aiagent/test_workflow_data_flow.py

#!/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())