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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

Agentic Workflows

In this chapter, we will explore agentic workflows. They are essential for building applications that exhibit dynamic behavior, responding intelligently to changes in the application state.

To grasp the concepts of agentic workflows, we will explore workflow state management, learning how applications transition through different states within a graph. You will then investigate controlled generation techniques, enabling you to specify the desired format for responses from large language models (LLMs).

Then, you will implement agentic RAG, a powerful approach that leverages agents to enhance traditional retrieval augmented generation (RAG) pipelines.

Finally, you will learn how to build agents capable of translating natural language into SQL queries, allowing you to interact with databases using conversational language.

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