This repository demonstrates how to build real-time AI applications with Spring AI Streaming Responses. The project showcases how to stream data from a Large Language Model (LLM) directly to the client, creating a fluid and interactive user experience similar to modern chatbots.
📖 Dive Deeper: For a complete walkthrough, detailed explanations of streaming, Server-Sent Events (SSE), and step-by-step instructions for building this example application, read our comprehensive blog post.
👉 Spring AI Streaming Response: Build Real-Time AI Apps
🎥 Visual Learning: Prefer video tutorials? Watch our step-by-step implementation guide on YouTube.
👉 Spring AI Streaming Response Tutorial | Build Real-Time AI Apps
Make sure to provide these Java environment variables when running the application:
GEMINI_API_KEY: Your Google Gemini API key.
This project implements a Real-Time AI Recipe Planner as a practical example of Spring AI's streaming capabilities. It showcases how to:
- Set up a Spring Boot application using Spring WebFlux and Spring AI.
- Use the Chat Client to receive continuous data chunks from an AI model.
- Build two distinct streaming endpoints: one for simple text content and another for rich responses including developer metadata.
The application allows a user to request a recipe for a specific dish, and the AI generates the ingredients and instructions live on screen, piece by piece, instead of making the user wait for the entire response to be completed.