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Incubating Status Recently Approved

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Overview

Spring AI Playground is a self-hosted web UI that simplifies AI experimentation and testing. It provides Java developers with an intuitive interface for working with large language models (LLMs), vector databases, prompt engineering, and Model Context Protocol (MCP) integrations. Built on Spring AI, it supports leading model providers and includes comprehensive tools for testing retrieval-augmented generation (RAG) workflows and MCP integrations. The goal is to make AI more accessible to developers, helping them quickly prototype Spring AI-based applications with enhanced contextual awareness and external tool capabilities.

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Key Features

Chat Playground

Unified chat interface powered by Spring AI’s ChatClient with dynamic RAG and MCP integration

VectorDB Playground

Full RAG flows: upload documents, chunk, embed, search, and inspect retrieval with score details

MCP Playground

Register, test, and invoke Model Context Protocol tools interactively

PWA Support

Progressive Web App for seamless desktop and mobile experience

Multi-Provider

Support for Ollama, OpenAI, and OpenAI-compatible servers

Vaadin UI

Modern, responsive UI built with Vaadin Flow

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Playgrounds

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Chat Playground

A unified chat interface powered by Spring AI’s ChatClient that can dynamically use configurations from VectorDB and MCP playgrounds, enabling conversations enriched with retrieval and external tools. Chat Playground with MCP

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VectorDB Playground

Supports full RAG flows:
  • Upload and manage documents
  • Chunk and embed content
  • Search vector databases
  • Inspect retrieval results with score details
Implemented using Spring AI’s RetrievalAugmentationAdvisor. VectorDB Playground

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MCP Playground

Integrates Spring AI’s Model Context Protocol, allowing developers to:
  • Register external tools
  • Test tool invocations interactively
  • Monitor tool execution
MCP Playground

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Supported Technologies

  • Ollama (with tool-enabled models)
  • OpenAI (GPT-3.5, GPT-4, etc.)
  • OpenAI-compatible servers (LM Studio, etc.)
  • PostgreSQL with pgvector
  • ChromaDB
  • Milvus
  • Qdrant
  • Redis
  • Other Spring AI supported vector stores
  • STDIO transport
  • SSE (Server-Sent Events) transport
  • Custom tool registration
  • Interactive tool testing

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Quick Start

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Prerequisites

  • Java 21 or later
  • Ollama running on your machine
  • Docker (optional, but recommended)
# Clone the repository
git clone https://github.com/JM-Lab/spring-ai-playground.git
cd spring-ai-playground

# Build Docker image
./mvnw spring-boot:build-image -Pproduction -DskipTests=true \
  -Dspring-boot.build-image.imageName=jmlab/spring-ai-playground:latest

# Run container
docker run -d -p 8282:8282 --name spring-ai-playground \
  -e SPRING_AI_OLLAMA_BASE_URL=http://host.docker.internal:11434 \
  -v spring-ai-playground:/home \
  --restart unless-stopped \
  jmlab/spring-ai-playground:latest

# Access the application
open http://localhost:8282

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Running Locally

./mvnw clean install
./mvnw spring-boot:run

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Development Team

  • Jemin Huh - Project maintainer

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Upcoming Features

1

Spring AI Agent

Enhanced agent abstractions and patterns
2

Observability

Comprehensive monitoring and tracing
3

Authentication

User authentication and authorization
4

Multimodal Support

Image and audio processing capabilities

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Resources

GitHub Repository

View source code and contribute

Project Proposal

Original community proposal

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License

This project is licensed under the Apache License 2.0.
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