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Spring AI Examples - Integrate AI into Spring Applications
This project provides a robust framework and examples for integrating AI technologies like Large Language Models (LLMs) and vector databases into Java applications using the Spring ecosystem.
Java
Added on 2025年6月29日636
Stars259
ForksJava
LanguageProject Introduction
Summary
This project, part of the Spring ecosystem, focuses on bringing Artificial Intelligence capabilities to Spring developers, offering comprehensive support for integrating various AI models and related technologies like vector databases within Spring applications.
Problem Solved
Integrating complex AI models and vector databases into enterprise Java applications is often challenging and requires significant boilerplate code. This project simplifies this process by providing idiomatic Spring abstractions and ready-to-use components.
Core Features
Unified API for AI Models
Seamlessly integrate leading AI models (OpenAI, Azure AI, etc.) into Spring applications with consistent APIs.
Vector Database Integration
Built-in support for vector databases (e.g., PGVector, Chroma) to enable RAG (Retrieval Augmented Generation) patterns.
AI Pipeline Construction
Provides components for creating AI pipelines, processing data, and generating content.
Tech Stack
Java
Spring Framework
Spring Boot
Spring AI
Various AI Provider APIs
Various Vector Database Clients
Use Cases
Spring AI enables a wide range of AI-powered use cases within Spring applications, from simple content generation to complex RAG-based systems.
Conversational AI & Chatbots
Details
Build chatbots or virtual assistants that can answer user queries by integrating with LLMs via the Spring AI ChatClient.
User Value
Enhance user engagement and provide automated support.
Content & Text Generation
Details
Generate creative text formats, translate languages, or write different kinds of creative content.
User Value
Automate content creation workflows and improve productivity.
Retrieval Augmented Generation (RAG)
Details
Implement RAG patterns where an LLM retrieves relevant information from your private data stored in a vector database before generating a response.
User Value
Provide accurate, context-aware responses based on proprietary data.
Text Analysis and Processing
Details
Analyze text to extract topics, sentiment, or named entities.
User Value
Gain insights from unstructured text data.
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