Summary

This repository provides code examples for the Packt book "Building AI Agents with LLMs, RAG, and Knowledge Graphs." It empowers developers to create AI agents that ground responses in real data and take actions, moving beyond simple text generation. Targeting engineers and researchers, it offers a practical guide to building autonomous, intelligent agents capable of complex reasoning and problem-solving by integrating LLMs with external data and structured knowledge.

How It Works

The project demonstrates an agent-based architecture combining Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) for factual accuracy and knowledge graphs for contextual reasoning. It focuses on enabling agents to plan, use tools, and retrieve information dynamically to achieve complex goals, leveraging popular Python libraries for implementation. This approach aims to enhance reliability and reasoning capabilities by grounding LLM outputs in external data and structured knowledge.

Quick Start & Requirements

• Installation: Clone the repository (git clone https://github.com/PacktPublishing/Modern-AI-Agents.git) and navigate into the directory (cd modern-ai-agents).
• Software: Python 3.10+, PyTorch/Transformers, Streamlite, Docker.
• Operating System: Windows, macOS, or Linux.
• Dependencies: Specific hardware requirements are not detailed. Links to Colab/Kaggle/Gradient/Studio Lab notebooks are provided per chapter within the README, but not for a unified setup.

Highlighted Details

• Practical roadmap for building AI agents from concept to implementation.
• Concrete Python examples and real-world case studies.
• Techniques for minimizing hallucinations and ensuring output accuracy.
• Methods for orchestrating single- and multi-agent systems to solve complex tasks.
• Guidance on optimizing prompts, memory, and context handling for long-running agents.
• Covers deployment and monitoring strategies for production environments.

Maintenance & Community

The repository accompanies a book authored by AI specialists Salvatore Raieli (drug discovery, LLMs) and Gabriele Iuculano (embedded systems, AI). No specific community channels (e.g., Discord, Slack) or explicit maintenance schedules are detailed in the README.

Licensing & Compatibility

The README does not explicitly state the software license for the code repository. This omission requires clarification for commercial use or integration into closed-source projects.

Limitations & Caveats

The repository serves as a code companion to a published book, and its scope is defined by the book's content. Specific limitations, such as alpha status, known bugs, or unsupported platforms, are not detailed. The absence of a clear software license is a notable caveat for adoption.