迁移到了pydantic

core/config/manager.py

@@ -9,7 +9,7 @@ import json
 import os
 import logging
 from pathlib import Path
-from typing import Dict, Type, TypeVar, Optional
+from typing import Dict, Type, TypeVar, Optional, Any, cast
 
 from core.config.models import (
     BaseConfig, AIModelConfig, SystemConfig, GenerateTopicConfig, ResourceConfig,
@@ -66,7 +66,7 @@ class ConfigManager:
         self.register_config('topic_gen', GenerateTopicConfig)
         self.register_config('content_gen', GenerateContentConfig)
 
-    def register_config(self, name: str, config_class: Type[T]):
+    def register_config(self, name: str, config_class: Type[T]) -> None:
         """
         注册一个配置类
 
@@ -91,14 +91,27 @@ class ConfigManager:
             配置实例
         """
         config = self._configs.get(name)
-        if not isinstance(config, config_class):
+        if config is None:
             # 如果配置不存在，先注册一个默认实例
-            if config is None:
-                self.register_config(name, config_class)
-                config = self._configs.get(name)
-            else:
-                raise TypeError(f"Configuration '{name}' is not of type '{config_class.__name__}'")
-        return config
+            self.register_config(name, config_class)
+            config = self._configs.get(name)
+            
+        # 确保配置是正确的类型
+        if not isinstance(config, config_class):
+            # 尝试转换配置
+            try:
+                if isinstance(config, BaseConfig):
+                    # 将现有配置转换为请求的类型
+                    new_config = config_class(**config.model_dump())
+                    self._configs[name] = new_config
+                    config = new_config
+                else:
+                    raise TypeError(f"Configuration '{name}' is not of type '{config_class.__name__}'")
+            except Exception as e:
+                logger.error(f"转换配置 '{name}' 到类型 '{config_class.__name__}' 失败: {e}")
+                raise TypeError(f"Configuration '{name}' is not of type '{config_class.__name__}'") from e
+                
+        return cast(T, config)
     
     def _load_all_configs_from_dir(self):
         """动态加载目录中的所有.json文件"""
@@ -176,7 +189,8 @@ class ConfigManager:
             raise ValueError("配置目录未设置，无法保存文件")
 
         path = self.config_dir / f"{name}.json"
-        config_data = self.get_config(name, BaseConfig).to_dict()
+        config = self.get_config(name, BaseConfig)
+        config_data = config.to_dict()
 
         try:
             with open(path, 'w', encoding='utf-8') as f:

core/config/models.py

@@ -4,18 +4,23 @@
 """
 配置模型
 定义了项目中所有配置的数据类模型
+使用pydantic进行数据验证和序列化
 """
 
-from dataclasses import dataclass, field, fields, asdict
-from typing import Dict, Any, List, Optional
-from pydantic import BaseModel, Field
+from typing import Dict, Any, List, Optional, Type, TypeVar, Union, ClassVar
+from pydantic import BaseModel, Field, model_validator, ConfigDict
 
+T = TypeVar('T', bound='BaseConfig')
 
-# 基础配置类，提供通用方法
-class BaseConfig:
+class BaseConfig(BaseModel):
     """可从字典更新的配置基类"""
     
-    def update(self, new_data: Dict[str, Any]):
+    model_config = ConfigDict(
+        extra="allow",  # 允许额外字段
+        arbitrary_types_allowed=True  # 允许任意类型
+    )
+    
+    def update(self, new_data: Dict[str, Any]) -> None:
         """从字典递归更新配置"""
         for key, value in new_data.items():
             if hasattr(self, key):
@@ -24,20 +29,105 @@ class BaseConfig:
                 if isinstance(current_attr, BaseConfig) and isinstance(value, dict):
                     # 递归更新嵌套的配置对象
                     current_attr.update(value)
-                # 新增：处理ReferItem列表的特殊情况
-                elif key == 'refer_list' and isinstance(value, list):
-                    # 显式地从字典创建ReferItem对象
-                    setattr(self, key, [ReferItem(**item) for item in value])
                 else:
                     # 否则，直接设置值
                     setattr(self, key, value)
-
+    
     def to_dict(self) -> Dict[str, Any]:
         """将配置转换为字典"""
-        return asdict(self)
+        return self.model_dump()
+
+
+class ReferItem(BaseConfig):
+    """单个Refer项"""
+    path: str = ""
+    sampling_rate: float = 1.0
+    step: str = ""  # 可选值: "topic", "content", "judge"，表示在哪个阶段使用
+
+
+class ReferConfig(BaseConfig):
+    """Refer配置，现在是一个列表"""
+    refer_list: List[ReferItem] = Field(default_factory=list)
+    
+    def update(self, new_data: Dict[str, Any]) -> None:
+        """特殊处理refer_list的更新"""
+        if 'refer_list' in new_data and isinstance(new_data['refer_list'], list):
+            # 将字典列表转换为ReferItem对象列表
+            new_list = []
+            for item in new_data['refer_list']:
+                if isinstance(item, dict):
+                    new_list.append(ReferItem(**item))
+                elif isinstance(item, ReferItem):
+                    new_list.append(item)
+            self.refer_list = new_list
+            
+            # 移除已处理的refer_list，避免重复处理
+            new_data_copy = new_data.copy()
+            new_data_copy.pop('refer_list')
+            super().update(new_data_copy)
+        else:
+            super().update(new_data)
+
+
+class PathConfig(BaseConfig):
+    """单个路径配置"""
+    path: str = ""
+
+
+class PathListConfig(BaseConfig):
+    """路径列表配置"""
+    paths: List[str] = Field(default_factory=list)
+
+
+class SamplingPathListConfig(BaseConfig):
+    """带采样率的路径列表配置"""
+    sampling_rate: float = 1.0
+    paths: List[str] = Field(default_factory=list)
+
+
+class OutputConfig(BaseConfig):
+    """输出配置"""
+    base_dir: str = "result"
+    image_dir: str = "images"
+    topic_dir: str = "topics"
+    content_dir: str = "contents"
+
+
+class ResourceConfig(BaseConfig):
+    """资源配置"""
+    resource_dirs: List[str] = Field(default_factory=list)
+    style: PathListConfig = Field(default_factory=PathListConfig)
+    demand: PathListConfig = Field(default_factory=PathListConfig)
+    object: PathListConfig = Field(default_factory=PathListConfig)
+    product: PathListConfig = Field(default_factory=PathListConfig)
+    refer: ReferConfig = Field(default_factory=ReferConfig)
+    image: PathListConfig = Field(default_factory=PathListConfig)
+    output_dir: OutputConfig = Field(default_factory=OutputConfig)
+
+
+class SystemConfig(BaseConfig):
+    """系统配置"""
+    debug: bool = False
+    log_level: str = "INFO"
+    parallel_processing: bool = True
+    max_workers: int = 4
+
+
+class TopicConfig(BaseConfig):
+    """选题配置"""
+    date: str = ""
+    num: int = 5
+    variants: int = 1
+
+
+class GenerateTopicConfig(BaseConfig):
+    """主题生成配置"""
+    topic_system_prompt: str = "resource/prompt/generateTopics/system.txt"
+    topic_user_prompt: str = "resource/prompt/generateTopics/user.txt"
+    model: Dict[str, Any] = Field(default_factory=dict)
+    topic: TopicConfig = Field(default_factory=TopicConfig)
 
 
-@dataclass
 class GenerateContentConfig(BaseConfig):
     """内容生成配置"""
     content_system_prompt: str = "resource/prompt/generateContent/contentSystem.txt"
@@ -46,9 +136,10 @@ class GenerateContentConfig(BaseConfig):
     judger_user_prompt: str = "resource/prompt/judgeContent/user.txt"
     enable_content_judge: bool = True
     refer_sampling_rate: float = 1.0
+    model: Dict[str, Any] = Field(default_factory=dict)
+    judger_model: Dict[str, Any] = Field(default_factory=dict)
 
 
-@dataclass
 class AIModelConfig(BaseConfig):
     """AI模型配置"""
     model: str = "qwq-plus"
@@ -63,99 +154,19 @@ class AIModelConfig(BaseConfig):
     topic_user_prompt: str = "resource/prompt/generateTopics/user.txt"
     refer_sampling_rate: float = Field(0.5, ge=0.0, le=1.0)
 
-    class Config:
-        pass
 
-
-@dataclass
 class PosterConfig(BaseConfig):
     """海报生成配置"""
-    target_size: List[int] = field(default_factory=lambda: [900, 1200])
+    target_size: List[int] = Field(default_factory=lambda: [900, 1200])
     additional_images_enabled: bool = True
     template_selection: str = "random"  # random, business, vibrant, original
-    available_templates: List[str] = field(default_factory=lambda: ["original", "business", "vibrant"])
+    available_templates: List[str] = Field(default_factory=lambda: ["original", "business", "vibrant"])
 
 
-@dataclass
 class ContentConfig(BaseConfig):
     """内容生成配置"""
     enable_content_judge: bool = True
     num: int = 5
     variants_per_topic: int = 1
     max_title_length: int = 30
-    max_content_length: int = 500
-
-
-@dataclass
-class PathConfig(BaseConfig):
-    """单个路径配置"""
-    path: str = ""
-
-@dataclass
-class PathListConfig(BaseConfig):
-    """路径列表配置"""
-    paths: List[str] = field(default_factory=list)
-
-@dataclass
-class SamplingPathListConfig(BaseConfig):
-    """带采样率的路径列表配置"""
-    sampling_rate: float = 1.0
-    paths: List[str] = field(default_factory=list)
-
-@dataclass
-class ReferItem:
-    """单个Refer项"""
-    path: str = ""
-    sampling_rate: float = 1.0
-    step: str = ""  # 可选值: "topic", "content", "judge"，表示在哪个阶段使用
-
-@dataclass
-class ReferConfig(BaseConfig):
-    """Refer配置，现在是一个列表"""
-    refer_list: List[ReferItem] = field(default_factory=list)
-
-@dataclass
-class OutputConfig(BaseConfig):
-    """输出配置"""
-    base_dir: str = "result"
-    image_dir: str = "images"
-    topic_dir: str = "topics"
-    content_dir: str = "contents"
-
-
-@dataclass
-class ResourceConfig(BaseConfig):
-    """资源配置"""
-    resource_dirs: List[str] = field(default_factory=list)
-    style: PathListConfig = field(default_factory=PathListConfig)
-    demand: PathListConfig = field(default_factory=PathListConfig)
-    object: PathListConfig = field(default_factory=PathListConfig)
-    product: PathListConfig = field(default_factory=PathListConfig)
-    refer: ReferConfig = field(default_factory=ReferConfig)
-    image: PathListConfig = field(default_factory=PathListConfig)
-    output_dir: OutputConfig = field(default_factory=OutputConfig)
-
-
-@dataclass
-class SystemConfig(BaseConfig):
-    """系统配置"""
-    debug: bool = False
-    log_level: str = "INFO"
-    parallel_processing: bool = True
-    max_workers: int = 4
-
-
-@dataclass
-class TopicConfig(BaseConfig):
-    """选题配置"""
-    date: str = ""
-    num: int = 5
-    variants: int = 1
-
-@dataclass
-class GenerateTopicConfig(BaseConfig):
-    """主题生成配置"""
-    topic_system_prompt: str = "resource/prompt/generateTopics/system.txt"
-    topic_user_prompt: str = "resource/prompt/generateTopics/user.txt"
-    model: Dict[str, Any] = field(default_factory=dict)
-    topic: TopicConfig = field(default_factory=TopicConfig) 
+    max_content_length: int = 500 

tests/test_config.py

@@ -0,0 +1,146 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""
+测试配置管理器和pydantic模型的兼容性
+"""
+
+import os
+import json
+import tempfile
+from pathlib import Path
+
+from core.config.manager import ConfigManager
+from core.config.models import (
+    BaseConfig, AIModelConfig, SystemConfig, GenerateTopicConfig, ResourceConfig,
+    GenerateContentConfig, PosterConfig, ContentConfig, ReferItem, ReferConfig
+)
+
+def test_config_creation():
+    """测试创建配置实例"""
+    print("测试创建配置实例...")
+    
+    # 创建各种配置实例
+    ai_config = AIModelConfig(model="test-model", api_key="test-key")
+    print(f"AI配置: {ai_config.model}, {ai_config.api_key}")
+    
+    # 测试嵌套配置
+    resource_config = ResourceConfig(
+        resource_dirs=["resource"],
+        refer=ReferConfig(
+            refer_list=[
+                ReferItem(path="path1", sampling_rate=0.5, step="topic"),
+                ReferItem(path="path2", sampling_rate=0.8, step="content")
+            ]
+        )
+    )
+    
+    # 验证嵌套配置
+    print(f"资源目录: {resource_config.resource_dirs}")
+    print(f"Refer项数量: {len(resource_config.refer.refer_list)}")
+    print(f"第一个Refer项: {resource_config.refer.refer_list[0].path}, {resource_config.refer.refer_list[0].sampling_rate}")
+    
+    # 测试序列化
+    config_dict = resource_config.to_dict()
+    print(f"序列化后的字典: {json.dumps(config_dict, indent=2)}")
+    
+    return True
+
+def test_config_manager():
+    """测试配置管理器"""
+    print("\n测试配置管理器...")
+    
+    # 创建临时目录
+    with tempfile.TemporaryDirectory() as temp_dir:
+        # 创建测试配置文件
+        ai_config_path = Path(temp_dir) / "ai_model.json"
+        ai_config = {
+            "model": "test-model",
+            "api_key": "test-key",
+            "temperature": 0.8
+        }
+        
+        with open(ai_config_path, "w") as f:
+            json.dump(ai_config, f)
+            
+        # 创建嵌套配置文件
+        resource_config_path = Path(temp_dir) / "resource.json"
+        resource_config = {
+            "resource_dirs": ["test_resource"],
+            "refer": {
+                "refer_list": [
+                    {"path": "test_path", "sampling_rate": 0.7, "step": "judge"}
+                ]
+            }
+        }
+        
+        with open(resource_config_path, "w") as f:
+            json.dump(resource_config, f)
+        
+        # 初始化配置管理器
+        config_manager = ConfigManager()
+        config_manager.load_from_directory(temp_dir)
+        
+        # 获取并验证AI配置
+        ai_model_config = config_manager.get_config("ai_model", AIModelConfig)
+        print(f"加载的AI配置: {ai_model_config.model}, {ai_model_config.api_key}, {ai_model_config.temperature}")
+        assert ai_model_config.model == "test-model"
+        assert ai_model_config.api_key == "test-key"
+        assert ai_model_config.temperature == 0.8
+        
+        # 获取并验证资源配置
+        resource_config = config_manager.get_config("resource", ResourceConfig)
+        print(f"加载的资源配置: {resource_config.resource_dirs}")
+        print(f"加载的Refer项: {resource_config.refer.refer_list[0].path}, {resource_config.refer.refer_list[0].sampling_rate}")
+        assert resource_config.resource_dirs == ["test_resource"]
+        assert len(resource_config.refer.refer_list) == 1
+        assert resource_config.refer.refer_list[0].path == "test_path"
+        assert resource_config.refer.refer_list[0].sampling_rate == 0.7
+        
+        # 测试更新配置
+        ai_model_config.update({"model": "updated-model"})
+        print(f"更新后的AI配置: {ai_model_config.model}")
+        assert ai_model_config.model == "updated-model"
+        
+        # 测试保存配置
+        config_manager.save_config("ai_model")
+        
+        # 重新加载并验证
+        with open(ai_config_path, "r") as f:
+            saved_config = json.load(f)
+        print(f"保存的配置: {saved_config['model']}")
+        assert saved_config["model"] == "updated-model"
+        
+    return True
+
+def test_config_type_conversion():
+    """测试配置类型转换"""
+    print("\n测试配置类型转换...")
+    
+    # 创建配置管理器
+    config_manager = ConfigManager()
+    
+    # 注册一个SystemConfig
+    config_manager.register_config("test_config", SystemConfig)
+    
+    # 尝试以不同类型获取
+    system_config = config_manager.get_config("test_config", SystemConfig)
+    print(f"获取为SystemConfig: {type(system_config).__name__}")
+    
+    # 尝试转换类型
+    try:
+        content_config = config_manager.get_config("test_config", ContentConfig)
+        print(f"成功转换为ContentConfig: {type(content_config).__name__}")
+    except TypeError as e:
+        print(f"类型转换失败，符合预期: {e}")
+    
+    return True
+
+if __name__ == "__main__":
+    print("开始测试pydantic配置模型和ConfigManager...")
+    
+    test_config_creation()
+    test_config_manager()
+    test_config_type_conversion()
+    
+    print("\n所有测试完成!")