pgvector

LangChain4j is an idiomatic, open-source Java library for building LLM-powered applications on the JVM. It offers a unified API over popular LLM providers and vector stores, and makes implementing tool calling (including MCP support), agents and RAG easy. It integrates seamlessly with enterprise Java frameworks like Quarkus and Spring Boot.

基于 Spring Boot 4.1、Java 25、Spring AI 2.0、React、PostgreSQL/pgvector、Redis 和 RustFS 构建的开源 AI 面试平台，支持简历智能分析、模拟面试、语音面试和知识库 RAG。

🔥🔥🔥 Turn AI-written code into real apps. Nubase is an open-source, AI-native backend platform for AI Coding, agentic applications, and modern product teams: Memory, Database, Storage, and Auth in one self-hostable service.

AI equity research agent with resilient workflows, evidence-grounded RAG, versioned reports, and automated quality evaluation.

基于Spring Boot+Vue+ElementUI的人力资源管理系统

Production-oriented Spring AI platform prototype for RAG, tool calling, async ingestion, JWT/RBAC, and observability.

基于 Spring AI 2.0 的生产级 ERP 智能助手｜Production-ready ERP AI assistant with RAG, Tool Calling, chat memory, dynamic SQL tools, multi-tenancy and ECharts.

A self-hosted RAG agent platform for knowledge management, LLM orchestration, and MCP tool integration. 面向知识管理、LLM 编排与 MCP 工具集成的自托管 RAG 智能体平台。

Samples showing architectural patterns for Modular RAG using Spring AI and Ollama.

This project contains source code to demonstrate the usage of Retrival Augmented Generation (RAG) feature using Spring AI using PG Vector as the Database and Ollama as the Local LLM

The database agent for Postgres and MySQL - Point it at PostgreSQL or MySQL and ask questions in plain English — schema exploration, query generation, slow query analysis, index recommendations, generated dashboards. Self-hosted and air-gapped.

This demo application showcases the implementation of Spring Boot microservices, highlighting their powerful features and capabilities, with a particular emphasis on integrating OpenAI for advanced AI functionalities.

A project to show howto use SpringAI with Ollama to chat with the documents in a library. Documents are stored in a normal/vector database. The AI is used to create embeddings from documents that are stored in the vector database. The vector database is used to query for the nearest document. That document is used by the AI to generate the answer.

a rag implementation example for document-based QA, using spring-ai, ollama, and postgres-pgvector vetor db.

Reference Java & Spring implementation of the Open Ingestion Standard (OIS). It provides a secure, decoupled, and vendor-neutral enterprise data integration platform leveraging modern Java 25 features, Spring Boot, Spring AI and vector search infrastructure to orchestrate data flows

Spring Boot + Spring AI 实践项目：聊天、RAG、工具调用、MCP、记忆、SSE 与 Vue 前端联调。

AI implementation using langchain4j and springAI frameworks with Java

Curso System Design Interview Notebook LM - From Zero to Production

PeopleMesh is the AI-powered matching layer for modern organizations. It helps people discover the right colleagues, internal opportunities, communities, and projects through semantic search that understands context, not just keywords.

基于 Spring Boot 3.5 + Java 21 + Spring AI + Vue 3，实现 AI 情感咨询、深度思考智能体、RAG 知识库检索、多工具调用等核心功能。支持恋爱报告生成、地图服务集成、PDF文档处理等实用场景。架构清晰、文档完善，非常适合作为 AI 应用学习和简历项目，学习门槛低