"""
数据盘点模块的数据模型
"""
from typing import Optional, List, Dict, Any
from pydantic import BaseModel, Field


# ==================== 请求模型 ====================

class FieldInput(BaseModel):
    """字段输入模型"""
    raw_name: str = Field(..., description="字段名（英文）")
    type: str = Field(..., description="字段类型")
    comment: Optional[str] = Field(None, description="字段注释（如果有）")


class TableInput(BaseModel):
    """表输入模型"""
    raw_name: str = Field(..., description="表名（英文/原始名称）")
    fields: List[FieldInput] = Field(..., description="字段列表", min_length=1)


class AnalyzeOptions(BaseModel):
    """AI 分析选项"""
    model: Optional[str] = Field("qwen-max", description="大模型选择（qwen-max/gpt-4）")
    temperature: Optional[float] = Field(0.3, ge=0.0, le=1.0, description="温度参数（0.0-1.0）")
    enable_pii_detection: Optional[bool] = Field(True, description="是否启用 PII 识别")
    enable_important_data_detection: Optional[bool] = Field(
        True, description="是否启用重要数据识别"
    )


class AIAnalyzeRequest(BaseModel):
    """AI 分析请求模型"""
    tables: List[TableInput] = Field(..., description="表列表", min_length=1)
    project_id: str = Field(..., description="项目ID")
    industry: Optional[str] = Field(None, description="行业信息（如：retail-fresh）")
    context: Optional[str] = Field(None, description="业务背景信息")
    options: Optional[AnalyzeOptions] = Field(None, description="可选配置")

    class Config:
        json_schema_extra = {
            "example": {
                "tables": [
                    {
                        "raw_name": "t_user_base_01",
                        "fields": [
                            {
                                "raw_name": "user_id",
                                "type": "varchar(64)",
                                "comment": "用户ID"
                            },
                            {
                                "raw_name": "phone",
                                "type": "varchar(11)",
                                "comment": "手机号"
                            }
                        ]
                    }
                ],
                "project_id": "project_001",
                "industry": "retail-fresh",
                "context": "某连锁生鲜零售企业，主营水果、蔬菜等生鲜产品",
                "options": {
                    "model": "qwen-max",
                    "temperature": 0.3,
                    "enable_pii_detection": True,
                    "enable_important_data_detection": True
                }
            }
        }


# ==================== 响应模型 ====================

class FieldOutput(BaseModel):
    """字段输出模型"""
    raw_name: str = Field(..., description="字段名（英文/原始名称）")
    ai_name: str = Field(..., description="AI 识别的中文名称")
    desc: str = Field(..., description="业务描述")
    type: str = Field(..., description="字段类型")
    pii: List[str] = Field(default_factory=list, description="识别的 PII 信息列表")
    pii_type: Optional[str] = Field(None, description="PII 类型（contact/identity/name/email等）")
    is_important_data: bool = Field(False, description="是否重要数据")
    confidence: int = Field(..., ge=0, le=100, description="置信度评分（0-100）")


class TableOutput(BaseModel):
    """表输出模型"""
    raw_name: str = Field(..., description="表名（英文/原始名称）")
    ai_name: str = Field(..., description="AI 识别的中文名称")
    desc: str = Field(..., description="业务描述")
    confidence: int = Field(..., ge=0, le=100, description="置信度评分（0-100）")
    ai_completed: bool = Field(True, description="AI 识别是否完成")
    fields: List[FieldOutput] = Field(..., description="字段列表")
    pii: List[str] = Field(default_factory=list, description="表的 PII 信息汇总")
    important: bool = Field(False, description="表是否包含重要数据")
    important_data_types: List[str] = Field(
        default_factory=list, description="重要数据类型列表"
    )


class Statistics(BaseModel):
    """统计信息"""
    total_tables: int = Field(..., description="总表数")
    total_fields: int = Field(..., description="总字段数")
    pii_fields_count: int = Field(0, description="包含 PII 的字段数")
    important_data_fields_count: int = Field(0, description="重要数据字段数")
    average_confidence: float = Field(..., ge=0, le=100, description="平均置信度")


class TokenUsage(BaseModel):
    """Token 使用情况"""
    prompt_tokens: int = Field(0, description="提示词 Token 数")
    completion_tokens: int = Field(0, description="完成 Token 数")
    total_tokens: int = Field(0, description="总 Token 数")


class AIAnalyzeResponse(BaseModel):
    """AI 分析响应模型"""
    tables: List[TableOutput] = Field(..., description="识别结果表列表")
    statistics: Statistics = Field(..., description="统计信息")
    processing_time: float = Field(..., description="处理耗时（秒）")
    model_used: str = Field(..., description="使用的大模型")
    token_usage: Optional[TokenUsage] = Field(None, description="Token 使用情况")