204 lines
8.7 KiB
Python
204 lines
8.7 KiB
Python
from pydantic import BaseModel, Field
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from typing import Optional
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class CitationResult(BaseModel):
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"""LLM查询品牌引用结果"""
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cited: bool = Field(..., description="是否检测到品牌引用")
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position: Optional[int] = Field(None, description="引用位置（1-based）")
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citation_text: Optional[str] = Field(None, description="引用文本片段")
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sentiment: str = Field(..., description="情感类型: positive/neutral/negative")
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confidence: float = Field(..., ge=0.0, le=1.0, description="置信度 0.0-1.0")
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class ScoringResult(BaseModel):
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"""评分结果"""
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mention_rate_score: float = Field(..., ge=0.0, le=100.0, description="提及率得分 0-100")
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sov_score: float = Field(..., ge=0.0, le=100.0, description="SOV得分 0-100")
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quality_score: float = Field(..., ge=0.0, le=100.0, description="引用质量得分 0-100")
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overall_score: float = Field(..., ge=0.0, le=100.0, description="综合评分 0-100")
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class Config:
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json_schema_extra = {
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"example": {
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"mention_rate_score": 66.67,
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"sov_score": 50.0,
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"quality_score": 78.5,
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"overall_score": 64.67
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}
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}
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class BrandScoreResponse(BaseModel):
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"""品牌评分响应"""
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mention_rate_score: float = Field(..., ge=0.0, le=100.0, description="提及率得分 0-100")
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sov_score: float = Field(..., ge=0.0, le=100.0, description="SOV得分 0-100")
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quality_score: float = Field(..., ge=0.0, le=100.0, description="引用质量得分 0-100")
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overall_score: float = Field(..., ge=0.0, le=100.0, description="综合评分 0-100")
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# ============================================================
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# V2 评分Schema
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# ============================================================
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class DimensionScoreResponse(BaseModel):
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"""单个维度评分响应"""
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name: str = Field(..., description="维度名称")
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score: float = Field(..., ge=0.0, description="该维度得分")
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max_score: float = Field(..., description="该维度满分")
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percentage: float = Field(..., ge=0.0, le=100.0, description="得分率百分比")
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detail: dict = Field(default_factory=dict, description="评分细节")
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class BrandScoreV2Response(BaseModel):
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"""品牌可见性评分V2响应"""
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overall_score: float = Field(..., ge=0.0, le=100.0, description="综合评分 0-100")
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health_level: str = Field(..., description="健康等级: excellent/good/pass/danger")
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mention_rate: DimensionScoreResponse = Field(..., description="提及率维度 (25分)")
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recommendation_rank: DimensionScoreResponse = Field(..., description="推荐排名维度 (25分)")
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sentiment_score: DimensionScoreResponse = Field(..., description="情感倾向维度 (20分)")
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citation_quality: DimensionScoreResponse = Field(..., description="引用质量维度 (15分)")
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competitive_position: DimensionScoreResponse = Field(..., description="竞品对比维度 (15分)")
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# V1兼容字段（保留旧字段，方便前端平滑迁移）
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mention_rate_score: float = Field(..., description="[V1兼容] 提及率得分 0-100")
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sov_score: float = Field(..., description="[V1兼容] SOV得分 0-100")
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quality_score: float = Field(..., description="[V1兼容] 引用质量得分 0-100")
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class Config:
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json_schema_extra = {
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"example": {
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"overall_score": 68.5,
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"health_level": "good",
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"mention_rate": {
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"name": "提及率",
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"score": 20.0,
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"max_score": 25.0,
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"percentage": 80.0,
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"detail": {"mentioned_count": 8, "total_queries": 10, "rate": 0.8},
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},
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"recommendation_rank": {
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"name": "推荐排名",
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"score": 18.5,
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"max_score": 25.0,
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"percentage": 74.0,
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"detail": {"avg_position": 2.5, "valid_count": 8},
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},
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"sentiment_score": {
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"name": "情感倾向",
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"score": 15.0,
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"max_score": 20.0,
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"percentage": 75.0,
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"detail": {"positive": 5, "neutral": 3, "negative": 0},
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},
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"citation_quality": {
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"name": "引用质量",
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"score": 10.0,
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"max_score": 15.0,
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"percentage": 66.7,
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"detail": {"cited_count": 8, "avg_depth_score": 0.667},
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},
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"competitive_position": {
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"name": "竞品对比",
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"score": 5.0,
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"max_score": 15.0,
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"percentage": 33.3,
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"detail": {"brand_mentions": 8, "competitor_count": 3, "ahead_count": 1},
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},
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"mention_rate_score": 80.0,
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"sov_score": 50.0,
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"quality_score": 66.7,
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}
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}
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class BrandScoreHistoryItem(BaseModel):
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"""评分历史条目"""
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date: str = Field(..., description="日期 YYYY-MM-DD")
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mention_rate_score: float = Field(..., ge=0.0, le=100.0, description="提及率得分")
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sov_score: float = Field(..., ge=0.0, le=100.0, description="SOV得分")
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quality_score: float = Field(..., ge=0.0, le=100.0, description="引用质量得分")
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overall_score: float = Field(..., ge=0.0, le=100.0, description="综合评分")
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total_queries: int = Field(..., description="总查询次数")
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cited_count: int = Field(..., description="被引用次数")
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class BrandScoreHistoryResponse(BaseModel):
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"""品牌评分历史响应"""
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history: list[BrandScoreHistoryItem] = Field(default_factory=list)
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total: int = Field(..., description="历史记录总数")
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class PlatformStats(BaseModel):
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"""平台统计"""
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queries: int = Field(..., description="查询次数")
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citations: int = Field(..., description="引用次数")
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rate: float = Field(..., description="引用率")
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class TrendItem(BaseModel):
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"""趋势数据"""
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date: str = Field(..., description="日期")
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score: int = Field(..., description="评分/引用数")
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class SentimentBreakdown(BaseModel):
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"""情感分布"""
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positive: int = Field(0, description="正面引用数")
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neutral: int = Field(0, description="中性引用数")
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negative: int = Field(0, description="负面引用数")
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class CitationsStatsResponse(BaseModel):
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"""引用统计响应"""
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total_queries: int = Field(..., description="总查询次数")
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total_citations: int = Field(..., description="总引用次数")
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citation_rate: float = Field(..., description="引用率百分比")
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platform_breakdown: dict[str, PlatformStats] = Field(
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default_factory=dict, description="各平台统计"
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)
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sentiment_breakdown: SentimentBreakdown = Field(
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..., description="情感分布"
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)
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trend: list[TrendItem] = Field(default_factory=list, description="趋势数据")
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class DimensionCompareItem(BaseModel):
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"""单维度对比项"""
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name: str = Field(..., description="维度名称")
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score: float = Field(..., description="得分")
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max_score: float = Field(..., description="满分")
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percentage: float = Field(..., description="得分率百分比")
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trend: str = Field("stable", description="趋势: up/down/stable")
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trend_value: float = Field(0.0, description="趋势变化值")
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class CompareItem(BaseModel):
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"""对比项"""
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entity_id: str = Field(..., description="品牌或竞品ID")
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entity_name: str = Field(..., description="品牌或竞品名称")
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entity_type: str = Field(..., description="类型: brand 或 competitor")
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mention_rate_score: float = Field(..., description="提及率得分")
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sov_score: float = Field(..., description="SOV得分")
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quality_score: float = Field(..., description="引用质量得分")
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overall_score: float = Field(..., description="综合评分")
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citation_count: int = Field(0, description="引用次数")
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# V2 多维度对比
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dimensions: list[DimensionCompareItem] = Field(
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default_factory=list, description="五维度评分对比"
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)
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# 趋势
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overall_trend: str = Field("stable", description="综合评分趋势: up/down/stable")
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overall_trend_value: float = Field(0.0, description="综合评分变化值")
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class CompareResponse(BaseModel):
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"""竞品对比响应"""
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brand_id: str = Field(..., description="品牌ID")
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brand_name: str = Field(..., description="品牌名称")
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items: list[CompareItem] = Field(default_factory=list, description="对比项列表（含品牌和竞品）")
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# 雷达图数据
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radar_data: list[dict] = Field(
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default_factory=list, description="雷达图数据（各维度各品牌得分）"
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)
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