models.py
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"""
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": "categoryName_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")
# 过滤器 - 精确匹配和多值匹配
filters: Optional[Dict[str, Union[str, int, bool, List[Union[str, int]]]]] = 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）",
json_schema_extra={
"examples": [
{
"categoryName_keyword": ["玩具", "益智玩具"],
"brandName_keyword": "乐高",
"in_stock": True
}
]
}
)
# 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 'price', 'create_time'）")
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",
json_schema_extra={
"examples": [
# 简单模式：只指定字段名，使用默认配置
["categoryName_keyword", "brandName_keyword"],
# 高级模式：详细配置
[
{"field": "categoryName_keyword", "size": 15},
{
"field": "price",
"type": "range",
"ranges": [
{"key": "0-50", "to": 50},
{"key": "50-100", "from": 50, "to": 100}
]
}
]
]
}
)
# 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")
# 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 VariantResult(BaseModel):
"""商品变体结果"""
variant_id: str = Field(..., description="变体ID")
title: Optional[str] = Field(None, description="变体标题")
price: Optional[float] = Field(None, description="价格")
compare_at_price: Optional[float] = Field(None, description="原价")
sku: Optional[str] = Field(None, description="SKU编码")
stock: int = Field(0, description="库存数量")
options: Optional[Dict[str, Any]] = Field(None, description="选项（颜色、尺寸等）")
class ProductResult(BaseModel):
"""商品搜索结果"""
product_id: str = Field(..., description="商品ID")
title: Optional[str] = Field(None, description="商品标题")
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")
product_type: Optional[str] = Field(None, description="商品类型")
tags: Optional[str] = Field(None, description="标签")
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")
variants: List[VariantResult] = Field(default_factory=list, description="变体列表")
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[ProductResult] = 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")
customer_id: str = Field(..., description="Customer configuration ID")
class ErrorResponse(BaseModel):
"""Error response model."""
error: str = Field(..., description="Error message")
detail: Optional[str] = Field(None, description="Detailed error information")