MCP-RAG
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Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP-RAGWhat is the hybrid search approach in this RAG system?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Modular RAG — 可插拔知识检索系统
一个模块化、可观测的 RAG 检索系统,支持 Hybrid Search + LLM Rerank + MCP 协议集成 + 全链路评估。
核心能力
模块 | 能力 | 说明 |
离线索引 | PDF/MD 摄入 → 分块 → LLM 精炼 → 混合索引 | 六阶段流水线,MarkItDown PDF 解析,自动去噪与元数据增强 |
在线检索 | Dense + Sparse 双路召回 → RRF 融合 → LLM 精排 | 集成问题路由、同义词扩展、低置信度二次检索 |
上下文拼装 | 检索后拼装 Chunk → LLM 生成带引用标注的答案 | Small-to-Big 上下文扩展,逐句引用核查 |
评估体系 | Ragas + CustomEvaluator(Hit Rate / MRR) | Golden Test Set 回归测试,量化对比策略效果 |
MCP 协议 | 标准 MCP Server,暴露 3 个 Tool | Claude Desktop、Cursor 等 AI 客户端即插即用 |
可观测性 | 全链路 TraceContext 10 阶段追踪 | Streamlit Dashboard 六页面可视化管理 |
Related MCP server: ragtag-mcp
架构
用户 Query
│
▼
QueryProcessor ─── 分词 / Filter 解析 / 同义词扩展 / 问题路由
│
╱ ╲
Dense 检索 Sparse 检索
DashScope 1024d jieba + BM25
ChromaDB HNSW JSON 倒排索引
╲ ╱
RRF(k=60) 融合
│
LLM Rerank 精排
│
AnswerGenerator ─── 上下文拼装 → LLM 生成答案
│
CitationVerifier ─── 逐句核查引用依据评估指标(基于 9 题 Golden Test Set 实测)
指标 | 纯 Dense | Hybrid | Hybrid+Rerank |
Context Precision | 0.13 | 0.62 | 0.81 |
Faithfulness | 0.55 | 0.84 | 0.998 |
Recall@5 | 7.2% | 21.1% | — |
Top-1 命中率 | 0% | 56% | — |
快速开始
pip install -e .
# 摄入文档
python -c "
from src.ingestion.pipeline import IngestionPipeline
from src.core.settings import load_settings
p = IngestionPipeline(load_settings())
p.run(file_path='data/documents/your-file.md')
"
# 查询
python scripts/query.py --query "你的问题" --collection default
# 评估
python scripts/run_evaluation.py --test-set tests/fixtures/semiconductor_golden.json
# Dashboard
streamlit run src/observability/dashboard/app.py配置
config/settings.yaml:
llm: {provider: "deepseek", model: "deepseek-v4-pro"}
embedding: {provider: "dashscope", model: "text-embedding-v3"} # 阿里云,OpenAI 兼容
rerank: {enabled: true, provider: "llm"}
retrieval: {dense_top_k: 20, sparse_top_k: 20, fusion_top_k: 10, rrf_k: 60}环境变量(.env):
DEEPSEEK_API_KEY=sk-xxx
EMBEDDING_API_KEY=sk-xxx技术栈
Python · ChromaDB · BM25 · RAGAS · RRF · LLM Rerank · MCP · DashScope · DeepSeek · jieba · Streamlit · MarkItDown · langchain-text-splitters · TDD · Factory Pattern
目录结构
src/
├── core/query_engine/ # Hybrid Search, Reranker, Query Processor, Query Cache
├── core/response/ # Answer Generator, Citation Verifier
├── core/trace/ # TraceContext, TraceCollector
├── ingestion/ # Pipeline, Chunk Refiner, Metadata Enricher
├── libs/ # LLM, Embedding, VectorStore, Reranker, Splitter, Evaluator
├── mcp_server/ # MCP Protocol Server + 3 Tools
└── observability/ # Dashboard, EvaluationLicense
MIT
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