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  • Book Overview & Buying Generative AI on Google Cloud with LangChain
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Generative AI on Google Cloud with LangChain

Generative AI on Google Cloud with LangChain

By : Leonid Kuligin, Jorge Zaldívar, Maximilian Tschochohei
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Generative AI on Google Cloud with LangChain

Generative AI on Google Cloud with LangChain

2 (1)
By: Leonid Kuligin, Jorge Zaldívar, Maximilian Tschochohei

Overview of this book

The rapid transformation and enterprise adoption of GenAI has created an urgent demand for developers to quickly build and deploy AI applications that deliver real value. Written by three distinguished Google AI engineers and LangChain contributors who have shaped Google Cloud’s integration with LangChain and implemented AI solutions for Fortune 500 companies, this book bridges the gap between concept and implementation, exploring LangChain and Google Cloud’s enterprise-ready tools for scalable AI solutions. You'll start by exploring the fundamentals of large language models (LLMs) and how LangChain simplifies the development of AI workflows by connecting LLMs with external data and services. This book guides you through using essential tools like the Gemini and PaLM 2 APIs, Vertex AI, and Vertex AI Search to create sophisticated, production-ready GenAI applications. You'll also overcome the context limitations of LLMs by mastering advanced techniques like Retrieval-Augmented Generation (RAG) and external memory layers. Through practical patterns and real-world examples, you’ll gain everything you need to harness Google Cloud’s AI ecosystem, reducing the time to market while ensuring enterprise scalability. You’ll have the expertise to build robust GenAI applications that can be tailored to solve real-world business challenges.
Table of Contents (22 chapters)
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Part 1: Intro to LangChain and Generative AI on Google Cloud
4
Part 2: Hallucinations and Grounding Responses
9
Part 3: Common Generative AI Architectures
15
Part 4: Designing Generative AI Applications

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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