Turn challenges into opportunities by learning advanced techniques for text generation, summarization, and question answering using LangChain and Google Cloud tools
Key Features
- Solve real-world business problems with hands-on examples of GenAI applications on Google Cloud
- Learn repeatable design patterns for Gen AI on Google Cloud with a focus on architecture and AI ethics
- Build and implement GenAI agents and workflows, such as RAG and NL2SQL, using LangChain and Vertex AI
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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.
What you will learn
- Build enterprise-ready applications with LangChain and Google Cloud
- Navigate and select the right Google Cloud generative AI tools
- Apply modern design patterns for generative AI applications
- Plan and execute proof-of-concepts for enterprise AI solutions
- Gain hands-on experience with LangChain’s and Google Cloud’s AI products
- Implement advanced techniques for text generation and summarization
- Leverage Vertex AI Search and other tools for scalable AI solutions
Who this book is for
If you’re an application developer or ML engineer eager to dive into GenAI, this book is for you. Whether you’re new to LangChain or Google Cloud, you’ll learn how to use these tools to build scalable AI solutions. This book is ideal for developers familiar with Python and machine learning basics looking to apply their skills in GenAI. Professionals who want to explore Google Cloud’s powerful suite of enterprise-grade GenAI products and their implementation will also find this book useful.
Table of Contents
- Using LangChain with Google Cloud
- Foundational Models on Google Cloud
- Grounding Responses on Google Cloud
- Vector Search on Google Cloud
- Advanced Techniques for Parsing and Ingesting Documents
- Multimodality
- Working with Long Context
- Building Chatbots
- Tools and Function Calling
- Agents in Generative AI
- Agentic Workflows
- Evaluating GenAI Applications
- GenAI System Design
- Appendix 1 – Overview of Generative AI
- Appendix 2 – Google Cloud Foundations
About the Author
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.
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.
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.
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