Spring AI: Multi-Provider AI Chat
Overview
I recently published a small hands-on project to explore how to build AI capabilities in Java using Spring Boot and Spring AI.
Project links:
Project Description
Explore different options using spring ai chatclient: GitHUB
Implementing Tools calling concept with spring ai: GitHUB
I’ve kept the repository README detailed so anyone can run and test the APIs quickly.
If you’re learning Spring AI and want a compact reference project, feel free to explore, fork, or share feedback.
Why Spring AI?
Most AI examples today are Python-first, which makes sense from a research perspective. But in many enterprise environments, core systems are still Java-based.
Spring AI brings AI integration into the familiar Spring ecosystem. That means:
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Dependency injection and clean configuration
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Provider abstraction (switch models without rewriting business logic)
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Consistent patterns alongside existing REST APIs
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Easier integration into existing enterprise applications
For teams already building with Spring Boot, this makes AI features feel like an extension of the platform rather than a separate experimental stack.
What the Project Covers
This project focuses on a practical backend setup with:
- Chat APIs
- Conversation APIs
- Streaming responses (SSE)
- Prompt-based analysis endpoints (code + ticket analysis)
- Multi-provider AI support using header/config-based selection
Supported Models
- OpenAI
- Gemini
- Ollama (locally setup)
- Groq
- Cohere
- Mistral
What I Wanted to Explore
With this project, I focused on:
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Clean abstraction of multiple AI providers
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Model selection without changing business logic
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Designing APIs that can support real workflows
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Handling streaming responses properly in a Spring Boot setup
References:
Credits
Prashant Hariharan is a Senior Java Consultant currently exploring AI system Integration using Spring AI and enterprise backend architectures.
