- 1. What Spring AI Actually Does
- 2. Prompts, and What the Model Really Sees
- 3. ChatClient in a Real Spring Boot App
- 4. Java Objects, Not Strings
- 5. Conversations That Remember
- 6. Embeddings: Teaching a Computer What "Similar" Means
- 7. Vector Stores: Semantic Search on PostgreSQL
- 8. RAG: Answering From Your Own Documents
- 9. Tool Calling: Letting the Model Use Your Code
- 10. MCP: Sharing Tools Between AI Applications
- 11. Testing What You Cannot Predict
- 12. Observability, Cost and Latency
- 13. Preparing for Production
- Appendix A. Gotchas, and What Fixed Them
Spring AI
A Plain-English Guide for Java Developers
This book is 100% completeLast updated on 2026-09-21
Spring AI gives you one API for chat models, embeddings, vector stores and tools, and swaps the provider with a single property. This book teaches it by building: thirteen chapters, seventeen complete Spring Boot projects, and every program run on a free local model and on a paid cloud model before a word was written about it. Start chapter 1 with no account, no API key and no credit card.
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About
About the Book
Use AI models in Java the way you already use databases.
Spring AI gives you one API for chat models, embeddings, vector stores and tools, and swaps the provider with a property. This book teaches it by building, not by listing features: thirteen chapters, seventeen complete Spring Boot projects, and every program run on a free local model and on a paid cloud model before a word was written about it.
WHAT YOU GET
- The complete book as a colour PDF, 354 pages, fixed layout: every diagram, code listing and table exactly as designed
- An EPUB of the same book, for phones and e-readers
- The code: 17 complete Maven projects, one per chapter, free and MIT licensed at https://github.com/raj-ansh/spring-ai-examples
- Appendix A: 113 gotchas with the exact error messages that led to them
- Free updates for this edition, whenever a new version is published
WHAT YOU BUILD
- A first chat program that runs with no API key at all
- Prompts that hold their rules under pressure
- Java objects out of a model, validated, with no string parsing
- A conversation that remembers, without leaking one user into another
- Embeddings, and a search that understands meaning
- A vector store on PostgreSQL and pgvector, in Docker
- Retrieval-augmented generation over your own PDFs
- Tools the model can call, including one that needs your approval first
- An MCP server and client, talking to each other
- A four-layer test suite, two layers of which need no model
- Metrics, traces and a cost meter, per feature
- Timeouts, retries, concurrency limits, redaction and secrets, ready for production
HONEST NOTES
Which versions? Spring AI 2.0.1, Spring Boot 4.1.1, Spring Framework 7.0.9, Java 21. Every number in the book was measured on that stack. Spring AI 2.0 will not load under Spring Boot 3.x at all, and most material published about it online is still pre-2.0 and wrong.
Do I need an API key? No. Every chapter runs on a free local model through Ollama. An OpenAI key is optional, and the book shows the same code on both.
Is the code really complete? Yes: one Maven project per chapter, not snippets. It is public, so you can read it before you buy the book: https://github.com/raj-ansh/spring-ai-examples
Can I read a chapter first? Yes. Chapter 1 is free above, and it is the chapter that gets a working AI program running on your own machine in about fifteen minutes, with no account and no API key.
Why a PDF as well as the EPUB? The figures and listings are laid out for the page, and a fixed-layout PDF keeps them that way, in colour, on any screen. Both files come with the book, so read whichever suits the device.
The paperback and Kindle editions are on Amazon if you prefer print.
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About the Author
Rajkumar Panda
Rajkumar is a Java architect with ten years of experience building production systems in Java and Spring Boot, and has worked across PHP, Angular and AWS. He wrote *Spring AI: A Plain-English Guide for Java Developers* the way he learns: by building the thing, running it, and writing down what actually happened, including the parts that failed. Every project in the book runs on a free local model, so any developer canfollow it without a credit card. The code is public at
https://github.com/raj-ansh/spring-ai-examples
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