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Generative AI with LangChain

You're reading from Generative AI with LangChain Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph

Product type Paperback

Published in May 2025

Publisher Packt

ISBN-13 9781837022014

Length 476 pages

Edition 2nd Edition

Languages

Python

Tools

LangChain

Concepts

GPT/LLMs

Authors (2):

Ben Auffarth

Leonid Kuligin

View More author details

Table of Contents (14) Chapters

Preface

1. The Rise of Generative AI: From Language Models to Agents

2. First Steps with LangChain FREE CHAPTER

3. Building Workflows with LangGraph

4. Building Intelligent RAG Systems

5. Building Intelligent Agents

6. Advanced Applications and Multi-Agent Systems

7. Software Development and Data Analysis Agents

8. Evaluation and Testing

9. Production-Ready LLM Deployment and Observability

10. The Future of Generative Models: Beyond Scaling

11. Other Books You May Enjoy

12. Index

Appendix

Economic and industry transformation

Integrating generative AI promises immense productivity gains through automating tasks across sectors, while potentially causing workforce disruptions due to the pace of change. According to PwC’s 2023 Global Artificial Intelligence Impact Index and JPMorgan’s 2024 The Economic Impact of Generative AI reports, AI could contribute up to $15.7 trillion to the global economy by 2030, boosting global GDP by up to 14%. This economic impact will be unevenly distributed, with China potentially seeing a 26% GDP boost and North America around 14%. The sectors expected to see the highest impact include (in order):

Healthcare
Automotive
Financial services
Transportation and logistics

JPM’s report highlights that AI is more than simple automation—it fundamentally enhances business capabilities. Future gains will likely spread across the economy as technology sector leadership evolves and innovations diffuse...

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Authors (2)

Ben Auffarth

Ben Auffarth

Ben Auffarth is a full-stack data scientist with more than 15 years of work experience. With a background and Ph.D. in computational and cognitive neuroscience, he has designed and conducted wet lab experiments on cell cultures, analyzed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores, built production systems processing hundreds and thousands of transactions per day, and trained language models on a large corpus of text documents. He co-founded and is the former president of Data Science Speakers, London.

See other products by Ben Auffarth

Leonid Kuligin

Leonid Kuligin

Leonid Kuligin is a staff AI engineer at Google Cloud, working on generative AI and classical machine learning solutions (such as demand forecasting or optimization problems). Leonid is one of the key maintainers of Google Cloud integrations on LangChain, and a visiting lecturer at CDTM (TUM and LMU). Prior to Google, Leonid gained more than 20 years of experience in building B2C and B2B applications based on complex machine learning and data processing solutions such as search, maps, and investment management in German, Russian, and US technological, financial, and retail companies.

See other products by Leonid Kuligin

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