ModelEngine智能体全流程体验评测：从创建到部署的深度实践

{
  "agent_config": {
    "name": "客服助手",
    "description": "专业的客户服务智能体",
    "model": "gpt-4-turbo",
    "temperature": 0.7,
    "max_tokens": 2048
  },
  "knowledge_base": {
    "enabled": true,
    "auto_summary": true,
    "embedding_model": "text-embedding-ada-002"
  }
}

# 知识库文档处理示例
class DocumentProcessor:
    def __init__(self, embedding_model):
        self.embedding_model = embedding_model
        
    def process_document(self, file_path):
        # 文档解析
        content = self.extract_content(file_path)
        
        # 智能分块
        chunks = self.smart_chunking(content)
        
        # 向量化处理
        embeddings = self.generate_embeddings(chunks)
        
        # 生成摘要
        summary = self.generate_summary(content)
        
        return {
            "chunks": chunks,
            "embeddings": embeddings,
            "summary": summary
        }
    
    def smart_chunking(self, content):
        """基于语义的智能分块算法"""
        # 实现语义相关性分析
        # 确保分块的完整性和连贯性
        pass

// 智能体测试脚本示例
const testCases = [
  {
    input: "我想了解产品的退换货政策",
    expected_intent: "policy_inquiry",
    expected_response_type: "informative"
  },
  {
    input: "帮我处理订单退款",
    expected_intent: "refund_request",
    expected_response_type: "action_required"
  }
];

async function runTests(agent) {
  const results = [];
  
  for (const testCase of testCases) {
    const response = await agent.chat(testCase.input);
    const evaluation = await evaluateResponse(response, testCase);
    results.push(evaluation);
  }
  
  return generateTestReport(results);
}

class KnowledgeBaseSummarizer:
    def __init__(self, model_config):
        self.extractor = KeyInfoExtractor()
        self.generator = SummaryGenerator(model_config)
        self.evaluator = QualityEvaluator()
    
    def generate_summary(self, documents):
        # 第一阶段：关键信息提取
        key_points = []
        for doc in documents:
            points = self.extractor.extract_key_points(doc)
            key_points.extend(points)
        
        # 第二阶段：信息聚合与去重
        clustered_points = self.cluster_similar_points(key_points)
        
        # 第三阶段：摘要生成
        summary = self.generator.generate(clustered_points)
        
        # 第四阶段：质量评估与优化
        quality_score = self.evaluator.evaluate(summary, documents)
        if quality_score < 0.8:
            summary = self.optimize_summary(summary, documents)
        
        return {
            "summary": summary,
            "key_points": clustered_points,
            "quality_score": quality_score
        }

class PromptGenerator:
    def __init__(self):
        self.scene_analyzer = SceneAnalyzer()
        self.intent_classifier = IntentClassifier()
        self.template_optimizer = TemplateOptimizer()
    
    def generate_prompt(self, context):
        # 场景分析
        scene_features = self.scene_analyzer.analyze(context)
        
        # 意图分类
        intent_categories = self.intent_classifier.classify(context)
        
        # 基础模板生成
        base_template = self.create_base_template(
            scene_features, 
            intent_categories
        )
        
        # 知识库集成
        kb_enhanced_template = self.integrate_knowledge_base(
            base_template, 
            context.knowledge_base
        )
        
        # 模板优化
        optimized_template = self.template_optimizer.optimize(
            kb_enhanced_template,
            context.performance_metrics
        )
        
        return optimized_template
    
    def create_base_template(self, scene_features, intent_categories):
        """基于场景和意图生成基础模板"""
        template_parts = []
        
        # 角色定义
        role_definition = self.generate_role_definition(scene_features)
        template_parts.append(role_definition)
        
        # 任务描述
        task_description = self.generate_task_description(intent_categories)
        template_parts.append(task_description)
        
        # 行为约束
        constraints = self.generate_constraints(scene_features)
        template_parts.append(constraints)
        
        return "\n\n".join(template_parts)

class ConversationDebugger {
    constructor(agent) {
        this.agent = agent;
        this.traceLog = [];
    }
    
    async debugConversation(input) {
        const startTime = Date.now();
        
        // 记录输入
        this.traceLog.push({
            type: 'input',
            content: input,
            timestamp: startTime
        });
        
        // 执行推理过程
        const reasoning = await this.agent.reason(input);
        this.traceLog.push({
            type: 'reasoning',
            content: reasoning,
            timestamp: Date.now()
        });
        
        // 知识库检索
        const retrieval = await this.agent.retrieveKnowledge(input);
        this.traceLog.push({
            type: 'retrieval',
            content: retrieval,
            timestamp: Date.now()
        });
        
        // 生成响应
        const response = await this.agent.generateResponse(input, reasoning, retrieval);
        this.traceLog.push({
            type: 'response',
            content: response,
            timestamp: Date.now()
        });
        
        return {
            response,
            debugInfo: this.generateDebugReport()
        };
    }
    
    generateDebugReport() {
        return {
            totalTime: this.traceLog[this.traceLog.length - 1].timestamp - this.traceLog[0].timestamp,
            steps: this.traceLog,
            performance: this.analyzePerformance()
        };
    }
}

{
  "mcp_config": {
    "servers": [
      {
        "name": "database_server",
        "command": "python",
        "args": ["-m", "mcp_server.database"],
        "env": {
          "DATABASE_URL": "postgresql://localhost:5432/mydb"
        }
      },
      {
        "name": "file_server",
        "command": "node",
        "args": ["file-server.js"],
        "cwd": "/path/to/file/server"
      }
    ]
  }
}

class OfficeAssistant:
    def __init__(self, mcp_client):
        self.mcp_client = mcp_client
        
    async def handle_request(self, user_input):
        # 解析用户意图
        intent = await self.parse_intent(user_input)
        
        if intent == "schedule_meeting":
            return await self.schedule_meeting_via_mcp(user_input)
        elif intent == "query_data":
            return await self.query_database_via_mcp(user_input)
        elif intent == "file_operation":
            return await self.handle_file_via_mcp(user_input)
    
    async def schedule_meeting_via_mcp(self, request):
        # 通过MCP调用日历服务
        calendar_tool = await self.mcp_client.get_tool("calendar", "create_event")
        result = await calendar_tool.call({
            "title": request.meeting_title,
            "start_time": request.start_time,
            "attendees": request.attendees
        })
        return f"会议已安排：{result.event_id}"

class MultiAgentSystem:
    def __init__(self):
        self.router_agent = RouterAgent()
        self.technical_agent = TechnicalSupportAgent()
        self.sales_agent = SalesAgent()
        self.escalation_agent = EscalationAgent()
        
    async def process_request(self, user_input):
        # 路由决策
        routing_decision = await self.router_agent.route(user_input)
        
        # 分发到专业智能体
        if routing_decision.category == "technical":
            response = await self.technical_agent.handle(user_input)
        elif routing_decision.category == "sales":
            response = await self.sales_agent.handle(user_input)
        else:
            response = await self.escalation_agent.handle(user_input)
        
        # 质量检查
        if response.confidence < 0.8:
            response = await self.escalation_agent.review(response)
        
        return response

{
  "message_type": "collaboration_request",
  "sender": "technical_agent",
  "receiver": "sales_agent",
  "context": {
    "user_query": "产品技术规格和价格信息",
    "technical_analysis": "产品符合用户需求",
    "confidence": 0.9
  },
  "request": "provide_pricing_info"
}

# 性能测试脚本
async def benchmark_platforms():
    test_cases = load_test_cases()
    platforms = [ModelEngine(), Dify(), Coze(), Versatile()]
    
    results = {}
    
    for platform in platforms:
        platform_results = {
            "response_time": [],
            "accuracy": [],
            "throughput": []
        }
        
        for test_case in test_cases:
            start_time = time.time()
            response = await platform.process(test_case.input)
            end_time = time.time()
            
            platform_results["response_time"].append(end_time - start_time)
            platform_results["accuracy"].append(
                evaluate_accuracy(response, test_case.expected)
            )
        
        results[platform.name] = platform_results
    
    return generate_benchmark_report(results)