基于Graphiti构建AI知识图谱服务：从集成到实践-优快云博客

本文链接：https://blog.youkuaiyun.com/csdn122345/article/details/148514362

摘要

本文详细介绍如何使用Graphiti MCP Server构建和集成AI知识图谱服务。通过Model Context Protocol (MCP)协议，实现AI助手与知识图谱的无缝交互，支持动态环境下的实时知识更新和查询。文章包含完整的集成方案、环境配置、服务部署以及最佳实践建议，帮助开发者快速构建智能化的知识图谱应用。

1. 知识图谱服务概述

1.1 什么是知识图谱服务

知识图谱服务是一种基于图数据库的AI服务，它能够：

实时集成用户交互数据
处理结构化和非结构化数据
支持增量数据更新
提供高效的知识检索
实现精确的历史查询

1.2 核心功能

功能模块	描述	应用场景
事件管理	添加、检索和删除事件	用户交互记录
实体管理	搜索和管理实体节点	知识图谱构建
搜索能力	语义和混合搜索	知识检索
分组管理	数据分组和过滤	多租户支持
图谱维护	清理和重建索引	系统维护

2. MCP协议详解

2.1 协议架构

2.2 数据流设计

3. 环境准备

3.1 基础环境要求

class EnvironmentChecker:
    """环境检查器"""
    def __init__(self):
        self.requirements = {
            "python": "3.10+",
            "neo4j": "5.26+",
            "openai": "latest",
            "uv": "latest"
        }
    
    def check_environment(self):
        """检查环境配置"""
        try:
            # 检查Python版本
            python_version = sys.version_info
            if python_version.major < 3 or \
               (python_version.major == 3 and python_version.minor < 10):
                raise EnvironmentError("Python版本需要3.10或更高")
            
            # 检查Neo4j连接
            self._check_neo4j()
            
            # 检查OpenAI配置
            self._check_openai()
            
            return True
        except Exception as e:
            logger.error(f"环境检查失败: {str(e)}")
            raise

3.2 环境变量配置

class ConfigManager:
    """配置管理器"""
    def __init__(self):
        self.required_vars = {
            "NEO4J_URI": "Neo4j数据库URI",
            "NEO4J_USER": "Neo4j用户名",
            "NEO4J_PASSWORD": "Neo4j密码",
            "OPENAI_API_KEY": "OpenAI API密钥",
            "MODEL_NAME": "OpenAI模型名称"
        }
    
    def validate_config(self):
        """验证配置"""
        missing_vars = []
        for var, desc in self.required_vars.items():
            if not os.getenv(var):
                missing_vars.append(f"{var} ({desc})")
        
        if missing_vars:
            raise EnvironmentError(
                f"缺少必要的环境变量: {', '.join(missing_vars)}"
            )

4. 服务部署

4.1 Docker部署配置

# docker-compose.yml
version: '3.8'

services:
  graphiti:
    image: zepai/graphiti:latest
    ports:
      - "8000:8000"
    environment:
      - NEO4J_URI=bolt://neo4j:7687
      - NEO4J_USER=${NEO4J_USER}
      - NEO4J_PASSWORD=${NEO4J_PASSWORD}
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - MODEL_NAME=${MODEL_NAME}
    depends_on:
      - neo4j

  neo4j:
    image: neo4j:5.26.0
    ports:
      - "7474:7474"  # HTTP
      - "7687:7687"  # Bolt
    volumes:
      - neo4j_data:/data
    environment:
      - NEO4J_AUTH=${NEO4J_USER}/${NEO4J_PASSWORD}

volumes:
  neo4j_data:

4.2 部署脚本

class DeploymentManager:
    """部署管理器"""
    def __init__(self, compose_file: str):
        self.compose_file = compose_file
    
    def deploy(self):
        """部署服务"""
        try:
            # 启动服务
            subprocess.run(
                ["docker-compose", "-f", self.compose_file, "up", "-d"],
                check=True
            )
            
            # 等待服务就绪
            self._wait_for_services()
            
            return True
        except Exception as e:
            logger.error(f"部署失败: {str(e)}")
            raise
    
    def _wait_for_services(self):
        """等待服务就绪"""
        services = {
            "graphiti": "http://localhost:8000/health",
            "neo4j": "http://localhost:7474"
        }
        
        for service, url in services.items():
            while True:
                try:
                    response = requests.get(url)
                    if response.status_code == 200:
                        break
                except:
                    time.sleep(5)

5. 客户端集成

5.1 Cursor IDE集成

{
  "mcpServers": {
    "graphiti-memory": {
      "url": "http://localhost:8000/sse"
    }
  }
}

5.2 Claude Desktop集成

{
  "mcpServers": {
    "graphiti-memory": {
      "command": "npx",
      "args": [
        "mcp-remote",
        "http://localhost:8000/sse"
      ]
    }
  }
}

6. 功能开发

6.1 事件管理

class EpisodeManager:
    """事件管理器"""
    def __init__(self, client):
        self.client = client
    
    def add_episode(self, name: str, content: str, source: str = "text"):
        """添加事件
        
        Args:
            name: 事件名称
            content: 事件内容
            source: 事件来源
        """
        try:
            response = self.client.add_episode(
                name=name,
                episode_body=content,
                source=source
            )
            return response
        except Exception as e:
            logger.error(f"添加事件失败: {str(e)}")
            raise

6.2 知识检索

class KnowledgeRetriever:
    """知识检索器"""
    def __init__(self, client):
        self.client = client
    
    def search_nodes(self, query: str, limit: int = 10):
        """搜索节点
        
        Args:
            query: 搜索查询
            limit: 结果限制
        """
        try:
            response = self.client.search_nodes(
                query=query,
                limit=limit
            )
            return response
        except Exception as e:
            logger.error(f"搜索节点失败: {str(e)}")
            raise