To get the most out of this book

Before diving in, it’s helpful to ensure you have a few things in place to make the most of your learning experience. This book is designed to be hands-on and practical, so having the right environment, tools, and mindset will help you follow along smoothly and get the full value from each chapter. Here’s what we recommend:

• Environment requirements: Set up a development environment with Python 3.10+ on any major operating system (Windows, macOS, or Linux). All code examples are cross-platform compatible and thoroughly tested.
• API access (optional but recommended): While we demonstrate using open-source models that can run locally, having access to commercial API providers like OpenAI, Anthropic, or other LLM providers will allow you to work with more powerful models. Many examples include both local and API-based approaches, so you can choose based on your budget and performance needs.
• Learning approach: We recommend typing the code yourself rather than copying and pasting. This hands-on practice reinforces learning and encourages experimentation. Each chapter builds on concepts introduced earlier, so working through them sequentially will give you the strongest foundation.
• Background knowledge: Basic Python proficiency is required, but no prior experience with machine learning or LLMs is necessary. We explain key concepts as they arise. If you’re already familiar with LLMs, you can focus on the implementation patterns and production-readiness aspects that distinguish this book.

  Software/Hardware covered in the book

  Python 3.10+

  LangChain 0.3.1+

  LangGraph 0.2.10+

  Various LLM providers (Anthropic, Google, OpenAI, local models)

You’ll find detailed guidance on environment setup in Chapter 1, along with clear explanations and step-by-step instructions to help you get started. We strongly recommend following these setup steps as outlined—given the fast-moving nature of LangChain, LangGraph and the broader ecosystem, skipping them might lead to avoidable issues down the line.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/benman1/generative_ai_with_langchain. We recommend typing the code yourself or using the repository as you progress through the chapters. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781837022014.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “Let’s also restore from the initial checkpoint for thread-a. We’ll see that we start with an empty history:”

A block of code is set as follows:

checkpoint_id = checkpoints[-1].config["configurable"]["checkpoint_id"]

_ = graph.invoke(

[HumanMessage(content="test")],

config={"configurable": {"thread_id": "thread-a", "checkpoint_id": checkpoint_id}})

Any command-line input or output is written as follows:

$ pip install langchain langchain-openai

Bold: Indicates a new term, an important word, or words that you see on the screen. For instance, words in menus or dialog boxes appear in the text like this. For example: “ The Google Research team introduced the Chain-of-Thought (CoT) technique early in 2022.”