model_validator的mode=“before“

"""

演示 mode="before" 中 data 参数的内容和返回值要求

"""

import typing as _t

from pydantic import BaseModel, model_validator

class DescribeWorkflowRequest(BaseModel):

session_id: str

dsl_data: _t.Optional[str] = None

dsl_path: _t.Optional[str] = None

@model_validator(mode="before")
@classmethod
def validate_dsl_source(cls, data):
    """演示 data 的内容和必须返回字典"""
    print("\n" + "="*50)
    print("【mode='before' 验证器执行】")
    print("="*50)
    print(f"data 的类型: {type(data)}")
    print(f"data 的内容: {data}")
    print(f"data 是否为字典: {isinstance(data, dict)}")
    
    # 如果 data 是字典，可以访问和修改
    if isinstance(data, dict):
        print("\n可以访问字典的键:")
        for key in data.keys():
            print(f"  - {key}: {data[key]}")
        
        # 可以修改字典
        if "dsl_data" in data and data["dsl_data"]:
            data["dsl_data"] = data["dsl_data"].strip()  # 去除空白
        
        # 可以添加新字段
        data["_processed"] = True
    
    print("\n返回修改后的字典...")
    print("="*50 + "\n")
    
    # ⚠️ 必须返回字典！
    if isinstance(data, dict):
        return data
    else:
        # 如果不是字典，需要转换为字典
        return dict(data) if hasattr(data, '__dict__') else {}

============================================================================

测试不同的输入场景

============================================================================

print("测试1: 从字典创建")

print("-" * 50)

request1 = DescribeWorkflowRequest(

session_id="abc123",

dsl_data=" some data ",

dsl_path=None

)

print(f"最终结果: {request1.model_dump()}")

print("\n测试2: 从字典创建（缺少字段）")

print("-" * 50)

try:

request2 = DescribeWorkflowRequest(

session_id="abc123"

缺少 dsl_data 和 dsl_path

)

print(f"最终结果: {request2.model_dump()}")

except Exception as e:

print(f"错误: {e}")

print("\n测试3: 从 JSON 字符串创建（模拟 API 请求）")

print("-" * 50)

import json

json_data = json.loads('{"session_id": "xyz789", "dsl_path": "/path/to/file.json"}')

request3 = DescribeWorkflowRequest(**json_data)

print(f"最终结果: {request3.model_dump()}")