Spring AI provides a Java framework for building generative AI applications, targeting enterprise developers who want to integrate AI models with existing Java infrastructure. It offers portable APIs for interacting with various AI models and vector databases, simplifying complex AI integrations and promoting reusable patterns.

How It Works

Spring AI leverages Spring's ecosystem principles, like POJO-based development and modular design, to create a familiar and productive environment for Java developers. It abstracts away the complexities of different AI provider APIs and vector store interactions through portable interfaces. Key features include a ChatClient for conversational AI, an Advisors API for common AI patterns like RAG, and support for structured output mapping to Java objects.

Quick Start & Requirements

• Install: Add Spring AI starters to your Spring Boot project via start.spring.io.
• Prerequisites: Java 17+, Maven, and API keys for desired AI model providers (e.g., OpenAI, Anthropic, Google).
• Resources:
  • Getting Started Guide
  • Reference Documentation
  • Examples

Highlighted Details

• Supports major AI providers (OpenAI, Google, AWS, Azure, Anthropic) and model types (Chat, Embeddings, Image, Audio).
• Integrates with numerous vector databases (PostgreSQL/PGVector, Pinecone, Redis, Chroma, etc.) via a portable API and SQL-like metadata filtering.
• Features include structured output mapping to POJOs, tool/function calling, observability, document ETL, and AI model evaluation.
• Provides RAG and conversation memory support through the Advisors API.

Maintenance & Community

The project is part of the Spring ecosystem, indicating strong backing and community support. Resources include a curated list of "Awesome Spring AI" and example projects.

Licensing & Compatibility

Apache License 2.0. This license is permissive and generally compatible with commercial and closed-source applications.

Limitations & Caveats

The repository contains large model files, requiring Git LFS or skipping smudge during cloning. Integration tests require setting API keys and may be skipped if keys are not configured. The project is actively developed, with recent breaking changes noted in upgrade notes.