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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 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 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. *Email sign-up and proof of purchase required
Table of Contents (22 chapters)
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1
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

Summary

In this chapter, we have introduced the foundational models offered by Google Cloud and which model will serve your use case best. You should be able to call Google Cloud foundational models, such as Gemini Pro, using the LangChain SDK. You should also understand how you can create simple LLM systems that take instructions to perform simple tasks using examples and a limited context passed via an input prompt. As you now know, Google Cloud allows you to connect both open source and partner models using the same logic and approach as in their native models. We also looked at the distinction between an LLM and a chat model interface on LangChain, and we discussed important concepts, such as SystemMessage, PromptTemplate, OutputParser, and callbacks.

We built our first simple chains, and we used key classes – VertexAI, ChatVertexAI, and VertexAIModelGarden – to work with Google Cloud models on LangChain.

In the next chapter, we will look at how you can significantly...

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