nageoffer/ragent

企业级 Agentic RAG 智能体 - 全链路覆盖文档解析、多路检索、意图识别、问题重写、会话记忆、MCP 工具调用与深度思考。面向真实业务场景，从 0 到 1 完整工程实现。

What it solves

Standard RAG (Retrieval-Augmented Generation) tutorials often only cover simple API calls, failing to address real-world production challenges like complex document parsing, retrieval accuracy, model instability, and high concurrency. Ragent provides a comprehensive engineering solution for these issues, specifically designed for Java developers transitioning into AI engineering.

How it works

The platform uses a modular architecture to manage the entire AI application lifecycle:

  • Hybrid Retrieval: Combines vector, keyword, knowledge graph, and web search results using RRF fusion and reranking.
  • Query Understanding: Employs query rewriting, splitting, tree-based intent recognition, and multi-knowledge-base routing.
  • Model Management: Features model tiering, first-token detection, and circuit breakers for failover and degradation.
  • Agentic Capabilities: Integrates MCP (Model Context Protocol) for tool discovery and execution.
  • Data Pipeline: An orchestratable ingestion pipeline for processing various document formats into searchable chunks.
  • Reliability Layers: Implements Redis-based fair queuing, distributed concurrency control, and full-link tracing.

Who it’s for

  • Java Backend Developers looking to transition into AI engineering roles by learning production-level RAG and Agent patterns.
  • Students/Job Seekers needing a sophisticated, real-world AI project to differentiate their resumes from standard CRUD applications.
  • Enterprise Developers seeking a reference implementation for robust, scalable AI application architectures.

Highlights

  • Full-stack implementation using Spring Boot 3 and React 18
  • Advanced hybrid retrieval (Vector, Elasticsearch, Knowledge Graph, Web Search)
  • Production-ready features including circuit breakers, distributed rate limiting, and idempotency
  • Comprehensive management console for knowledge bases, traces, and model configurations
  • Support for MCP (Model Context Protocol) for external tool integration