Generative AI on Google Cloud with LangChain 1st Edition by Leonid Kuligin ISBN 9781835889329 by Leonid Kuligin & Jorge Zaldvar & Maximilian Tschochohei instant download after payment.

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ISBN 13: 9781835889329
Author: Leonid Kuligin

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.

Generative AI on Google Cloud with LangChain 1st Table of contents:

Part 1: Intro to LangChain and Generative AI on Google Cloud
Chapter 1: Using LangChain with Google Cloud
Technical requirements
LangChain
LangChain primitives – chains and runnables
LCEL
RunnablePassThrough and itemgetter
Main LangChain building blocks
Data structures
Interfaces
LangChain with Google Cloud
LangChain integration with Google
Summary
References
Chapter 2: Foundational Models on Google Cloud
Technical requirements
What is Vertex AI?
Google Cloud console
Using LangChain
Prompt templates
Using chat models
Using callbacks
Other models besides Gemini
Vertex AI Model Garden
Open-source models
Partner models
Prompt engineering for foundational models
Summary
References
Part 2: Hallucinations and Grounding Responses
Chapter 3: Grounding Responses
Technical requirements
Overcoming hallucinations in LLMs
Understanding RAG applications – closed-book versus open-book question-answering
Vertex AI Agent Builder
Creating a data store and indexing documents
Querying Vertex AI Agent Builder
Using Vertex AI Agent Builder
Building a custom RAG application
Customizing the RAG application further
Query expansion
Filtering documents
Further methods
Summary
References
Chapter 4: Vector Search on Google Cloud
Technical requirements
What is vector search?
Overview of vector search architecture
LangChain interfaces – embeddings and vector stores
Embeddings
Vector stores
Vector store with Vertex AI Vector Search
Creating the required infrastructure
LangChain integration
Vectorstore with pgvector on Cloud SQL
Creating a CloudSQL instance and database
Enabling the pgVector extension
LangChain integration
Vectorstore with BigQuery
Creating a BigQuery dataset, table, and vector index
LangChain integration
Summary
References
Chapter 5: Ingesting Documents
Technical requirements
Ingesting documents with LangChain
Plain text documents
CSV documents
PDF documents
GoogleDriveLoader
Additional types
Loading directories
Integrating with Google Cloud Storage
Document chunking
RecursiveCharacterTextSplitter
Other Text Splitters
Applying text splitters
Parsing documents with Document AI
LangChain usage
Ingesting data with Vertex AI Agent Builder
Website data
Unstructured Data
Structured data
Parsing
Chunking
Summary
Links
Chapter 6: Multimodality
Technical requirements
Multimodality with Gemini and LangChain
Using Imagen with LangChain
Developing multimodal RAGs
Extracting images from PDF documents
Multimodal context
Parsing an image
Summary
References

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Tags: Leonid Kuligin, Generative, Google