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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

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19. Other Books You May Enjoy

Appendix 1: Overview of Generative AI

1. Appendix 2: Google Cloud Foundations

Part 2: Hallucinations and Grounding Responses

One of the main concerns when building generative AI applications is hallucinations and how to keep LLMs fresh in line with changes and new knowledge in the outside world. We’re going to discuss key patterns to ground the responses and implement memory layers outside of the LLM itself.

This part has the following chapters:

Chapter 3, Grounding Responses on Google Cloud
Chapter 4, Vector Search on Google Cloud
Chapter 5, Advanced Techniques for Parsing and Ingesting Documents
Chapter 6, Multimodality

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

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

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

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