Artificial Intelligence is no longer a niche capability—it’s rapidly becoming a foundational element across enterprise applications. Whether you're building smarter chatbots, generating insights from unstructured content, or integrating Large Language Models (LLMs) into your workflows, developers increasingly need streamlined ways to plug AI into real-world systems.
That’s where Spring AI steps in.
In this blog post, I’ll introduce Spring AI, a new project from the Spring team that brings first-class support for integrating generative AI and foundation models into Spring-based applications. It’s an exciting addition to the Spring ecosystem that aims to make AI integration as natural as working with data sources or messaging.
What is Spring AI?
Spring AI is an open-source project that provides a unified and consistent programming model to work with modern AI capabilities like:
- Large Language Models (LLMs) such as OpenAI, Azure OpenAI, Hugging Face, and Ollama
- Embedding Models for semantic search
- Vector Stores (like Redis, Milvus, Qdrant, Pinecone, and PostgreSQL with pgvector)
- Prompt Templates, RAG (Retrieval-Augmented Generation) workflows, and tool execution
The project is deeply inspired by Spring Data and Spring Cloud, and brings that same level of abstraction and consistency to AI workflows.
Key Features of Spring AI
1. Unified LLM API
Spring AI provides a consistent interface across multiple LLM providers like:
- OpenAI
- Azure OpenAI
- Hugging Face
- Ollama
This allows you to write code once and switch providers with minimal changes.
var response = chatClient.call(new Prompt("Tell me a joke about Spring Boot"));
System.out.println(response.getResult());
2. Prompt Engineering Made Easy
PromptTemplate template = new PromptTemplate("Translate this text to French: {text}");
template.add("text", "Hello, world!");
3. Support for RAG (Retrieval-Augmented Generation)
Integrate AI responses with external knowledge sources using vector search. Spring AI supports various vector stores and offers abstractions for embedding, storing, and retrieving content semantically.
Embedding embedding = embeddingClient.embed("Spring is great for microservices!");
vectorStore.add(new EmbeddingDocument("id-1", embedding, metadata));
4. Integration with Spring Boot
Spring AI is a first-class citizen in the Spring ecosystem. It works seamlessly with Spring Boot and supports features like:
- Declarative configuration using
application.yml
- Integration with Actuator and Observability
- Use of
@Bean
,@Configuration
, and dependency injection
5. Tool Execution and Function Calling
Spring AI supports tool calling and function execution—critical for agent-based applications.
A Simple Use Case
Let’s say you’re building a customer support chatbot. With Spring AI, you can:
- Use OpenAI to handle natural language queries.
- Store support articles in a vector database.
- Implement RAG to enhance responses using your private knowledge base.
- Define functions (e.g., "create support ticket") that the model can call programmatically.
The entire pipeline is manageable using familiar Spring idioms.
Why Use Spring AI?
If you’re already using Spring Boot in your backend stack, Spring AI provides:
- Consistency: Familiar APIs and configuration patterns.
- Portability: Swap providers or vector stores with minimal refactoring.
- Flexibility: Fine-grained control over prompts, embeddings, and function calls.
- Productivity: Rapid prototyping and integration without boilerplate.
Getting Started
To get started:
- Add Spring AI to your Maven or Gradle project:
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>
- Configure your provider:
spring:
ai:
openai:
api-key: ${OPENAI_API_KEY}
- Inject and use the
ChatClient
,EmbeddingClient
, or other components in your service.
Official guide: https://docs.spring.io/spring-ai/reference
The Future of Enterprise AI with Spring
Spring AI represents a big leap in making AI accessible for mainstream enterprise developers. Instead of reinventing the wheel, teams can build intelligent systems using familiar patterns and strong ecosystem support.
Whether you’re building smart assistants, enhancing search, or enabling decision support, Spring AI offers a solid foundation.
I’ll be diving deeper into use cases and tutorials in future posts—stay tuned!