refactor(schema): 重构数据模型以提高可维护性和可扩展性

- 新增 LLMParameters 类以统一处理 LLM 参数
- 新增 TokensUsage 类以统一处理 token 使用信息
- 更新 LLMResponse 和 LLMRequest 类，使用新的 LLMParameters 和 TokensUsage 类
- 优化数据模型结构，提高代码的可读性和可维护性

schema/__init__.py

@@ -1,4 +1,4 @@
 from .dataset import *
-from .dataset_generation import APIProvider, LLMResponse, LLMRequest
+from .dataset_generation import *
 from .md_doc import MarkdownNode
 from .prompt import promptTempleta

schema/dataset_generation.py

@@ -12,40 +12,36 @@ class APIProvider(SQLModel, table=True):
         description="记录创建时间"
     )
 
+class LLMParameters(SQLModel):
+    temperature: Optional[float] = None
+    max_tokens: Optional[int] = None
+    top_p: Optional[float] = None
+    frequency_penalty: Optional[float] = None
+    presence_penalty: Optional[float] = None
+    seed: Optional[int] = None
+
+class TokensUsage(SQLModel):
+    prompt_tokens: int = Field(default=0, description="提示词使用的token数量")
+    completion_tokens: int = Field(default=0, description="完成部分使用的token数量")
+    prompt_cache_hit_tokens: Optional[int] = Field(default=None, description="缓存命中token数量")
+    prompt_cache_miss_tokens: Optional[int] = Field(default=None, description="缓存未命中token数量")
+
 class LLMResponse(SQLModel):
     timestamp: datetime  = Field(
         default_factory=lambda: datetime.now(timezone.utc),
         description="响应的时间戳"
     )
     response_id: str = Field(..., description="响应的唯一ID")
-    tokens_usage: dict = Field(default_factory=lambda: {
-        "prompt_tokens": 0,
-        "completion_tokens": 0,
-        "prompt_cache_hit_tokens": None,
-        "prompt_cache_miss_tokens": None
-    }, description="token使用信息")
+    tokens_usage: TokensUsage = Field(default_factory=TokensUsage, description="token使用信息")
     response_content: dict = Field(default_factory=dict, description="API响应的内容")
     total_duration: float = Field(default=0.0, description="请求的总时长，单位为秒")
-    llm_parameters: dict = Field(default_factory=lambda: {
-        "temperature": None,
-        "max_tokens": None,
-        "top_p": None,
-        "frequency_penalty": None,
-        "presence_penalty": None,
-        "seed": None
-    }, description="API的生成参数")
+    llm_parameters: Optional[LLMParameters] = Field(default=None, description="LLM参数")
 
 class LLMRequest(SQLModel):
     prompt: str = Field(..., description="发送给API的提示词") 
-    provider_id: int = Field(foreign_key="apiprovider.id")
-    provider: APIProvider = Relationship()
+    api_provider: APIProvider = Field(..., description="API提供者的信息")
     format: Optional[str] = Field(default=None, description="API响应的格式")
     response: list[LLMResponse] = Field(default_factory=list, description="API响应列表")
     error: Optional[list[str]] = Field(default=None, description="API请求过程中发生的错误信息")
     total_duration: float = Field(default=0.0, description="请求的总时长，单位为秒")
-    total_tokens_usage: dict = Field(default_factory=lambda: {
-        "prompt_tokens": 0,
-        "completion_tokens": 0,
-        "prompt_cache_hit_tokens": None,
-        "prompt_cache_miss_tokens": None
-    }, description="token使用信息")
+    total_tokens_usage: TokensUsage = Field(default_factory=TokensUsage, description="token使用信息")