Write code once and switch between providers (OpenAI, Ollama, Anthropic, Azure, Amazon Bedrock) via simple configuration changes.
Introduction Enterprise Java development is undergoing a massive shift. Integrating Artificial Intelligence (AI) into enterprise applications used to require complex Python microservices, heavy data pipelines, and brittle REST connections. The emergence of changes this landscape entirely.
For developers searching for practical implementations, curated resources, and comprehensive guides, repository collections and documentation like a "Spring AI in Action PDF GitHub" style resource represent the ultimate blueprint for production-ready development. This article serves as an extensive, actionable guide to mastering Spring AI, mirroring the structured, hands-on approach you would find in an open-source reference manual. 1. What is Spring AI?
Prompts are structured inputs sent to the AI model. PromptTemplate allows you to create dynamic, reusable text structures using placeholders, similar to view resolution engines like Thymeleaf. EmbeddingModel spring ai in action pdf github
| Your Goal | Best Resource (Search term) | Format | | :--- | :--- | :--- | | | spring-ai-reference.pdf | PDF (Generated from docs) | | Copy-paste RAG code | github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/test | GitHub Source | | Troubleshooting prompts | github.com/rd-1-2025/spring-ai-workshop | GitHub (Workshop) | | Production deployment | spring-ai-kubernetes-example by dashaun | GitHub Repo | | Cheat sheet | spring-ai-cheatsheet.pdf (gist.github.com) | PDF (1 page) |
The accompanying GitHub repository provides a wealth of code examples and sample projects that demonstrate how to use Spring AI in real-world applications. The repository includes:
This is the official source code for the Spring AI library itself. It's not specifically the book's code, but the framework the book teaches. Managed by VMware (Spring's maintainer), this repository contains the portable API that standardizes interactions with various AI model providers and vector databases. Write code once and switch between providers (OpenAI,
Your search for tells us you are serious about bridging theory and practice. Here is your actionable roadmap:
org.springframework.ai spring-ai-bom 1.0.0-M1 pom import org.springframework.boot spring-boot-starter-web org.springframework.ai spring-ai-openai-starter Use code with caution. Application Configuration
: Specifically tailored for Spring developers with no prior Generative AI skills. The emergence of changes this landscape entirely
The team decided to use the following technology stack:
Spring AI uses familiar Spring ecosystem design principles. These principles include portability, modular design, and POJO-centric development. It offers an abstraction layer. This layer allows developers to interact with major AI providers, such as OpenAI, Google Gemini, and Anthropic. This interaction occurs without being tied to a specific vendor's SDK.
The book by Craig Walls, published by Manning Publications , is widely praised as an essential guide for Java developers looking to integrate Generative AI without switching to Python. Released in late 2025, it covers building AI-native applications using Spring AI and Spring Boot. Core Review Highlights