处理agent答非所问的问题

test_workflow_data_flow.py

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+#!/usr/bin/env python3
+"""
+工作流数据流转测试脚本
+用于诊断"答非所问"问题
+"""
+import asyncio
+import json
+import sys
+import os
+
+# 添加项目路径
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'backend'))
+
+from app.services.workflow_engine import WorkflowEngine
+from app.core.database import SessionLocal
+
+
+def print_section(title):
+    """打印分隔线"""
+    print("\n" + "=" * 80)
+    print(f"  {title}")
+    print("=" * 80)
+
+
+def print_data(label, data, indent=0):
+    """格式化打印数据"""
+    prefix = "  " * indent
+    print(f"{prefix}{label}:")
+    if isinstance(data, dict):
+        print(f"{prefix}  {json.dumps(data, ensure_ascii=False, indent=2)}")
+    else:
+        print(f"{prefix}  {data}")
+
+
+async def test_workflow_data_flow():
+    """测试工作流数据流转"""
+    print_section("工作流数据流转测试")
+    
+    # 模拟一个简单的工作流
+    workflow_data = {
+        "nodes": [
+            {
+                "id": "start-1",
+                "type": "start",
+                "position": {"x": 100, "y": 100},
+                "data": {
+                    "label": "开始"
+                }
+            },
+            {
+                "id": "llm-1",
+                "type": "llm",
+                "position": {"x": 300, "y": 100},
+                "data": {
+                    "label": "LLM处理",
+                    "provider": "deepseek",
+                    "model": "deepseek-chat",
+                    "prompt": "请处理用户请求。",
+                    "temperature": 0.5,
+                    "max_tokens": 1500
+                }
+            },
+            {
+                "id": "end-1",
+                "type": "end",
+                "position": {"x": 500, "y": 100},
+                "data": {
+                    "label": "结束",
+                    "output_format": "text"
+                }
+            }
+        ],
+        "edges": [
+            {"id": "e1", "source": "start-1", "target": "llm-1"},
+            {"id": "e2", "source": "llm-1", "target": "end-1"}
+        ]
+    }
+    
+    # 模拟前端发送的输入数据
+    input_data = {
+        "query": "苹果英语怎么讲?",
+        "USER_INPUT": "苹果英语怎么讲?"
+    }
+    
+    print_section("1. 初始输入数据")
+    print_data("input_data", input_data)
+    
+    # 创建引擎（不使用logger，避免数据库依赖）
+    engine = WorkflowEngine("test-workflow", workflow_data)
+    
+    # 重写get_node_input方法，添加详细日志
+    original_get_node_input = engine.get_node_input
+    
+    def logged_get_node_input(node_id, node_outputs, active_edges=None):
+        print_section(f"获取节点输入: {node_id}")
+        print_data("node_outputs", node_outputs)
+        result = original_get_node_input(node_id, node_outputs, active_edges)
+        print_data(f"返回的input_data (for {node_id})", result)
+        return result
+    
+    engine.get_node_input = logged_get_node_input
+    
+    # 重写execute_node方法，添加详细日志
+    original_execute_node = engine.execute_node
+    
+    async def logged_execute_node(node, input_data):
+        node_id = node.get('id')
+        node_type = node.get('type')
+        
+        print_section(f"执行节点: {node_id} ({node_type})")
+        print_data("节点配置", node.get('data', {}))
+        print_data("输入数据", input_data)
+        
+        result = await original_execute_node(node, input_data)
+        
+        print_data("执行结果", result)
+        
+        # 如果是LLM节点，特别关注prompt和输出
+        if node_type == 'llm':
+            print_section(f"LLM节点详细分析: {node_id}")
+            node_data = node.get('data', {})
+            prompt = node_data.get('prompt', '')
+            print_data("原始prompt", prompt)
+            print_data("输入数据", input_data)
+            
+            # 模拟prompt格式化逻辑
+            if isinstance(input_data, dict):
+                # 检查是否有嵌套的input字段
+                nested_input = input_data.get('input')
+                if isinstance(nested_input, dict):
+                    print("⚠️  发现嵌套的input字段！")
+                    print_data("嵌套input内容", nested_input)
+                    # 尝试提取user_query
+                    user_query = None
+                    for key in ['query', 'input', 'text', 'message', 'content', 'user_input', 'USER_INPUT']:
+                        if key in nested_input:
+                            user_query = nested_input[key]
+                            print(f"✅ 从嵌套input中提取到user_query: {key} = {user_query}")
+                            break
+                else:
+                    # 从顶层提取
+                    user_query = None
+                    for key in ['query', 'input', 'text', 'message', 'content', 'user_input', 'USER_INPUT']:
+                        if key in input_data:
+                            value = input_data[key]
+                            if isinstance(value, str):
+                                user_query = value
+                                print(f"✅ 从顶层提取到user_query: {key} = {user_query}")
+                                break
+        
+        return result
+    
+    engine.execute_node = logged_execute_node
+    
+    # 执行工作流
+    print_section("开始执行工作流")
+    try:
+        result = await engine.execute(input_data)
+        print_section("工作流执行完成")
+        print_data("最终结果", result)
+    except Exception as e:
+        print_section("执行出错")
+        print(f"错误: {str(e)}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    asyncio.run(test_workflow_data_flow())