explaingit

spring-projects/spring-ai

8,697JavaAudience · developerComplexity · 3/5Setup · moderate

TLDR

Spring AI is a Java framework that connects Spring Boot applications to AI models from OpenAI, Anthropic, Google, and others through a single consistent API, supporting chat, image generation, vector search, and retrieval-augmented generation.

Mindmap

mindmap
  root((repo))
    What it does
      AI model integration
      Single consistent API
    AI Capabilities
      Text generation
      Image generation
      Audio transcription
      Speech synthesis
    Features
      Vector databases
      Function calling
      Conversation memory
      RAG support
    Providers
      OpenAI
      Anthropic
      Google
      Amazon
Click or tap to explore — scroll the page freely

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

Things people build with this

USE CASE 1

Add AI chat functionality to a Spring Boot application using any major AI provider without writing custom HTTP integration code.

USE CASE 2

Build a retrieval-augmented generation feature that searches your company documents and uses an AI model to answer questions about them.

USE CASE 3

Switch your application from one AI provider to another by changing a configuration value rather than rewriting application code.

USE CASE 4

Store and search document embeddings in a vector database using Spring AI's common interface across more than a dozen providers.

Tech stack

JavaSpring Boot

Getting it running

Difficulty · moderate Time to first run · 30min

Requires Java 17 or newer and a Spring Boot project, add via start.spring.io. Each AI provider requires its own API key.

In plain English

Spring AI is a Java framework for building applications that use AI models. It is part of the Spring ecosystem, which is a widely used set of tools for building Java applications, and it brings the same design patterns and coding conventions from that ecosystem to the world of AI integration. The main problem it solves is connecting your application's data and existing code to AI models. Rather than writing custom integration code for each AI provider, Spring AI gives you a single consistent API that works across providers like Anthropic, OpenAI, Google, Amazon, Microsoft, and others. If you switch providers, you change a configuration value rather than rewriting your application. Beyond basic chat, it supports several types of AI interactions: generating text, converting text to images, transcribing audio, and producing speech from text. It also supports vector databases, which are a type of storage system used to make AI-powered search and retrieval work efficiently. Spring AI connects to over a dozen vector database providers through one common interface. Other features include function calling (where the AI can trigger code in your application to get real-time information), conversation memory so the AI remembers earlier messages in a session, and retrieval-augmented generation (a technique where the AI answers questions by first searching your documents rather than relying only on what it was trained on). There are also utilities to help test whether AI responses are accurate. Spring Boot integration is included, meaning you can add Spring AI to a new project through the standard Spring project generator at start.spring.io. The project requires Java 17 or newer. Documentation and example projects are linked from the README.

Copy-paste prompts

Prompt 1
Using spring-projects/spring-ai, help me add a REST endpoint to my Spring Boot app that takes a user question and returns an answer from OpenAI.
Prompt 2
I want to build a RAG system with Spring AI that searches my company's PDF documents and uses Claude to answer questions about them. Walk me through the setup.
Prompt 3
Using Spring AI, show me how to configure my application to switch from OpenAI to Anthropic without changing any of my Java service code.
Prompt 4
Help me set up a vector database connection in Spring AI and write code to store document embeddings and search for the most relevant chunk given a query.
Open on GitHub → Explain another repo

← spring-projects on gitmyhub — every repo by this author, as a profile.

Verify against the repo before relying on details.