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Generative AI on Google Cloud with LangChain

You're reading from Generative AI on Google Cloud with LangChain Design scalable generative AI solutions with Python, LangChain, and Vertex AI on Google Cloud

Product type Paperback

Published in Dec 2024

Publisher Packt

ISBN-13 9781835889329

Length 306 pages

Edition 1st Edition

Concepts

GPT/LLMs

Author (1):

Leonid Kuligin

View More author details

Table of Contents (22) Chapters

Preface

1. Part 1: Intro to LangChain and Generative AI on Google Cloud

2. Chapter 1: Using LangChain with Google Cloud FREE CHAPTER

3. Chapter 2: Foundational Models on Google Cloud

4. Part 2: Hallucinations and Grounding Responses

5. Chapter 3: Grounding Responses

6. Chapter 4: Vector Search on Google Cloud

7. Chapter 5: Ingesting Documents

8. Chapter 6: Multimodality

9. Part 3: Common Generative AI Architectures

10. Chapter 7: Working with Long Context

11. Chapter 8: Building Chatbots

12. Chapter 9: Tools and Function Calling

13. Chapter 10: Agents

14. Chapter 11: Agentic Workflows

15. Part 4: Designing Generative AI Applications

16. Chapter 12: Evaluating GenAI Applications

17. Chapter 13: Generative AI System Design

18. Index

Why subscribe?

19. Other Books You May Enjoy

Appendix 1: Overview of Generative AI

1. Appendix 2: Google Cloud Foundations

Part 3: Common Generative AI Architectures

Many LLMs have limitations on how many tokens they can process. If the size of the input document exceeds the token limit of the model, additional steps need to be taken to bypass this limit. Even though many models increase their token limits and larger and larger texts could be fed into the model to generate a summary, the techniques described in this part are still relevant to optimize your applications for speed and performance.

This part has the following chapters:

Chapter 7, Working with Long Context
Chapter 8, Building Chatbots
Chapter 9, Tools and Function Calling
Chapter 10, Agents in Generative AI
Chapter 11, Agentic Workflows

The rest of the chapter is locked

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Authors (3)

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

Maximilian Tschochohei

Maximilian Tschochohei

Maximilian Tschochohei leads AI engineering at Google Cloud Consulting EMEA. He implements LangChain applications together with Google Cloud customers. Before Google, he worked in strategy and technology consulting with Boston Consulting Group.

See other products by Maximilian Tschochohei

Jorge Zaldívar

Jorge Zaldívar

Jorge Zaldivar is an AI Engineer at Google and also a contributor to LangChain's integrations with Google. He has a decade of experience building complex Machine Learning applications and products applied to the energy and financial industries.

See other products by Jorge Zaldívar

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