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SOLVRO MCP - Knowledge Graph RAG System

Name: SOLVRO MCP - Knowledge Graph RAG System
Author: Solvro

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by Solvro

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Python

Hybrid

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  Frontend   │────▶│  ToPWR API  │────▶│  MCP Server │────▶│    Neo4j    │
│    :80      │     │    :8000    │     │    :8005    │     │    :7687    │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
React + Nginx         FastAPI              FastMCP          Knowledge Graph

PWrChat UI - React 聊天机器人（会话侧边栏、深色/浅色模式切换、持久化主题）
智能查询路由 - Guardrails 系统确定查询相关性
自然语言转 Cypher - 将问题转换为图查询
知识图谱 RAG - 基于 Neo4j 的检索增强生成
MCP 协议 - 标准模型上下文协议接口
可观测性 - 可选的 Langfuse 追踪集成
Docker 就绪 - 一键部署

快速开始

# Setup
just setup
cp .env.example .env  # Edit with your API keys

# Run with Docker
just up      # Start Neo4j + MCP Server + API
just logs    # View logs
just down    # Stop services

Related MCP server: Unreal Engine Knowledge Graph MCP Server

架构

系统概览

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  Frontend   │────▶│  ToPWR API  │────▶│  MCP Server │────▶│    Neo4j    │
│    :80      │     │    :8000    │     │    :8005    │     │    :7687    │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
React + Nginx         FastAPI              FastMCP          Knowledge Graph

服务	端口	描述
`frontend`	80	PWrChat — 由 Nginx 提供的 React 聊天机器人 UI
`topwr-api`	8000	ToPWR 应用的 FastAPI 后端
`mcp-server`	8005	带有 RAG 流水线的 MCP 服务器
`neo4j`	7474/7687	知识图谱数据库

RAG 流水线

系统核心是一个基于 LangGraph 的 RAG 流水线，用于智能处理用户查询：

流水线流程：

Guardrails - 快速 LLM 判断查询是否与知识库相关
Cypher 生成 - 精确的 LLM 将自然语言转换为 Cypher 查询
检索 - 在 Neo4j 知识图谱中执行查询
响应 - 返回结构化的上下文数据

数据流水线

用于将文档摄入知识图谱的独立 ETL 流水线：

流水线步骤：

文档加载 - PDF 和文本文档摄入
文本提取 - OCR 和内容提取
LLM 处理 - 从内容生成 Cypher 查询
图填充 - 执行查询以构建知识图谱

配置

将 .env.example 复制为 .env 并进行配置：

########################################
# LLM / AI Provider Keys
########################################

# OpenAI API key (optional)
OPENAI_API_KEY=

# DeepSeek API key (optional)
DEEPSEEK_API_KEY=

# Google Generative AI / PaLM API key (optional)
GOOGLE_API_KEY=

# CLARIN LLM API key (optional, used by API & client)
CLARIN_API_KEY=


########################################
# Langfuse Observability
########################################

LANGFUSE_SECRET_KEY=
LANGFUSE_PUBLIC_KEY=
LANGFUSE_HOST=https://cloud.langfuse.com


########################################
# Neo4j Database
########################################

# URI used by data pipeline, MCP server and graph config
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=


########################################
# Data Pipeline Runtime Controls
########################################

# Max parallel pages processed per batch
DATA_PIPELINE_MAX_CONCURRENCY=4

# Minutes after which a stuck in-progress hash can be reclaimed
DATA_PIPELINE_CLAIM_STALE_MINUTES=30


########################################
# MCP Server Networking
########################################

# Bind host for the MCP server process
MCP_BIND_HOST=0.0.0.0

# Host/port used by API and MCP client to reach the MCP server
MCP_HOST=127.0.0.1
MCP_PORT=8005

命令

# Docker Stack
just up          # Start all services (including frontend at :80)
just down        # Stop services
just logs        # View logs
just ps          # Service status
just nuke        # Remove everything

# Local Development
just mcp-server  # Run MCP server
just api         # Run FastAPI
just kg "query"  # Query knowledge graph

# Frontend
just frontend-install  # Install npm dependencies
just frontend-dev      # Start dev server at :3000 (requires running API)
just frontend-build    # Build for production

# Quality
just lint        # Format & lint
just test        # Run tests
just ci          # Full CI pipeline
uv run --with pytest python -m pytest tests/data_pipeline/test_pipeline_concurrency.py -q
                # Run pipeline concurrency/idempotency tests only

# Data Pipeline
just prefect-up  # Start Prefect
just pipeline    # Run ETL

项目结构

src/
├── mcp_server/      # MCP server + RAG pipeline
├── mcp_client/      # CLI client
├── topwr_api/       # FastAPI backend
├── config/          # Configuration
└── data_pipeline/   # Prefect ETL flows

frontend/
├── src/
│   ├── api/         # API client
│   ├── hooks/       # useUserId, useSessions, useChat, useTheme
│   ├── components/  # Sidebar, Chat, shared UI
│   └── types/       # TypeScript mirrors of backend models
└── package.json     # React + Vite + TailwindCSS

docker/
├── compose.stack.yml    # Main stack (Neo4j + MCP + API + Frontend)
├── compose.prefect.yml  # Data pipeline
├── Dockerfile.mcp       # MCP server image
├── Dockerfile.api       # FastAPI image
├── Dockerfile.frontend  # React + Nginx image
└── nginx.conf           # SPA fallback + API proxy

API 使用

聊天端点

curl -X POST http://localhost:8000/api/chat \
  -H "Content-Type: application/json" \
  -d '{"user_id": "user1", "message": "Czym jest nagroda dziekana?"}'

响应：

{
  "session_id": "abc123",
  "message": "Nagroda dziekana to wyróżnienie przyznawane...",
  "metadata": {
    "source": "mcp_knowledge_graph",
    "trace_id": "xyz789"
  }
}

会话管理

# Get session history
curl http://localhost:8000/api/sessions/{session_id}/history

# List user sessions  
curl http://localhost:8000/api/users/{user_id}/sessions

技术栈

技术	用途
React 18 + TypeScript	前端聊天 UI
Vite + TailwindCSS v3	构建工具与样式
Nginx	前端服务 + API 代理
FastMCP	模型上下文协议服务器
LangGraph	RAG 状态机
LangChain	LLM 编排
Neo4j	知识图谱数据库
FastAPI	REST API 后端
Langfuse	可观测性（可选）
Prefect	数据流水线编排
Docker	容器化

许可证

MIT © Solvro

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