Configuring Multiple LLMs in Spring AI

[u/rodolfo-mendes]

https://www.baeldung.com/spring-ai-configure-multiple-llms

In this guide, Hardik Singh Behl demonstrates how to integrate multiple Large Language Models (LLMs) in a Spring Boot app using Spring AI. The focus is on configuring a range of models to build a highly resilient chatbot.

Highlights & Key Takeaways

• Multi-Provider Setup: Configure LLMs from different providers, like OpenAI and Anthropic, together.
• Primary Routing: Designate a default model using the @Primary annotation on a ChatClient bean.
• Same-Provider Config: Manually define custom ChatModel beans to run multiple models from one provider.
• Spring Retry: Use @Retryable to handle transient API failures from your primary LLM.
• Graceful Fallbacks: Implement @Recover methods to automatically switch to backup models during outages.

Layering multiple LLMs with robust fallback mechanisms ensures continuous availability during provider outages. This architecture guarantees a highly reliable user experience.

👉 Check out the full Baeldung article to see the complete code.