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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

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Product type Paperback
Published in Dec 2024
Publisher Packt
ISBN-13 9781835889329
Length 306 pages
Edition 1st Edition
Concepts
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Author (1):
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Leonid Kuligin Leonid Kuligin
Author Profile Icon Leonid Kuligin
Leonid Kuligin
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Table of Contents (22) Chapters Close

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 19. Other Books You May Enjoy Appendix 1: Overview of Generative AI 1. Appendix 2: Google Cloud Foundations

Grounding Responses

Hallucinations are one of the key problems in large language models (LLMs). In this chapter, we’re going to discuss what that means and how you can reduce the amount of hallucinations. We will discuss closed-book and open-book question-answering, and how retrieval augmented generation (RAG) is gaining popularity. If the concept of RAG is new to you, please do not worry as we’ll discuss it in this chapter.

We’ll also look at a managed Google Cloud service – Vertex AI Agent Builder – that enables you to build RAG-based applications that use a custom corpus of data or documents. A classical RAG application consists of two steps – based on the query, retrieving relevant passages from a large corpus of documents, and then passing these passages as a context in a prompt to the LLM to generate a full answer. We’ll discuss the key steps of building an RAG application and focus on ways to improve context preparation for...

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