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Generative AI with LangChain
Generative AI with LangChain

Generative AI with LangChain: Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph , Second Edition

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Profile Icon Ben Auffarth Profile Icon Leonid Kuligin
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$44.99
Paperback May 2025 476 pages 2nd Edition
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Arrow left icon
Profile Icon Ben Auffarth Profile Icon Leonid Kuligin
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$44.99
Paperback May 2025 476 pages 2nd Edition
eBook
$35.99
Paperback
$44.99
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Free Trial
Renews at $19.99p/m
eBook
$35.99
Paperback
$44.99
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Renews at $19.99p/m

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Generative AI with LangChain

Summary

This chapter explored the transformative impact of generative AI and LLMs, covering their technical foundations and evolution into agentic applications. We examined the key players in the LLM ecosystem, the shift toward open-source AI, and the emergence of agentic AI as a new paradigm in artificial intelligence.

Looking ahead, Chapter 2 will introduce LangChain, a powerful framework designed to bridge the gap between LLMs and practical, production-ready AI applications. We will explore the fundamentals of LangChain, walk through its setup, and take our first steps in building AI-driven applications. As we progress, we will evolve from basic use cases to complex multi-agent architectures, demonstrating how LangChain can be used to harness the full potential of generative AI in real-world scenarios.

Review Questions

  1. What are the key differences between traditional AI systems and generative AI?
  2. How do Large Language Models (LLMs) generate human-like text, and what are their main capabilities?
  3. What technological advancements have contributed to the rise of generative AI?
  4. Why is the open-source revolution significant in the development of AI models?
  5. What is agentic AI, and how does it differ from traditional LLM-based systems?
  6. What are some real-world applications of agentic AI in different industries?
  7. What are the challenges associated with agentic AI, and how can they be addressed?
  8. How does LangChain enhance the capabilities of LLMs, and why is it an essential tool for building AI applications?

Exploring LangChain’s building blocks

To build practical applications, we need to know how to work with different model providers. Let’s explore the various options available, from cloud services to local deployments. We’ll start with fundamental concepts like LLMs and chat models, then dive into prompts, chains, and memory systems.

Model interfaces

LangChain provides a unified interface for working with various LLM providers. This abstraction makes it easy to switch between different models while maintaining a consistent code structure. The following examples demonstrate how to implement LangChain’s core components in practical scenarios.

Please note that users should almost exclusively be using the newer chat models as most model providers have adopted a chat-like interface for interacting with language models. We still provide the LLM interface, because it’s very easy to use as string-in, string-out.

LLM interaction...

Running local models

When building LLM applications with LangChain, you need to decide where your models will run.

  • Advantages of local models:
    • Complete data control and privacy
    • No API costs or usage limits
    • No internet dependency
    • Control over model parameters and fine-tuning
  • Advantages of cloud models:
    • No hardware requirements or setup complexity
    • Access to the most powerful, state-of-the-art models
    • Elastic scaling without infrastructure management
    • Continuous model improvements without manual updates
  • When to choose local models:
    • Applications with strict data privacy requirements
    • Development and testing environments
    • Edge or offline deployment scenarios
    • Cost-sensitive applications with predictable, high-volume usage

Let’s start with one of the most developer-friendly options for running local models.

Getting started with Ollama

Ollama provides a developer-friendly way to run powerful open-source models locally. It provides a simple interface for...

Multimodal AI applications

AI systems have evolved beyond text-only processing to work with diverse data types. In the current landscape, we can distinguish between two key capabilities that are often confused but represent different technological approaches.

Multimodal understanding represents the ability of models to process multiple types of inputs simultaneously to perform reasoning and generate responses. These advanced systems can understand the relationships between different modalities, accepting inputs like text, images, PDFs, audio, video, and structured data. Their processing capabilities include cross-modal reasoning, context awareness, and sophisticated information extraction. Models like Gemini 2.5, GPT-4V, Sonnet 3.7, and Llama 4 exemplify this capability. For instance, a multimodal model can analyze a chart image along with a text question to provide insights about the data trend, combining visual and textual understanding in a single processing flow.

Content...

Summary

After setting up our development environment and configuring necessary API keys, we’ve explored the foundations of LangChain development, from basic chains to multimodal capabilities. We’ve seen how LCEL simplifies complex workflows and how LangChain integrates with both text and image processing. These building blocks prepare us for more advanced applications in the coming chapters.

In the next chapter, we’ll expand on these concepts to create more sophisticated multimodal applications with enhanced control flow, structured outputs, and advanced prompt techniques. You’ll learn how to combine multiple modalities in complex chains, incorporate more sophisticated error handling, and build applications that leverage the full potential of modern LLMs.

Review questions

  1. What are the three main limitations of raw LLMs that LangChain addresses?
    • Memory limitations
    • Tool integration
    • Context constraints
    • Processing speed
    • Cost optimization
  2. Which of the following best describes the purpose of LCEL (LangChain Expression Language)?
    • A programming language for LLMs
    • A unified interface for composing LangChain components
    • A template system for prompts
    • A testing framework for LLMs
  3. Name three types of memory systems available in LangChain
  4. Compare and contrast LLMs and chat models in LangChain. How do their interfaces and use cases differ?
  5. What role do Runnables play in LangChain? How do they contribute to building modular LLM applications?
  6. When running models locally, which factors affect model performance? (Select all that apply)
    • Available RAM
    • CPU/GPU capabilities
    • Internet connection speed
    • Model quantization level
    • Operating system type
  7. Compare the following model deployment options and identify scenarios where each would...

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Key benefits

  • Bridge the gap between prototype and production with robust LangGraph agent architectures
  • Apply enterprise-grade practices for testing, observability, and monitoring
  • Build specialized agents for software development and data analysis
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

This second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines. You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs—complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy. Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.

Who is this book for?

This book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it’s especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.

What you will learn

  • Design and implement multi-agent systems using LangGraph
  • Implement testing strategies that identify issues before deployment
  • Deploy observability and monitoring solutions for production environments
  • Build agentic RAG systems with re-ranking capabilities
  • Architect scalable, production-ready AI agents using LangGraph and MCP
  • Work with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-mini
  • Design secure, compliant AI systems aligned with modern ethical practices
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Publication date : May 23, 2025
Length: 476 pages
Edition : 2nd
Language : English
ISBN-13 : 9781837022014
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Table of Contents

12 Chapters
The Rise of Generative AI: From Language Models to Agents Chevron down icon Chevron up icon
First Steps with LangChain Chevron down icon Chevron up icon
Building Workflows with LangGraph Chevron down icon Chevron up icon
Building Intelligent RAG Systems Chevron down icon Chevron up icon
Building Intelligent Agents Chevron down icon Chevron up icon
Advanced Applications and Multi-Agent Systems Chevron down icon Chevron up icon
Software Development and Data Analysis Agents Chevron down icon Chevron up icon
Evaluation and Testing Chevron down icon Chevron up icon
Production-Ready LLM Deployment and Observability Chevron down icon Chevron up icon
The Future of Generative Models: Beyond Scaling Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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