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"""
Request and response models for the API.
"""
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from pydantic import BaseModel, Field, field_validator
from typing import List, Dict, Any, Optional, Union, Literal
class RangeFilter(BaseModel):
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"""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")
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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},
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{"lt": 50},
{"gte": "2023-01-01T00:00:00Z"}
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]
}
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")
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multi_select: bool = Field(
True,
description="是否支持多选(disjunctive faceting)。启用后,选中该分面的过滤器时,仍会显示其他可选项"
)
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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": [
{
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"field": "category.keyword",
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"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}
]
}
]
}
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class SearchRequest(BaseModel):
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"""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)")
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size: int = Field(10, ge=1, le=1000, description="返回结果数量")
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from_: int = Field(0, ge=0, alias="from", description="分页偏移量")
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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"
)
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# 过滤器 - 精确匹配和多值匹配
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filters: Optional[Dict[str, Union[str, int, bool, List[Union[str, int]], Dict[str, Any], List[Dict[str, Any]]]]] = Field(
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None,
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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\"}, ...]",
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json_schema_extra={
"examples": [
{
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"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"}
]
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}
]
}
)
# 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
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sort_by: Optional[str] = Field(None, description="排序字段名。支持:'price'(价格,自动根据sort_order选择min_price或max_price)、'sales'(销量)、'create_time'(创建时间)、'update_time'(更新时间)")
sort_order: Optional[str] = Field("desc", description="排序方向: 'asc'(升序)或 'desc'(降序)。注意:price+asc=价格从低到高,price+desc=价格从高到低")
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# 分面搜索
facets: Optional[List[FacetConfig]] = Field(
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None,
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description="分面配置对象列表。支持 specifications 分面:field=\"specifications\"(所有规格名称)或 field=\"specifications.color\"(指定规格名称)",
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json_schema_extra={
"examples": [
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[
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{"field": "category1_name", "size": 15, "type": "terms"},
{"field": "category2_name", "size": 10, "type": "terms"},
{"field": "specifications.color", "size": 20, "type": "terms"},
{"field": "specifications.size", "size": 15, "type": "terms"}
],
[
{"field": "category1_name", "size": 15, "type": "terms"},
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{
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"field": "min_price",
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"type": "range",
"ranges": [
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{"key": "50-100", "from": 50, "to": 100},
{"key": "100-200", "from": 100, "to": 200},
{"key": "200+", "from": 200}
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]
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},
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{"field": "specifications", "size": 10, "type": "terms"}
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]
]
}
)
# 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")
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# SKU筛选参数
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sku_filter_dimension: Optional[List[str]] = Field(
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None,
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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)。"
)
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)
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# 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")
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class ImageSearchRequest(BaseModel):
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"""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")
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class SkuResult(BaseModel):
"""SKU 结果"""
sku_id: str = Field(..., description="SKU ID")
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# 与 ES nested skus 结构对齐
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price: Optional[float] = Field(None, description="价格")
compare_at_price: Optional[float] = Field(None, description="原价")
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sku_code: Optional[str] = Field(None, description="SKU编码")
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stock: int = Field(0, description="库存数量")
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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")
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class SpuResult(BaseModel):
"""SPU 搜索结果"""
spu_id: str = Field(..., description="SPU ID")
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title: Optional[str] = Field(None, description="商品标题")
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brief: Optional[str] = Field(None, description="商品短描述")
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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")
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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")
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tags: Optional[List[str]] = Field(None, description="标签列表")
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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")
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# 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)"
)
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skus: List[SkuResult] = Field(default_factory=list, description="SKU列表")
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relevance_score: float = Field(..., ge=0.0, description="相关性分数(ES原始分数)")
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class SearchResponse(BaseModel):
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"""搜索响应模型(外部友好格式)"""
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# 核心结果
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results: List[SpuResult] = Field(..., description="搜索结果列表")
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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)"
)
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# 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")
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# 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)")
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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")
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class ErrorResponse(BaseModel):
"""Error response model."""
error: str = Field(..., description="Error message")
detail: Optional[str] = Field(None, description="Detailed error information")
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