This repository is a curated list of resources for developers building generative AI applications with Spring AI. It targets Java developers within the Spring ecosystem, aiming to simplify LLM integration and provide a consistent, Spring-style developer experience.

How It Works

Spring AI offers consistent abstractions across various AI providers, simplifying LLM integration into Spring applications. It supports popular LLM providers, robust prompt engineering, built-in caching and retry mechanisms, vectorized storage integration, and streaming responses, all with native Spring Boot integration.

Quick Start & Requirements

• Install/Run: Not directly applicable as this is a resource list. Project examples may have specific setup instructions.
• Prerequisites: Java, Spring Boot. Specific AI models or providers may require API keys or local installations (e.g., Ollama).
• Resources:
  • Spring AI Project
  • Documentation
  • Examples

Highlighted Details

• Comprehensive coverage of official resources, blogs, and learning materials including books and podcasts.
• Extensive list of code examples and demos, showcasing features like RAG, conversational memory, and OpenAI-compatible APIs.
• Includes community-driven samples and tools like the Spring AI Playground for easier experimentation.
• Features talks and videos from key Spring and AI conferences.

Maintenance & Community

• Maintained by the Spring team, with notable contributors including Mark Pollack and Christian Tzolov.
• Community engagement encouraged via contributions.

Licensing & Compatibility

• The list itself is under an unspecified license. Linked Spring AI projects typically follow Apache 2.0.
• Compatible with standard Java and Spring Boot applications.

Limitations & Caveats

Some linked workshop materials may be outdated. The repository is a curated list, not a single runnable project, so setup complexity varies by the specific example or tool being explored.