ahead](#toc0_)\n", 493 | "\n", 494 | "In our upcoming lectures, we'll build upon this foundation to explore:\n", 495 | "\n", 496 | "**Chapter 2 - Fundamentals of LlamaIndex**: We'll dive deeper into LlamaIndex's core components, including document processing, indexing strategies, and advanced LLM configurations.\n", 497 | "\n", 498 | "**Chapter 3 - Building Blocks of AI Agents**: You'll learn to create sophisticated tools, implement advanced memory systems, and build agents that can handle complex multi-step tasks.\n", 499 | "\n", 500 | "**Chapter 4 - Designing Agent Workflows**: We'll explore LlamaIndex's powerful workflow system for building complex, multi-agent applications with sophisticated control flow.\n", 501 | "\n", 502 | "### [Hands-on Practice](#toc0_)\n", 503 | "\n", 504 | "Before our next session, I encourage you to experiment with the agent we built today:\n", 505 | "\n", 506 | "1. **Try different tools**: Create your own custom tools for tasks relevant to your interests\n", 507 | "2. **Experiment with prompts**: Modify the agent's system prompts to change its personality or behavior\n", 508 | "3. **Test edge cases**: See how the agent handles ambiguous requests or error conditions\n", 509 | "4. **Explore memory**: Build conversations that span multiple topics and observe how the agent maintains context\n", 510 | "\n", 511 | "### [Key Takeaways](#toc0_)\n", 512 | "\n", 513 | "💡 **Remember these fundamental principles:**\n", 514 | "- LlamaIndex is designed for data-aware AI applications\n", 515 | "- Agents combine reasoning, action, and observation in a continuous loop\n", 516 | "- The framework's modular architecture allows for gradual complexity increase\n", 517 | "- Real-world agents require proper tool design, memory management, and error handling\n", 518 | "\n", 519 | "As we continue our journey, you'll discover that LlamaIndex's true power lies not just in its individual components, but in how they work together to create intelligent, capable, and reliable AI agents that can tackle real-world challenges.\n", 520 | "\n", 521 | "Welcome to the exciting world of agentic AI with LlamaIndex! 🚀" 522 | ] 523 | } 524 | ], 525 | "metadata": { 526 | "kernelspec": { 527 | "display_name": "Python 3", 528 | "language": "python", 529 | "name": "python3" 530 | }, 531 | "language_info": { 532 | "codemirror_mode": { 533 | "name": "ipython", 534 | "version": 3 535 | }, 536 | "file_extension": ".py", 537 | "mimetype": "text/x-python", 538 | "name": "python", 539 | "nbconvert_exporter": "python", 540 | "pygments_lexer": "ipython3", 541 | "version": "3.8.0" 542 | } 543 | }, 544 | "nbformat": 4, 545 | "nbformat_minor": 4 546 | } 547 | -------------------------------------------------------------------------------- /Lectures/01 Introduction to Agentic AI/1. The Evolution from LLMs to Autonomous Agents.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "# The Evolution from LLMs to Autonomous Agents\n", 15 | "\n", 16 | "\n", 17 | "Welcome to the fascinating world of Agentic AI! In this opening lecture, we'll embark on a journey that traces the remarkable evolution from traditional Large Language Models (LLMs) to sophisticated autonomous agents. This transformation represents one of the most significant advances in artificial intelligence, fundamentally changing how we interact with and leverage AI systems.\n", 18 | "\n", 19 | "\n", 20 | "Understanding this evolution is crucial because it sets the foundation for everything we'll explore in this course. We'll discover how AI systems have grown from passive text generators to proactive problem-solvers capable of reasoning, planning, and taking action in complex environments. By exploring key historical milestones and breakthrough moments, we'll gain insight into how we arrived at today's powerful agentic systems." 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": {}, 26 | "source": [ 27 | "" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "**Table of contents** \n", 35 | "- [What are Large Language Models (LLMs)?](#toc1_) \n", 36 | " - [The Foundation of Modern AI](#toc1_1_) \n", 37 | " - [Historical Timeline of Modern LLMs](#toc1_2_) \n", 38 | " - [Core Characteristics of LLMs](#toc1_3_) \n", 39 | " - [The Limitations That Sparked Evolution](#toc1_4_) \n", 40 | "- [The Birth of AI Agents](#toc2_) \n", 41 | " - [Historical Foundations of AI Agents](#toc2_1_) \n", 42 | " - [The Modern Agent Renaissance](#toc2_2_) \n", 43 | " - [Defining Modern AI Agents](#toc2_3_) \n", 44 | " - [The Agent Paradigm Shift](#toc2_4_) \n", 45 | "- [The Thought-Action-Observation Cycle](#toc3_) \n", 46 | " - [Understanding the TAO Framework](#toc3_1_) \n", 47 | " - [The Cycle in Practice](#toc3_2_) \n", 48 | " - [Why This Cycle Matters](#toc3_3_) \n", 49 | "- [Historical Timeline: From Concept to Reality](#toc4_) \n", 50 | " - [The Foundational Era (1950-1990)](#toc4_1_) \n", 51 | " - [The Learning Era (1990-2010)](#toc4_2_) \n", 52 | " - [The Deep Learning Revolution (2010-2020)](#toc4_3_) \n", 53 | " - [The Agentic AI Era (2020-Present)](#toc4_4_) \n", 54 | "- [Real-World Applications and Use Cases](#toc5_) \n", 55 | " - [Business and Enterprise Applications](#toc5_1_) \n", 56 | " - [Personal Productivity Applications](#toc5_2_) \n", 57 | " - [Technical and Development Applications](#toc5_3_) \n", 58 | " - [The Competitive Advantage](#toc5_4_) \n", 59 | "- [Key Differences: LLMs vs. AI Agents](#toc6_) \n", 60 | " - [Interaction Patterns](#toc6_1_) \n", 61 | " - [Memory and State Management](#toc6_2_) \n", 62 | " - [Environmental Interaction](#toc6_3_) \n", 63 | " - [Problem-Solving Approach](#toc6_4_) \n", 64 | " - [Learning and Adaptation](#toc6_5_) \n", 65 | " - [Autonomy Level](#toc6_6_) \n", 66 | "- [The Future Landscape](#toc7_) \n", 67 | " - [Emerging Capabilities](#toc7_1_) \n", 68 | " - [Societal Impact](#toc7_2_) \n", 69 | " - [Challenges and Considerations](#toc7_3_) \n", 70 | "- [Conclusion](#toc8_) \n", 71 | " - [Key Takeaways](#toc8_1_) \n", 72 | " - [The Road Ahead: LlamaIndex and the Future](#toc8_2_) \n", 73 | " - [Looking Ahead](#toc8_3_) \n", 74 | "\n", 75 | "\n", 82 | "" 83 | ] 84 | }, 85 | { 86 | "cell_type": "markdown", 87 | "metadata": {}, 88 | "source": [ 89 | "## [What are Large Language Models (LLMs)?](#toc0_)" 90 | ] 91 | }, 92 | { 93 | "cell_type": "markdown", 94 | "metadata": {}, 95 | "source": [ 96 | "Large Language Models represent a revolutionary breakthrough in natural language processing that has transformed how machines understand and generate human language. These sophisticated neural networks are trained on vast amounts of text data, enabling them to capture intricate patterns in language and generate remarkably human-like responses.\n" 97 | ] 98 | }, 99 | { 100 | "cell_type": "markdown", 101 | "metadata": {}, 102 | "source": [ 103 | "### [The Foundation of Modern AI](#toc0_)\n" 104 | ] 105 | }, 106 | { 107 | "cell_type": "markdown", 108 | "metadata": {}, 109 | "source": [ 110 | "LLMs are built on the **transformer architecture**, a neural network design that excels at processing sequential data like text. This revolutionary architecture was introduced in the landmark paper **\"Attention Is All You Need\"** by Vaswani et al. in **June 2017**, which fundamentally changed how we approach natural language processing.\n" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "" 118 | ] 119 | }, 120 | { 121 | "cell_type": "markdown", 122 | "metadata": {}, 123 | "source": [ 124 | "The key innovation lies in their ability to understand context and relationships between words across long passages of text through the **attention mechanism**. When we interact with an LLM, we're essentially communicating with a system that has been exposed to billions of words from books, articles, websites, and other text sources.\n" 125 | ] 126 | }, 127 | { 128 | "cell_type": "markdown", 129 | "metadata": {}, 130 | "source": [ 131 | "### [Historical Timeline of Modern LLMs](#toc0_)\n" 132 | ] 133 | }, 134 | { 135 | "cell_type": "markdown", 136 | "metadata": {}, 137 | "source": [ 138 | "The rapid evolution of LLMs has been marked by several breakthrough moments:\n", 139 | "\n", 140 | "**2017 - The Transformer Revolution**: Google's \"Attention Is All You Need\" paper introduced the transformer architecture, replacing recurrent neural networks with attention mechanisms for better parallelization and performance.\n", 141 | "\n", 142 | "**2018 - GPT-1**: OpenAI released the first Generative Pre-trained Transformer (GPT-1) with 117 million parameters, demonstrating the potential of unsupervised pre-training for language understanding.\n", 143 | "\n", 144 | "**2019 - GPT-2**: OpenAI's GPT-2 (1.5 billion parameters) was initially considered \"too dangerous to release\" due to its ability to generate coherent, human-like text. This marked the first widespread recognition of LLM capabilities.\n", 145 | "\n", 146 | "**2020 - GPT-3**: The release of GPT-3 (175 billion parameters) in **June 2020** was a watershed moment, demonstrating emergent abilities in few-shot learning, code generation, and creative writing that surprised even its creators.\n", 147 | "\n", 148 | "**2022 - ChatGPT**: OpenAI's release of ChatGPT in **November 2022** brought LLMs to mainstream attention, achieving 100 million users in just 2 months and sparking the current AI revolution.\n" 149 | ] 150 | }, 151 | { 152 | "cell_type": "markdown", 153 | "metadata": {}, 154 | "source": [ 155 | "The training process involves two main phases:\n", 156 | "- **Pre-training**: The model learns general language patterns by predicting the next word in sentences across massive datasets\n", 157 | "- **Fine-tuning**: The model is refined for specific tasks or to follow instructions more effectively\n" 158 | ] 159 | }, 160 | { 161 | "cell_type": "markdown", 162 | "metadata": {}, 163 | "source": [ 164 | "" 165 | ] 166 | }, 167 | { 168 | "cell_type": "markdown", 169 | "metadata": {}, 170 | "source": [ 171 | "### [Core Characteristics of LLMs](#toc0_)\n" 172 | ] 173 | }, 174 | { 175 | "cell_type": "markdown", 176 | "metadata": {}, 177 | "source": [ 178 | "LLMs possess several remarkable capabilities that make them powerful tools for various applications:\n", 179 | "\n", 180 | "**Pattern Recognition**: They excel at identifying and replicating patterns in text, from grammatical structures to writing styles and even logical reasoning patterns.\n", 181 | "\n", 182 | "**Contextual Understanding**: Modern LLMs can maintain context across thousands of tokens (roughly 750-1000 words per 1000 tokens), allowing for coherent long-form conversations and document analysis.\n", 183 | "\n", 184 | "**Emergent Abilities**: As LLMs grow larger, they develop unexpected capabilities that weren't explicitly programmed, such as basic mathematical reasoning, code generation, and creative writing.\n" 185 | ] 186 | }, 187 | { 188 | "cell_type": "markdown", 189 | "metadata": {}, 190 | "source": [ 191 | "### [The Limitations That Sparked Evolution](#toc0_)\n" 192 | ] 193 | }, 194 | { 195 | "cell_type": "markdown", 196 | "metadata": {}, 197 | "source": [ 198 | "Despite their impressive capabilities, traditional LLMs face several fundamental limitations that prevent them from being truly autonomous:\n", 199 | "\n", 200 | "**Passive Nature**: LLMs are essentially sophisticated autocomplete systems. They respond to prompts but cannot initiate actions or pursue goals independently.\n", 201 | "\n", 202 | "**No Real-World Interaction**: Traditional LLMs exist in isolation, unable to access external information, use tools, or interact with systems beyond generating text.\n", 203 | "\n", 204 | "**Limited Memory**: Most LLMs have no persistent memory between conversations, starting each interaction with a blank slate.\n", 205 | "\n", 206 | "**Static Knowledge**: Their knowledge is frozen at the time of training, making them unable to access current information or learn from new experiences.\n" 207 | ] 208 | }, 209 | { 210 | "cell_type": "markdown", 211 | "metadata": {}, 212 | "source": [ 213 | "## [The Birth of AI Agents](#toc0_)" 214 | ] 215 | }, 216 | { 217 | "cell_type": "markdown", 218 | "metadata": {}, 219 | "source": [ 220 | "The limitations of traditional LLMs led researchers and developers to ask a fundamental question: *What if we could give LLMs the ability to think, plan, and act in the real world?* This question sparked the development of AI agents – systems that combine the language understanding capabilities of LLMs with the ability to perceive, reason, and take action.\n", 221 | "\n", 222 | "However, the concept of AI agents predates modern LLMs by decades. Let's explore this rich history to understand how we arrived at today's sophisticated agentic systems.\n" 223 | ] 224 | }, 225 | { 226 | "cell_type": "markdown", 227 | "metadata": {}, 228 | "source": [ 229 | "" 230 | ] 231 | }, 232 | { 233 | "cell_type": "markdown", 234 | "metadata": {}, 235 | "source": [ 236 | "### [Historical Foundations of AI Agents](#toc0_)\n" 237 | ] 238 | }, 239 | { 240 | "cell_type": "markdown", 241 | "metadata": {}, 242 | "source": [ 243 | "The journey toward autonomous AI agents began long before the transformer revolution:\n", 244 | "\n", 245 | "**1950 - The Turing Test**: Alan Turing proposed his famous test to evaluate whether a machine could exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. This laid the conceptual foundation for thinking about machines as independent \"agents.\"\n", 246 | "\n", 247 | "**1956 - The Dartmouth Conference**: John McCarthy, Marvin Minsky, and colleagues organized the Dartmouth Summer Research Project on Artificial Intelligence, officially coining the term \"Artificial Intelligence\" and setting the stage for decades of agent research.\n", 248 | "\n", 249 | "**1966 - ELIZA**: Joseph Weizenbaum at MIT created ELIZA, one of the first conversational AI agents. Using simple pattern matching and substitution, ELIZA could simulate a Rogerian psychotherapist, demonstrating early human-computer interaction possibilities.\n", 250 | "\n", 251 | "**1970s - Expert Systems Era**: This decade saw the rise of expert systems like:\n", 252 | "- **DENDRAL (1965-1970s)**: One of the first expert systems, designed to identify organic molecules\n", 253 | "- **MYCIN (1972)**: Developed at Stanford to diagnose bacterial infections and recommend antibiotics\n", 254 | "- **PROSPECTOR (1974)**: Used for mineral exploration, successfully identifying a molybdenum deposit worth over $100 million\n", 255 | "\n", 256 | "**1986 - Shakey the Robot**: SRI International's Shakey was the first general-purpose mobile robot capable of reasoning about its actions, combining AI planning with robotics.\n", 257 | "\n", 258 | "**1992 - TD-Gammon**: Gerald Tesauro's breakthrough reinforcement learning agent learned to play backgammon at world-class level purely through self-play, demonstrating the power of learning from experience.\n" 259 | ] 260 | }, 261 | { 262 | "cell_type": "markdown", 263 | "metadata": {}, 264 | "source": [ 265 | "### [The Modern Agent Renaissance](#toc0_)\n" 266 | ] 267 | }, 268 | { 269 | "cell_type": "markdown", 270 | "metadata": {}, 271 | "source": [ 272 | "The combination of powerful LLMs with classical agent concepts has created a new generation of intelligent systems:\n", 273 | "\n", 274 | "**2016 - AlphaGo**: DeepMind's victory over world Go champion Lee Sedol marked a turning point, showing that AI could master complex strategic thinking previously thought to be uniquely human.\n", 275 | "\n", 276 | "**2022 - The Agent Framework Explosion**: The release of ChatGPT sparked massive interest in building autonomous systems:\n", 277 | "- **LangChain** launched in **October 2022**, providing the first major framework for building LLM-powered applications and agents\n", 278 | "- **ReAct (Reasoning + Acting)** paper by Yao et al. introduced the systematic approach of interleaving reasoning and acting that forms the backbone of modern agents\n", 279 | "\n", 280 | "**2023 - The Autonomous Agent Breakthrough**: This year saw the emergence of the first truly autonomous agents:\n", 281 | "- **AutoGPT** (March 2023): One of the first systems to demonstrate autonomous goal pursuit, breaking down complex tasks into subtasks\n", 282 | "- **BabyAGI** (March 2023): Showcased autonomous task generation and prioritization\n", 283 | "- **GPT-4 with plugins** (March 2023): OpenAI's integration of external tools marked mainstream adoption of agentic capabilities\n" 284 | ] 285 | }, 286 | { 287 | "cell_type": "markdown", 288 | "metadata": {}, 289 | "source": [ 290 | "### [Defining Modern AI Agents](#toc0_)\n" 291 | ] 292 | }, 293 | { 294 | "cell_type": "markdown", 295 | "metadata": {}, 296 | "source": [ 297 | "An **AI agent** is an autonomous system that can perceive its environment, make decisions, and take actions to achieve specific goals. Unlike traditional LLMs that simply respond to prompts, modern agents are proactive systems capable of:\n", 298 | "\n", 299 | "- **Goal-oriented behavior**: Working towards specific objectives over extended periods\n", 300 | "- **Environmental interaction**: Using tools, APIs, and accessing external resources \n", 301 | "- **Persistent memory**: Maintaining context and learning from experiences across sessions\n", 302 | "- **Dynamic planning**: Adapting strategies based on changing conditions and feedback\n", 303 | "- **Tool use**: Leveraging external capabilities to extend their functionality\n" 304 | ] 305 | }, 306 | { 307 | "cell_type": "markdown", 308 | "metadata": {}, 309 | "source": [ 310 | "" 311 | ] 312 | }, 313 | { 314 | "cell_type": "markdown", 315 | "metadata": {}, 316 | "source": [ 317 | "### [The Agent Paradigm Shift](#toc0_)\n" 318 | ] 319 | }, 320 | { 321 | "cell_type": "markdown", 322 | "metadata": {}, 323 | "source": [ 324 | "The transition from LLMs to agents represents a fundamental paradigm shift in AI:\n", 325 | "\n", 326 | "**From Reactive to Proactive**: Instead of waiting for human prompts, agents can initiate actions and pursue goals independently.\n", 327 | "\n", 328 | "**From Isolated to Connected**: Agents can interact with databases, APIs, web services, and other external systems.\n", 329 | "\n", 330 | "**From Stateless to Stateful**: Agents maintain memory and can build upon previous interactions and experiences.\n", 331 | "\n", 332 | "**From Single-turn to Multi-turn**: Agents engage in extended interactions, building complex solutions through multiple steps.\n", 333 | "\n", 334 | "💡 **Tip:** Think of the difference between a traditional LLM and an AI agent like the difference between a knowledgeable librarian who can only answer questions when asked, versus a research assistant who can independently investigate topics, gather information from multiple sources, compile comprehensive reports, and even take action based on their findings." 335 | ] 336 | }, 337 | { 338 | "cell_type": "markdown", 339 | "metadata": {}, 340 | "source": [ 341 | "## [The Thought-Action-Observation Cycle](#toc0_)\n", 342 | "\n", 343 | "At the heart of every AI agent lies a fundamental operational pattern known as the **Thought-Action-Observation (TAO) cycle**. This cycle represents how agents process information, make decisions, and learn from their interactions with the environment.\n", 344 | "\n", 345 | "This framework was formalized and popularized by the **ReAct (Reasoning + Acting)** paper by Shunyu Yao et al., published in **October 2022**. The ReAct approach demonstrated that combining reasoning traces with task-specific actions significantly improves both performance and interpretability of language models in interactive environments." 346 | ] 347 | }, 348 | { 349 | "cell_type": "markdown", 350 | "metadata": {}, 351 | "source": [ 352 | "" 353 | ] 354 | }, 355 | { 356 | "cell_type": "markdown", 357 | "metadata": {}, 358 | "source": [ 359 | "### [Understanding the TAO Framework](#toc0_)\n", 360 | "\n", 361 | "The TAO cycle consists of three interconnected phases that agents continuously iterate through:\n", 362 | "\n", 363 | "**Thought (Reasoning)**: The agent analyzes the current situation, considers available options, and formulates a plan of action based on its goals and understanding of the environment. This internal reasoning process is often made explicit in modern agents, allowing us to see their \"thinking.\"\n", 364 | "\n", 365 | "**Action (Execution)**: The agent executes its planned action, which could involve using a tool, making an API call, retrieving information, performing a calculation, or interacting with external systems.\n", 366 | "\n", 367 | "**Observation (Learning)**: The agent observes the results of its action, updates its understanding of the situation, and incorporates this new information into its decision-making process for the next iteration.\n", 368 | "\n", 369 | "### [The Cycle in Practice](#toc0_)\n", 370 | "\n", 371 | "Let's examine how this cycle works with a practical example. Imagine an agent tasked with finding the current weather in Tokyo:\n", 372 | "\n", 373 | "**Thought**: \"I need to find the current weather in Tokyo. I should use a weather API to get real-time information.\"\n", 374 | "\n", 375 | "**Action**: The agent calls a weather API with the parameters for Tokyo, Japan.\n", 376 | "\n", 377 | "**Observation**: The agent receives weather data showing it's 22°C and sunny in Tokyo.\n", 378 | "\n", 379 | "The agent then enters another cycle:\n", 380 | "\n", 381 | "**Thought**: \"I have the weather information. Now I should format this data in a user-friendly way.\"\n", 382 | "\n", 383 | "**Action**: The agent formats the response: \"The current weather in Tokyo is 22°C and sunny.\"\n", 384 | "\n", 385 | "**Observation**: The agent confirms the response is complete and formatted appropriately.\n", 386 | "\n", 387 | "### [Why This Cycle Matters](#toc0_)\n", 388 | "\n", 389 | "The TAO cycle is crucial because it enables several key capabilities:\n", 390 | "\n", 391 | "**Iterative Problem Solving**: Complex problems can be broken down into smaller steps, with each cycle building upon the previous one.\n", 392 | "\n", 393 | "**Error Recovery**: If an action doesn't produce the expected result, the agent can observe this and adjust its approach in the next cycle.\n", 394 | "\n", 395 | "**Dynamic Adaptation**: Agents can respond to changing conditions by continuously updating their understanding through observations.\n", 396 | "\n", 397 | "**Goal Achievement**: By repeatedly cycling through thought, action, and observation, agents can work towards complex, multi-step objectives.\n", 398 | "\n", 399 | "❗️ **Important Note:** The TAO cycle is what distinguishes true AI agents from simple chatbots or LLMs. It's the engine that drives autonomous behavior and enables agents to operate independently in complex environments." 400 | ] 401 | }, 402 | { 403 | "cell_type": "markdown", 404 | "metadata": {}, 405 | "source": [ 406 | "## [Historical Timeline: From Concept to Reality](#toc0_)\n", 407 | "\n", 408 | "To better understand how we arrived at today's sophisticated AI agents, let's examine the complete timeline of key developments:\n", 409 | "\n", 410 | "### [The Foundational Era (1950-1990)](#toc0_)\n", 411 | "- **1950**: Alan Turing proposes the Turing Test\n", 412 | "- **1956**: Dartmouth Conference establishes AI as a field\n", 413 | "- **1966**: ELIZA demonstrates conversational AI\n", 414 | "- **1970s**: Expert systems like MYCIN and DENDRAL emerge\n", 415 | "- **1986**: Shakey the Robot combines AI with robotics\n", 416 | "\n", 417 | "### [The Learning Era (1990-2010)](#toc0_)\n", 418 | "- **1992**: TD-Gammon masters backgammon through reinforcement learning\n", 419 | "- **1997**: IBM's Deep Blue defeats world chess champion Garry Kasparov\n", 420 | "- **2005**: Stanley wins DARPA Grand Challenge (autonomous vehicle race)\n", 421 | "\n", 422 | "### [The Deep Learning Revolution (2010-2020)](#toc0_)\n", 423 | "- **2012**: AlexNet revolutionizes computer vision with deep learning\n", 424 | "- **2016**: AlphaGo defeats world Go champion Lee Sedol\n", 425 | "- **2017**: \"Attention Is All You Need\" introduces transformer architecture\n", 426 | "- **2018**: GPT-1 demonstrates language model potential\n", 427 | "- **2019**: GPT-2 shows emergent language capabilities\n", 428 | "- **2020**: GPT-3 achieves unprecedented language understanding\n", 429 | "\n", 430 | "### [The Agentic AI Era (2020-Present)](#toc0_)\n", 431 | "- **2022**: ChatGPT brings AI to mainstream (November)\n", 432 | "- **2022**: LangChain framework launches (October)\n", 433 | "- **2022**: ReAct paper formalizes reasoning-acting approach (October)\n", 434 | "- **2022**: LlamaIndex (GPT Index) created by Jerry Liu (December)\n", 435 | "- **2023**: AutoGPT and BabyAGI demonstrate autonomous agents (March)\n", 436 | "- **2023**: GPT-4 with plugins enables tool use (March)\n", 437 | "- **2024**: Multi-agent systems become production-ready\n", 438 | "- **2025**: Agentic AI enters enterprise mainstream\n", 439 | "\n", 440 | "This timeline shows how each breakthrough built upon previous discoveries, culminating in today's powerful agentic systems that combine the best of language understanding, reasoning, and autonomous action." 441 | ] 442 | }, 443 | { 444 | "cell_type": "markdown", 445 | "metadata": {}, 446 | "source": [ 447 | "## [Real-World Applications and Use Cases](#toc0_)\n", 448 | "\n", 449 | "The evolution from LLMs to autonomous agents has unlocked a vast array of practical applications across industries and domains. These applications demonstrate the transformative potential of agentic AI systems.\n", 450 | "\n", 451 | "### [Business and Enterprise Applications](#toc0_)\n", 452 | "\n", 453 | "**Customer Service Agents**: Modern customer service agents can handle complex inquiries by accessing multiple databases, processing returns, updating account information, and escalating issues appropriately. Unlike traditional chatbots that follow scripted responses, these agents can reason through unique situations and take appropriate actions.\n", 454 | "\n", 455 | "**Research and Analysis Agents**: These agents can conduct comprehensive market research by gathering data from multiple sources, analyzing trends, generating reports, and even creating visualizations. They can work autonomously for hours, compiling information that would take human researchers days to complete.\n", 456 | "\n", 457 | "**Process Automation Agents**: In enterprise environments, agents can manage complex workflows, coordinate between different systems, and handle exceptions that arise during automated processes.\n", 458 | "\n", 459 | "### [Personal Productivity Applications](#toc0_)\n", 460 | "\n", 461 | "**Personal Assistant Agents**: Advanced personal assistants can manage calendars, book travel arrangements, research topics of interest, and even handle email management. They learn from user preferences and can make intelligent decisions on behalf of their users.\n", 462 | "\n", 463 | "**Learning and Education Agents**: These agents can create personalized learning experiences, adapt to individual learning styles, provide tutoring support, and even generate custom educational content based on specific needs.\n", 464 | "\n", 465 | "### [Technical and Development Applications](#toc0_)\n", 466 | "\n", 467 | "**Code Review and Development Agents**: These agents can analyze codebases, suggest improvements, identify potential bugs, and even implement fixes. They can work continuously to maintain code quality and assist development teams.\n", 468 | "\n", 469 | "**DevOps and Monitoring Agents**: In technical environments, agents can monitor system health, respond to alerts, perform routine maintenance tasks, and coordinate responses to incidents.\n", 470 | "\n", 471 | "### [The Competitive Advantage](#toc0_)\n", 472 | "\n", 473 | "Organizations that successfully implement AI agents gain significant competitive advantages:\n", 474 | "\n", 475 | "**24/7 Operation**: Agents work continuously without breaks, enabling round-the-clock productivity.\n", 476 | "\n", 477 | "**Scalability**: A single agent design can be deployed across multiple instances to handle varying workloads.\n", 478 | "\n", 479 | "**Consistency**: Agents perform tasks with consistent quality and adherence to protocols.\n", 480 | "\n", 481 | "**Cost Efficiency**: While requiring initial investment, agents can significantly reduce operational costs over time." 482 | ] 483 | }, 484 | { 485 | "cell_type": "markdown", 486 | "metadata": {}, 487 | "source": [ 488 | "## [Key Differences: LLMs vs. AI Agents](#toc0_)\n", 489 | "\n", 490 | "Understanding the fundamental differences between traditional LLMs and AI agents is essential for appreciating the significance of this technological evolution. These differences extend far beyond simple feature comparisons – they represent entirely different approaches to AI system design and capability.\n", 491 | "\n", 492 | "### [Interaction Patterns](#toc0_)\n", 493 | "\n", 494 | "**LLMs**: Operate in a **request-response pattern**. They wait for human input, process it, and provide a response. Each interaction is typically independent, with limited connection to previous exchanges.\n", 495 | "\n", 496 | "**AI Agents**: Engage in **goal-oriented interactions**. They can initiate conversations, pursue objectives across multiple interactions, and maintain context over extended periods. Agents can work autonomously without constant human guidance.\n", 497 | "\n", 498 | "### [Memory and State Management](#toc0_)\n", 499 | "\n", 500 | "**LLMs**: Generally **stateless** between conversations. While they can maintain context within a single conversation (up to their context window limit), they typically start fresh with each new session.\n", 501 | "\n", 502 | "**AI Agents**: Maintain **persistent memory** and state across interactions. They can remember previous conversations, learn from past experiences, and build upon accumulated knowledge over time.\n", 503 | "\n", 504 | "### [Environmental Interaction](#toc0_)\n", 505 | "\n", 506 | "**LLMs**: Limited to **text-based interactions** within their training parameters. They cannot access external information, use tools, or interact with other systems.\n", 507 | "\n", 508 | "**AI Agents**: Can **interact with their environment** through various tools and interfaces. They can access databases, call APIs, browse the internet, execute code, and integrate with external systems.\n", 509 | "\n", 510 | "### [Problem-Solving Approach](#toc0_)\n", 511 | "\n", 512 | "**LLMs**: Solve problems through **single-step reasoning**. They generate responses based on their training data and the current prompt, but cannot break down complex problems into multiple steps or iterate on solutions.\n", 513 | "\n", 514 | "**AI Agents**: Employ **multi-step reasoning** and iterative problem-solving. They can decompose complex problems, execute partial solutions, evaluate results, and adjust their approach based on feedback.\n", 515 | "\n", 516 | "### [Learning and Adaptation](#toc0_)\n", 517 | "\n", 518 | "**LLMs**: Have **static knowledge** that doesn't change after training. They cannot learn from new experiences or update their understanding based on recent interactions.\n", 519 | "\n", 520 | "**AI Agents**: Can **learn and adapt** through experience. While they may not update their core language model, they can accumulate knowledge, refine their strategies, and improve their performance over time.\n", 521 | "\n", 522 | "### [Autonomy Level](#toc0_)\n", 523 | "\n", 524 | "**LLMs**: **Human-dependent** – require human prompts to function and cannot operate independently.\n", 525 | "\n", 526 | "**AI Agents**: **Semi-autonomous to fully autonomous** – can operate independently, make decisions, and pursue goals with minimal human oversight.\n", 527 | "\n", 528 | "| Aspect | Traditional LLMs | AI Agents |\n", 529 | "|--------|------------------|-----------|\n", 530 | "| **Interaction** | Request-Response | Goal-Oriented |\n", 531 | "| **Memory** | Stateless | Persistent |\n", 532 | "| **Environment** | Text-Only | Multi-Modal Tools |\n", 533 | "| **Problem Solving** | Single-Step | Multi-Step Iterative |\n", 534 | "| **Learning** | Static Knowledge | Dynamic Adaptation |\n", 535 | "| **Autonomy** | Human-Dependent | Semi/Fully Autonomous |" 536 | ] 537 | }, 538 | { 539 | "cell_type": "markdown", 540 | "metadata": {}, 541 | "source": [ 542 | "## [The Future Landscape](#toc0_)\n", 543 | "\n", 544 | "As we stand at the threshold of the agentic AI revolution, it's important to understand the trajectory of this technology and its potential implications for society, business, and individual productivity.\n", 545 | "\n", 546 | "### [Emerging Capabilities](#toc0_)\n", 547 | "\n", 548 | "The next generation of AI agents will possess increasingly sophisticated capabilities:\n", 549 | "\n", 550 | "**Multi-Modal Intelligence**: Future agents will seamlessly work with text, images, audio, and video, enabling them to understand and interact with the world in ways that mirror human perception.\n", 551 | "\n", 552 | "**Advanced Reasoning**: Agents will develop more sophisticated reasoning capabilities, including causal reasoning, counterfactual thinking, and complex problem decomposition.\n", 553 | "\n", 554 | "**Collaborative Intelligence**: We'll see the emergence of multi-agent systems where specialized agents work together, each contributing their unique capabilities to solve complex problems.\n", 555 | "\n", 556 | "**Continuous Learning**: Future agents will be able to continuously update their knowledge and capabilities based on new experiences and changing environments.\n", 557 | "\n", 558 | "### [Societal Impact](#toc0_)\n", 559 | "\n", 560 | "The widespread adoption of AI agents will have profound implications:\n", 561 | "\n", 562 | "**Workplace Transformation**: Many routine and even complex knowledge work tasks will be augmented or automated by AI agents, leading to new job categories and skill requirements.\n", 563 | "\n", 564 | "**Democratization of Expertise**: AI agents will make specialized knowledge and capabilities accessible to individuals and organizations that previously couldn't afford expert consultation.\n", 565 | "\n", 566 | "**Enhanced Human Capability**: Rather than replacing humans, the most successful implementations will augment human capabilities, allowing people to focus on higher-level creative and strategic work.\n", 567 | "\n", 568 | "### [Challenges and Considerations](#toc0_)\n", 569 | "\n", 570 | "As we embrace this technology, we must also address important challenges:\n", 571 | "\n", 572 | "**Ethical Considerations**: Ensuring AI agents operate ethically, respect privacy, and make decisions that align with human values.\n", 573 | "\n", 574 | "**Reliability and Trust**: Building agents that are reliable, predictable, and trustworthy in critical applications.\n", 575 | "\n", 576 | "**Human-Agent Collaboration**: Developing effective patterns for humans and agents to work together productively.\n", 577 | "\n", 578 | "💡 **Tip:** The future belongs to those who can effectively collaborate with AI agents, leveraging their capabilities while providing human judgment, creativity, and ethical oversight." 579 | ] 580 | }, 581 | { 582 | "cell_type": "markdown", 583 | "metadata": {}, 584 | "source": [ 585 | "## [Conclusion](#toc0_)\n", 586 | "\n", 587 | "The evolution from Large Language Models to autonomous AI agents represents one of the most significant advances in artificial intelligence. We've journeyed from passive text generators to proactive, goal-oriented systems capable of reasoning, planning, and taking action in complex environments.\n", 588 | "\n", 589 | "### [Key Takeaways](#toc0_)\n", 590 | "\n", 591 | "**Paradigm Shift**: The transition from LLMs to agents represents a fundamental change in how we conceptualize and deploy AI systems – from reactive tools to proactive partners.\n", 592 | "\n", 593 | "**The TAO Cycle**: The Thought-Action-Observation cycle is the engine that drives autonomous behavior, enabling agents to break down complex problems and work towards solutions iteratively.\n", 594 | "\n", 595 | "**Real-World Impact**: AI agents are already transforming industries and creating new possibilities for automation, productivity, and problem-solving across diverse domains.\n", 596 | "\n", 597 | "**Competitive Advantage**: Organizations that successfully implement AI agents gain significant advantages in terms of efficiency, scalability, and capability.\n", 598 | "\n", 599 | "### [The Road Ahead: LlamaIndex and the Future](#toc0_)\n", 600 | "\n", 601 | "As we stand at this pivotal moment in AI history, **LlamaIndex** has emerged as one of the leading frameworks for building production-ready AI agents. Created by Jerry Liu in **late 2022**, LlamaIndex (originally called GPT Index) was designed specifically to bridge the gap between LLMs and real-world data, making it easier to build agents that can reason over private knowledge bases and take meaningful actions.\n", 602 | "\n", 603 | "**Why LlamaIndex Matters**: Unlike general-purpose frameworks, LlamaIndex was built from the ground up with agentic workflows in mind, providing:\n", 604 | "- Native support for the Thought-Action-Observation cycle\n", 605 | "- Sophisticated retrieval and reasoning capabilities \n", 606 | "- Seamless integration with various data sources\n", 607 | "- Production-ready deployment tools\n", 608 | "\n", 609 | "### [Looking Ahead](#toc0_)\n", 610 | "\n", 611 | "As we continue through this course, we'll dive deeper into the practical aspects of building and deploying AI agents using LlamaIndex. You'll learn how to:\n", 612 | "\n", 613 | "- Set up and configure the LlamaIndex framework\n", 614 | "- Build your first AI agent from scratch using historical best practices\n", 615 | "- Implement the ReAct pattern and TAO cycle in real applications\n", 616 | "- Create sophisticated multi-step reasoning systems\n", 617 | "- Deploy agents in production environments with proper monitoring\n", 618 | "\n", 619 | "The journey from understanding the conceptual foundations to building production-ready AI agents is both challenging and rewarding. By the end of this course, you'll have the knowledge and skills to create AI agents that can autonomously solve complex problems and add significant value to any organization or project.\n", 620 | "\n", 621 | "You'll be part of the next chapter in this remarkable history – the era where AI agents become as commonplace and transformative as the internet was in the 1990s.\n", 622 | "\n", 623 | "❗️ **Important Note:** Remember that with great power comes great responsibility. As you develop AI agents, always consider the ethical implications, ensure robust testing, and design systems that augment rather than replace human judgment in critical decisions.\n", 624 | "\n", 625 | "Welcome to the age of Agentic AI – let's build the future together!" 626 | ] 627 | } 628 | ], 629 | "metadata": { 630 | "kernelspec": { 631 | "display_name": "Python 3", 632 | "language": "python", 633 | "name": "python3" 634 | }, 635 | "language_info": { 636 | "codemirror_mode": { 637 | "name": "ipython", 638 | "version": 3 639 | }, 640 | "file_extension": ".py", 641 | "mimetype": "text/x-python", 642 | "name": "python", 643 | "nbconvert_exporter": "python", 644 | "pygments_lexer": "ipython3", 645 | "version": "3.8.0" 646 | } 647 | }, 648 | "nbformat": 4, 649 | "nbformat_minor": 4 650 | } 651 | --------------------------------------------------------------------------------