This repository provides source code examples for the book "LangChain and LangGraph for RAG and AI Agents: A Practical Introduction." It targets developers and researchers looking to build practical AI applications using Retrieval-Augmented Generation (RAG) and agent-based systems with LangChain and LangGraph. The benefit is hands-on code to implement advanced RAG techniques and design patterns for AI agents.

How It Works

The project is structured around chapters of the accompanying book, with each directory containing source code for specific topics. It covers foundational concepts of LLM application development, prompt engineering, and the core components of LangChain, including LangChain Expression Language (LCEL). Advanced sections delve into RAG implementation, evaluation using LangSmith, and the creation of AI agents using LangGraph, demonstrating practical agent design patterns.

Quick Start & Requirements

• Install: Primarily uses pip for package installation.
• Prerequisites: Python 3.10.12 is confirmed. Specific package versions are detailed in requirements.txt files within each chapter's directory.
• Environment: Google Colab is the primary tested environment.
• Notes: Users may need to install specific versions of httpx, openai, pydantic, and numpy to avoid compatibility errors, as detailed in the README. Links to the book on Amazon and the publisher are provided.

Highlighted Details

• Comprehensive coverage of LangChain and LangGraph for RAG and AI agents.
• Practical examples for implementing agent design patterns.
• Guidance on evaluating RAG applications using LangSmith.
• Troubleshooting common version-related errors in Google Colab.

Maintenance & Community

Issues and errata for the book and code can be reported via the GitHub Issues page: https://github.com/GenerativeAgents/agent-book/issues. Information on post-publication updates and errata is available via the publisher.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the provided README. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The code is tied to specific versions of Python packages, requiring careful management to avoid compatibility issues. Some advanced features, like using gpt-4o for synthetic data generation, may encounter rate limit errors depending on OpenAI API usage tiers. The README notes potential issues with newer package versions and provides workarounds.