MCP Knowledge Service

Reusable MCP-based knowledge retrieval service for domain-specific document ingestion and cited hybrid retrieval.

可复用的 MCP 知识检索服务，通过 collection 隔离不同业务知识库，提供文档导入、混合检索和来源引用。

Overview / 项目概述

MCP Knowledge Service is a domain-neutral retrieval server exposed through the Model Context Protocol. It ingests documents into named collections, builds Dense Retrieval and BM25 indexes, fuses results with RRF, and returns cited search results to MCP clients.

该服务不绑定具体业务。examples/salon/ 只是一个可运行的示例，用于展示业务项目如何通过 collection 接入自己的知识库。

Related MCP server: PDFDashboardWithMCP

Core Capabilities / 核心能力

Architecture / 系统架构

• Ingestion: converts PDF / Markdown into vector and BM25 retrieval data under a selected collection.

• MCP Serving: an MCP Client calls tools exposed by the MCP Server through stdio.

• Query: query_knowledge_hub uses Dense Retrieval, BM25, and RRF to return Retrieved Answer with Citations.

中文说明：导入阶段把 PDF / Markdown 转换为 collection 下的向量和 BM25 检索数据；在线阶段由 MCP Client 通过 stdio 调用 MCP Server 暴露的 tools；query_knowledge_hub 使用 Dense Retrieval、BM25 和 RRF 返回 citations。

Collection Isolation / Collection 隔离

Collection names isolate domain-specific knowledge retrieval. Examples:

• knowledge_hub

• salon_knowledge

• email_knowledge

• internal_policy_knowledge

Collection names must be passed through CLI arguments, MCP tool arguments, environment variables, or config. Service code should not hardcode a business-specific collection.

Quick Start / 快速启动

python3.11 -m venv .venv
source .venv/bin/activate
pip install -e '.[dev]'
cp .env.example .env
cp config/settings.example.yaml config/settings.yaml

Fill local provider values in .env or shell environment before real embedding or provider-backed ingestion. Do not commit .env or config/settings.yaml.

真实执行 embedding 或 Provider 驱动的 ingestion 前，在本地 .env 或 shell 环境中填入私有 Key；不要提交这些配置。

MCP server entry point:

python -m src.mcp_server.server

python -m src.mcp_server.server starts an stdio JSON-RPC server, not an interactive CLI.

For normal application use, let the MCP client launch it automatically.

For standalone verification, start it through an MCP client or verification script that sends initialize, tools/list, and tool calls.

The server uses stdio transport. Stdout is reserved for MCP protocol messages; logs are written to stderr.

python -m src.mcp_server.server 启动的是 stdio JSON-RPC Server，不是可直接交互查询的 CLI。

正常业务运行时，应由 MCP Client 自动拉起该进程。

单独验证时，应通过 MCP Client 或验证脚本发送 initialize、tools/list 和 tool call，而不是只在终端直接运行该命令。

Ingestion / 知识导入

Ingest documents into a selected collection:

python scripts/ingest.py \
  --path <SOURCE_PATH> \
  --collection knowledge_hub \
  --force

Dry run:

python scripts/ingest.py \
  --path <SOURCE_PATH> \
  --collection knowledge_hub \
  --dry-run

Real ingestion requires provider configuration for embeddings and writable runtime storage. Do not commit runtime ChromaDB, BM25, SQLite, logs, traces, or local reports.

MCP Tools / MCP 工具

Expected tools from tools/list:

• query_knowledge_hub

• list_collections

• get_document_summary

MCP tool input for query_knowledge_hub:

{
  "query": "What is this collection about?",
  "top_k": 4,
  "collection": "knowledge_hub"
}

CLI query:

python scripts/query.py \
  --query "What is this collection about?" \
  --collection knowledge_hub \
  --top-k 4

Verified Public Scope / 已验证公开范围

• MCP initialize

• tools/list

• query_knowledge_hub

• citations

• salon example ingestion: 7 PDFs, 24 chunks, 24 vectors, 24 BM25 documents

• smoke_test_collection proves collection is not hardcoded

• runtime data is intentionally excluded from Git

The salon example verification is an integration check, not a general retrieval-quality benchmark.

Example Integration / 示例集成

The examples/salon/ directory contains a complete example used by AI Hair Salon Agent:

• 7 source Markdown files

• generated PDFs

• ingestion instructions

• example collection: salon_knowledge

It is an integration example, not the default domain of this repository.

Related Repository / 关联项目

AI Hair Salon Agent: https://github.com/hyh0620/ai-hair-salon-agent

AI Hair Salon Agent is a reference consumer that calls this service through an MCP client with collection=salon_knowledge.

AI Hair Salon Agent 是该服务的参考消费者，通过 MCP Client 调用 salon_knowledge collection。

Verification / 验证方式

Static dependency check:

.venv/bin/python -m pip check

Unit and integration tests:

pytest

Real MCP verification:

initialize -> tools/list -> query_knowledge_hub

Real ingestion verification:

python scripts/ingest.py \
  --path <SOURCE_PATH> \
  --collection <COLLECTION_NAME> \
  --force

Some real ingestion and provider tests require local provider configuration, API credentials, and runtime storage. Keep those local files out of Git.

Known Limits / 当前限制

Documentation / 文档

• Architecture / 系统架构

• Ingestion / 知识导入

• Integration / 接入指南

• Evaluation / 检索评估

Skills / 项目工作流

Operational skills are under .github/skills/:

• setup-environment

• ingest-knowledge

• verify-mcp-server

• evaluate-retrieval

• package-clean

They describe repeatable operational workflows and do not contain credentials.

Security / 安全说明

Do not commit .env, API keys, config/settings.yaml, local runtime databases, ChromaDB files, BM25 indexes, logs, trace files, or raw local evaluation dumps.