api/models.py

"""
Request and response models for the API.
"""
from pydantic import BaseModel, Field, field_validator
from typing import List, Dict, Any, Optional, Union, Literal
class RangeFilter(BaseModel):
    """范围过滤器（支持数值和日期时间字符串）"""
    gte: Optional[Union[float, str]] = Field(None, description="大于等于 (>=)。数值或ISO日期时间字符串")
    gt: Optional[Union[float, str]] = Field(None, description="大于 (>)。数值或ISO日期时间字符串")
    lte: Optional[Union[float, str]] = Field(None, description="小于等于 (<=)。数值或ISO日期时间字符串")
    lt: Optional[Union[float, str]] = Field(None, description="小于 (<)。数值或ISO日期时间字符串")
    
    def model_post_init(self, __context):
        """确保至少指定一个边界值"""
        if not any([self.gte, self.gt, self.lte, self.lt]):
            raise ValueError('至少需要指定一个范围边界（gte, gt, lte, lt）')
    
    class Config:
        json_schema_extra = {
            "examples": [
                {"gte": 50, "lte": 200},
                {"gt": 100},
                {"lt": 50},
                {"gte": "2023-01-01T00:00:00Z"}
            ]
        }
class FacetConfig(BaseModel):
    """分面配置（简化版）"""
    field: str = Field(..., description="分面字段名")
    size: int = Field(10, ge=1, le=100, description="返回的分面值数量")
    type: Literal["terms", "range"] = Field("terms", description="分面类型")
    ranges: Optional[List[Dict[str, Any]]] = Field(
        None,
        description="范围分面的范围定义（仅当 type='range' 时需要）"
    )
    
    class Config:
        json_schema_extra = {
            "examples": [
                {
                    "field": "category.keyword",
                    "size": 15,
                    "type": "terms"
                },
                {
                    "field": "price",
                    "size": 4,
                    "type": "range",
                    "ranges": [
                        {"key": "0-50", "to": 50},
                        {"key": "50-100", "from": 50, "to": 100},
                        {"key": "100-200", "from": 100, "to": 200},
                        {"key": "200+", "from": 200}
                    ]
                }
            ]
        }
class SearchRequest(BaseModel):
    """搜索请求模型（重构版）"""
    
    # 基础搜索参数
    query: str = Field(..., description="搜索查询字符串，支持布尔表达式（AND, OR, RANK, ANDNOT）")
    size: int = Field(10, ge=1, le=100, description="返回结果数量")
    from_: int = Field(0, ge=0, alias="from", description="分页偏移量")
    language: Literal["zh", "en"] = Field(
        "zh",
        description="响应语言：'zh'（中文）或 'en'（英文），用于选择 title/description/vendor 等多语言字段"
    )
    
    # 过滤器 - 精确匹配和多值匹配
    filters: Optional[Dict[str, Union[str, int, bool, List[Union[str, int]], Dict[str, Any], List[Dict[str, Any]]]]] = Field(
        None,
        description="精确匹配过滤器。单值表示精确匹配，数组表示 OR 匹配（匹配任意一个值）。支持 specifications 嵌套过滤：{\"specifications\": {\"name\": \"color\", \"value\": \"green\"}} 或 [{\"name\": \"color\", \"value\": \"green\"}, ...]",
        json_schema_extra={
            "examples": [
                {
                    "category_name": ["手机", "电子产品"],
                    "vendor_zh.keyword": "奇乐",
                    "specifications": {"name": "颜色", "value": "白色"}
                },
                {
                    "specifications": [
                        {"name": "颜色", "value": "白色"},
                        {"name": "尺寸", "value": "256GB"}
                    ]
                }
            ]
        }
    )
    
    # 范围过滤器 - 数值范围
    range_filters: Optional[Dict[str, RangeFilter]] = Field(
        None,
        description="数值范围过滤器。支持 gte, gt, lte, lt 操作符",
        json_schema_extra={
            "examples": [
                {
                    "price": {"gte": 50, "lte": 200},
                    "days_since_last_update": {"lte": 30}
                }
            ]
        }
    )
    
    # 排序
    sort_by: Optional[str] = Field(None, description="排序字段名（如 'min_price', 'max_price', 'title'）")
    sort_order: Optional[str] = Field("desc", description="排序方向: 'asc'（升序）或 'desc'（降序）")
    
    # 分面搜索 - 简化接口
    facets: Optional[List[Union[str, FacetConfig]]] = Field(
        None,
        description="分面配置。可以是字段名列表（使用默认配置）或详细的分面配置对象。支持 specifications 分面：\"specifications\"（所有name）或 \"specifications.color\"（指定name）",
        json_schema_extra={
            "examples": [
                # 简单模式：只指定字段名，使用默认配置
                ["category1_name", "category2_name", "specifications"],
                # 指定specifications的某个name
                ["specifications.颜色", "specifications.尺寸"],
                # 高级模式：详细配置
                [
                    {"field": "category1_name", "size": 15},
                    {
                        "field": "min_price",
                        "type": "range",
                        "ranges": [
                            {"key": "0-50", "to": 50},
                            {"key": "50-100", "from": 50, "to": 100}
                        ]
                    },
                    "specifications"  # 所有specifications name的分面
                ]
            ]
        }
    )
    
    # 高级选项
    min_score: Optional[float] = Field(None, ge=0, description="最小相关性分数阈值")
    highlight: bool = Field(False, description="是否高亮搜索关键词（暂不实现）")
    debug: bool = Field(False, description="是否返回调试信息")
    
    # SKU筛选参数
    sku_filter_dimension: Optional[List[str]] = Field(
        None,
        description=(
            "子SKU筛选维度（店铺配置），为字符串列表。"
            "指定后，每个SPU下的SKU将按这些维度的组合进行分组，每个维度组合只保留一个SKU返回。"
            "例如：['color'] 表示按颜色分组，每种颜色选一款；['color', 'size'] 表示按颜色+尺码组合分组。"
            "支持的值：'option1'、'option2'、'option3' 或选项名称（如 'color'、'size'，将通过 option1_name/2_name/3_name 匹配）。"
        )
    )
    
    # 个性化参数（预留）
    user_id: Optional[str] = Field(None, description="用户ID，用于个性化搜索和推荐")
    session_id: Optional[str] = Field(None, description="会话ID，用于搜索分析")
class ImageSearchRequest(BaseModel):
    """图片搜索请求模型"""
    image_url: str = Field(..., description="查询图片的 URL")
    size: int = Field(10, ge=1, le=100, description="返回结果数量")
    filters: Optional[Dict[str, Union[str, int, bool, List[Union[str, int]]]]] = None
    range_filters: Optional[Dict[str, RangeFilter]] = None
class SearchSuggestRequest(BaseModel):
    """搜索建议请求模型（框架，暂不实现）"""
    query: str = Field(..., min_length=1, description="搜索查询字符串")
    size: int = Field(5, ge=1, le=20, description="返回建议数量")
    types: List[Literal["query", "product", "category", "brand"]] = Field(
        ["query"],
        description="建议类型：query（查询建议）, product（商品建议）, category（类目建议）, brand（品牌建议）"
    )
class FacetValue(BaseModel):
    """分面值"""
    value: Union[str, int, float] = Field(..., description="分面值")
    label: Optional[str] = Field(None, description="显示标签（如果与 value 不同）")
    count: int = Field(..., description="匹配的文档数量")
    selected: bool = Field(False, description="是否已选中（当前过滤器中）")
class FacetResult(BaseModel):
    """分面结果（标准化格式）"""
    field: str = Field(..., description="字段名")
    label: str = Field(..., description="分面显示名称")
    type: Literal["terms", "range"] = Field(..., description="分面类型")
    values: List[FacetValue] = Field(..., description="分面值列表")
    total_count: Optional[int] = Field(None, description="该字段的总文档数")
class SkuResult(BaseModel):
    """SKU 结果"""
    sku_id: str = Field(..., description="SKU ID")
    # 与 ES nested skus 结构对齐
    price: Optional[float] = Field(None, description="价格")
    compare_at_price: Optional[float] = Field(None, description="原价")
    sku_code: Optional[str] = Field(None, description="SKU编码")
    stock: int = Field(0, description="库存数量")
    weight: Optional[float] = Field(None, description="重量")
    weight_unit: Optional[str] = Field(None, description="重量单位")
    option1_value: Optional[str] = Field(None, description="选项1取值（如颜色）")
    option2_value: Optional[str] = Field(None, description="选项2取值（如尺码）")
    option3_value: Optional[str] = Field(None, description="选项3取值")
    image_src: Optional[str] = Field(None, description="SKU图片地址")
class SpuResult(BaseModel):
    """SPU 搜索结果"""
    spu_id: str = Field(..., description="SPU ID")
    title: Optional[str] = Field(None, description="商品标题")
    brief: Optional[str] = Field(None, description="商品短描述")
    handle: Optional[str] = Field(None, description="商品handle")
    description: Optional[str] = Field(None, description="商品描述")
    vendor: Optional[str] = Field(None, description="供应商/品牌")
    category: Optional[str] = Field(None, description="类目（兼容字段，等同于category_name）")
    category_path: Optional[str] = Field(None, description="类目路径（多级，用于面包屑）")
    category_name: Optional[str] = Field(None, description="类目名称（展示用）")
    category_id: Optional[str] = Field(None, description="类目ID")
    category_level: Optional[int] = Field(None, description="类目层级")
    category1_name: Optional[str] = Field(None, description="一级类目名称")
    category2_name: Optional[str] = Field(None, description="二级类目名称")
    category3_name: Optional[str] = Field(None, description="三级类目名称")
    tags: Optional[List[str]] = Field(None, description="标签列表")
    price: Optional[float] = Field(None, description="价格（min_price）")
    compare_at_price: Optional[float] = Field(None, description="原价")
    currency: str = Field("USD", description="货币单位")
    image_url: Optional[str] = Field(None, description="主图URL")
    in_stock: bool = Field(True, description="是否有库存")
    # SKU 扁平化信息
    sku_prices: Optional[List[float]] = Field(None, description="所有SKU价格列表")
    sku_weights: Optional[List[int]] = Field(None, description="所有SKU重量列表")
    sku_weight_units: Optional[List[str]] = Field(None, description="所有SKU重量单位列表")
    total_inventory: Optional[int] = Field(None, description="总库存")
    option1_name: Optional[str] = Field(None, description="选项1名称（如颜色）")
    option2_name: Optional[str] = Field(None, description="选项2名称（如尺码）")
    option3_name: Optional[str] = Field(None, description="选项3名称")
    specifications: Optional[List[Dict[str, Any]]] = Field(
        None,
        description="规格列表（与 ES specifications 字段对应）"
    )
    skus: List[SkuResult] = Field(default_factory=list, description="SKU列表")
    relevance_score: float = Field(..., ge=0.0, description="相关性分数（ES原始分数）")
class SearchResponse(BaseModel):
    """搜索响应模型（外部友好格式）"""
    
    # 核心结果
    results: List[SpuResult] = Field(..., description="搜索结果列表")
    total: int = Field(..., description="匹配的总文档数")
    max_score: float = Field(..., description="最高相关性分数")
    
    # 分面搜索结果（标准化格式）
    facets: Optional[List[FacetResult]] = Field(
        None,
        description="分面统计结果（标准化格式）"
    )
    
    # 查询信息
    query_info: Dict[str, Any] = Field(
        default_factory=dict,
        description="查询处理信息（原始查询、改写、语言检测、翻译等）"
    )
    
    # 推荐与建议
    suggestions: List[str] = Field(default_factory=list, description="搜索建议")
    related_searches: List[str] = Field(default_factory=list, description="相关搜索")
    
    # 性能指标
    took_ms: int = Field(..., description="搜索总耗时（毫秒）")
    performance_info: Optional[Dict[str, Any]] = Field(None, description="详细性能信息")
    
    # 调试信息
    debug_info: Optional[Dict[str, Any]] = Field(None, description="调试信息（仅当 debug=True）")
class SearchSuggestResponse(BaseModel):
    """搜索建议响应模型（框架，暂不实现）"""
    query: str = Field(..., description="原始查询")
    suggestions: List[Dict[str, Any]] = Field(..., description="建议列表")
    took_ms: int = Field(..., description="耗时（毫秒）")
class DocumentResponse(BaseModel):
    """Single document response model."""
    id: str = Field(..., description="Document ID")
    source: Dict[str, Any] = Field(..., description="Document source")
class HealthResponse(BaseModel):
    """Health check response model."""
    status: str = Field(..., description="Service status")
    elasticsearch: str = Field(..., description="Elasticsearch status")
class ErrorResponse(BaseModel):
    """Error response model."""
    error: str = Field(..., description="Error message")
    detail: Optional[str] = Field(None, description="Detailed error information")