OpenAI API Integration in Spring Boot with Spring AI
Fast recall
Quick revision
Core answer
spring-ai-openai-spring-boot-starter dependency, set spring.ai.openai.api-key in application.properties, and inject ChatClient or OpenAiChatModel. Call chatClient.prompt().user("your prompt").call().content() to get a response. Spring AI handles HTTP, retries, and model configuration through auto-configuration.Whether the candidate knows Spring AI's auto-configuration approach, how API keys flow through properties, and the ChatClient fluent API
Manually wiring HTTP clients when Spring AI's auto-configuration handles it; not using the ChatClient builder pattern and going straight to the lower-level model
Mention model-level options like temperature and max tokens via ChatOptions, streaming responses via stream().content(), and how to swap providers by changing the starter dependency
Interview-ready guide
Interview answer
Add the spring-ai-openai-spring-boot-starter dependency, set spring.ai.openai.api-key in application.properties, and inject ChatClient or OpenAiChatModel. Call chatClient.prompt().user("your prompt").call().content() to get a response. Spring AI handles HTTP, retries, and model configuration through auto-configuration.
OpenAI API Integration in Spring Boot with Spring AI
Direct Answer
Add the spring-ai-openai-spring-boot-starter dependency, set spring.ai.openai.api-key in application.properties, and inject ChatClient or OpenAiChatModel. Call chatClient.prompt().user("your prompt").call().content() to get a response. Spring AI handles HTTP, retries, and model configuration through auto-configuration.
Deep Dive & Architecture
All three are supported by Spring AI starters: spring-ai-openai, spring-ai-anthropic, spring-ai-vertex-ai-gemini.
The ChatClient interface is identical across providers — swap the starter and properties to switch.
Key differences: OpenAI uses OpenAiChatModel, Anthropic uses AnthropicChatModel, Google uses VertexAiGeminiChatModel. Auth differs — OpenAI and Anthropic use API keys, Gemini on Vertex AI uses service account credentials. Token context windows and pricing vary significantly.
Manually wiring HTTP clients when Spring AI's auto-configuration handles it; not using the ChatClient builder pattern and going straight to the lower-level model
Common Mistakes & Pitfalls
Manually wiring HTTP clients when Spring AI's auto-configuration handles it; not using the ChatClient builder pattern and going straight to the lower-level model
Check your understanding
Answer these without looking back at the article.
- How do you handle LLM API rate limits and implement retry logic in a Spring Boot service?
- What is function calling in LLM APIs and how do you implement it with Spring AI?
- Compare OpenAI, Anthropic Claude, and Google Gemini APIs from a Java backend integration perspective
Looking for a structured roadmap?
Every module on this page is also part of the full Java backend interview track.