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SPRING AI
Build AI applications with Spring Boot and Java
Apache-2.0
ABOUT
Java developers integrating AI into enterprise applications face a fragmented ecosystem of LLM providers, vector stores, and embedding models — each with different APIs, authentication, and configuration. Spring AI provides a consistent, portable abstraction layer similar to Spring's JDBC and ORM templates. Developers declare a dependency on an AI provider, configure a chat client bean, and use a unified API to call OpenAI, Anthropic, Ollama, or any supported provider without changing application code. This is essential infrastructure for Java-based AI services.
INTEGRATION GUIDE
1. Build a Java REST API that queries different LLMs (OpenAI, Anthropic, Ollama) with a single abstraction
2. Implement RAG pipelines in Spring Boot applications using vector store templates for Chroma, Pinecone, or Weaviate
3. Create enterprise document processing workflows that extract, embed, and index documents for semantic search
4. Develop conversational AI agents with tool calling and memory persistence in a Spring Boot microservice
5. Integrate AI-powered features into existing Java enterprise applications without replacing the tech stack
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javaspringaiframeworkenterprisellmvector-database