This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.

Production-grade Node.js RAG system with hybrid retrieval, pluggable adapters, and OpenTelemetry tracing

An advanced, fault-tolerant Retrieval-Augmented Generation (RAG) agent designed for mission-critical document auditing. This engine bypasses the limitations of standard semantic search by implementing a high-performance Hybrid Retrieval pipeline, Reciprocal Rank Fusion (RRF), and AI Cross-Encoder re-ranking.

25 源并行搜索，RRF 融合 + LLM 评分 + 交叉验证。知乎/雪球/V2EX/Twitter/YouTube/B站/GitHub/HN — 一次搜索，结构化简报。零付费 API，零配置。

Production-style RAG pipeline with multi-strategy retrieval (sparse, dense, hybrid RRF), LangGraph orchestration, and full OpenTelemetry/Phoenix tracing over a 4M-review Amazon Electronics dataset (Cell Phone and Accessories)

Production-grade RAG pipeline. Multi-format ingestion + 3 swappable chunking strategies + dual (dense + BM25) indexing + RRF fusion + cross-encoder rerank + grounded generation with bracketed citations + LLM-as-judge citation verification + composite-confidence I-don't-know gate.

Build a hybrid system that combines dense and sparse vectors with Reciprocal Rank Fusion, demonstrating how to get the best of both semantic understanding and keyword precision.

RAG-based AI legal assistant using hybrid retrieval and local LLM inference.