Spring AI

Spring AI is an application framework for AI engineering. Its goal is to apply to the AI domain Spring ecosystem design principles such as portability and modular design and promote using POJOs as the building blocks of an application to the AI domain.

At its core, Spring AI addresses the fundamental challenge of AI integration: Connecting your enterprise Data and APIs with the AI Models.

Features

Spring AI provides the following features:

• Support for all major AI Model providers such as Anthropic, OpenAI, Microsoft, Amazon, Google, and Ollama. Supported model types include:
  • Chat Completion
  • Embedding
  • Text to Image
  • Audio Transcription
  • Text to Speech
  • Moderation
• Portable API support across AI providers for both synchronous and streaming API options are supported. Access to model-specific features is also available.
• Structured Outputs - Mapping of AI Model output to POJOs.
• Support for all major Vector Database providers such as Apache Cassandra, Azure Vector Search, Chroma, Milvus, MongoDB Atlas, Neo4j, Oracle, PostgreSQL/PGVector, PineCone, Qdrant, Redis, and Weaviate.
• Portable API across Vector Store providers, including a novel SQL-like metadata filter API.
• Tools/Function Calling - permits the model to request the execution of client-side tools and functions, thereby accessing necessary real-time information as required.
• Observability - Provides insights into AI-related operations.
• Document injection ETL framework for Data Engineering.
• AI Model Evaluation - Utilities to help evaluate generated content and protect against hallucinated response.
• ChatClient API - Fluent API for communicating with AI Chat Models, idiomatically similar to the WebClient and RestClient APIs.
• Advisors API - Encapsulates recurring Generative AI patterns, transforms data sent to and from Language Models (LLMs), and provides portability across various models and use cases.
• Support for Chat Conversation Memory and Retrieval Augmented Generation (RAG).
• Spring Boot Auto Configuration and Starters for all AI Models and Vector Stores - use the start.spring.io to select the Model or Vector-store of choice.

This feature set lets you implement common use cases such as "Q&A over your documentation" or "Chat with your documentation."

Documentation

Extensive reference documentation, sample applications, and workshop/course material.

Getting Started

You can get started in a few simple steps:

1. Create a Spring Boot Web application with a Spring AI OpenAI boot starter dependency. This Spring Initializr link can help you bootstrap the application. (With start.spring.io you can select any AI Models or Vector Stores that you want to use in your new applications).

2. Add your OpenAI key to the application.properties:

   spring.ai.openai.api-key=<YOUR OPENAI KEY>
   

3. Add the following snippet to your SpringAiDemoApplication class:

   @Bean
   public CommandLineRunner runner(ChatClient.Builder builder) {
       return args -> {
           ChatClient chatClient = builder.build();
           String response = chatClient.prompt("Tell me a joke").call().content();							
           System.out.println(response);
       };
   }
   

4. Run the application:

   ./mvnw spring-boot:run
   

Want to get started in another way? View the Getting Started section in the reference documentation.