Movie data generator with rag project.

A complete RAG (Retrieval-Augmented Generation) application built with Spring Boot, PostgreSQL, and Ollama integration for document processing and AI-powered querying

🤖 Develop cutting-edge AI applications with the "yu-ai-agent" project, enhancing your skills and boosting your job prospects in the AI field.

Tools to make, find, or validate test data

A Java-based system for vector embeddings that layers consistent hashing and replication on top of Elasticsearch to enable scalable, fault-tolerant similarity search.

Use of Spring AI with ChatClient, Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) with Spring AI to build more accurate and contextually aware applications.

This is the source code of the Milvus vector database for Ballerina

Java Client for the Pinecone Vector Database

Mastering Spring AI

Exploring features of Spring AI for building intelligent apps

A 100D vector database using minibase storage engine

Microservicio experimental en Java/Spring Boot para aprender Spring AI, embeddings y recomendaciones financieras personalizadas mediante inteligencia artificial.

Comparison tool to view players from 2000 to 2024 and find similar players based on advanced statistical data.

Production-ready intelligent Q&A system with RAG pipeline, OpenAI GPT-4o-mini, vector embeddings, PostgreSQL pgvector, Spring Boot 3.3+, JWT auth, Redis caching, Docker, CI/CD, and comprehensive monitoring

This project implements the RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) algorithm using Spring AI.

Spring AI RAG vector store sentiment search on custom data loaded by tiko with a REST API.

🔎📚 This document processing system is designed to efficiently analyze user documents and provide accurate responses to user queries related to the content. Powered by advanced algorithms, it offers a seamless experience for users seeking insights or information within their documents.

A Document Question-Answering System built with Spring Boot, designed to allow users to upload documents (PDFs, text files, etc.) and ask natural language questions to extract precise answers. The system leverages Retrieval-Augmented Generation (RAG) techniques to combine information retrieval and language models for accurate context-based response

针对有旅行意愿，但是没有时间规划行程，或在旅途中想要获得帮助建议等问题，开发了一个基于Spring AI框架的智能旅游咨询平台。项目采用RAG检索增强生成技术构建专业知识库，通过Tool Calling、MCP协议集成多种外部工具服务，实现具备自主规划能力的AI Agent智能体，为用户提供旅行方面困惑的咨询服务。