Spring AI is a robust extension of the Spring Framework. It’s designed to empower Java developers to create AI-capable applications without the need for extensive reskilling. By leveraging the familiarity and strengths of the Spring Framework, Spring AI democratizes access to sophisticated AI features, making it easier for developers to build intelligent apps.

@Service
public class SpringAI {

private final ChatClient chatClient;

public SpringAI(ChatClient.Builder builder) {
this.chatClient = builder.build();
}

public String tellMeAJoke()
return chatClient.prompt().user("Tell me a joke").call().content());
}

}

Tool calling allows you to register your own functions to connect the LLMs to the APIs of external systems. These systems can provide LLMs with real-time data and perform data processing actions on their behalf. Spring AI greatly simplifies code you need to write to support function invocation. It handles the function invocation conversation for you. You can provide your function as a @Bean and then provide the bean name of the function in your prompt options to activate that function. Additionally, you can define and reference multiple functions in a single prompt.

Standardized protocol that enables AI models and Agents to interact with external tools and resources in a structured way. It supports multiple transport mechanisms to provide flexibility across different environments.

At its core, Spring AI addresses the fundamental challenge of AI integration - Connecting your enterprise Data and APIs with the AI Models. A technique termed Retrieval Augmented Generation (RAG) has emerged to address the challenge of incorporating relevant data into prompts for accurate AI model responses.Spring AI greatly simplifies code you need to write to support RAG pipelines.

Generative AI brings with it it's own set of challenges. Spring AI supports the following patterns to address these challenges.