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YiYunn-lucky

Modular RAG MCP Server

by YiYunn-lucky
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Python

Modular RAG MCP Server

一个可插拔、可观测的模块化 RAG（检索增强生成）服务框架，通过 MCP（Model Context Protocol）协议对外暴露工具接口，支持 Copilot / Claude 等 AI 助手直接调用。

核心能力一览

模块

能力

说明

Ingestion Pipeline

PDF → Markdown → Chunk → Transform → Embedding → Upsert

全链路数据摄取，支持多模态图片描述（Image Captioning）

Hybrid Search

Dense (向量) + Sparse (BM25) + RRF Fusion + Rerank

粗排召回 + 精排重排的两段式检索架构

MCP Server

标准 MCP 协议暴露 Tools

query_knowledge_hub、list_collections、get_document_summary

Dashboard

Streamlit 六页面管理平台

系统总览 / 数据浏览 / Ingestion 管理 / 摄取追踪 / 查询追踪 / 评估面板

Evaluation

Ragas + Custom 评估体系

支持 golden test set 回归测试，拒绝"凭感觉"调优

Observability

全链路白盒化追踪

Ingestion 与 Query 两条链路的每一个中间状态透明可见

技术亮点

🔌 全链路可插拔架构：LLM / Embedding / Reranker / Splitter / VectorStore / Evaluator 每一个核心环节均定义了抽象接口，支持"乐高积木式"替换，通过配置文件一键切换后端，零代码修改。

🔍 混合检索 + 重排：BM25 稀疏检索解决专有名词精确匹配 + Dense Embedding 解决同义词语义匹配，RRF 融合后可选 Cross-Encoder / LLM Rerank 精排，平衡查全率与查准率。

🖼️ 多模态图像处理：采用 Image-to-Text 策略，利用 Vision LLM 自动生成图片描述并缝合进 Chunk，复用纯文本 RAG 链路即可实现"搜文字出图"。

📡 MCP 生态集成：遵循 Model Context Protocol 标准，可直接对接 GitHub Copilot、Claude Desktop 等 MCP Client，零前端开发，一次开发处处可用。

📊 可视化管理 + 自动化评估：Streamlit Dashboard 提供完整的数据管理与链路追踪能力，集成 Ragas 等评估框架，建立基于数据的迭代反馈回路。

🧪 三层测试体系：Unit / Integration / E2E 分层测试，覆盖独立模块逻辑、模块间交互、完整链路（MCP Client / Dashboard）。

📖 详细架构设计、模块说明和任务排期请参阅 DEV_SPEC.md

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