Neo
8638ecad2a
1. apps/backend/app/ai/prompts/app2a_finance_area_prompt.py (新建):
- payload: 业态说明 + 区域占比 + 对比口径 + 核心 KPI + 优惠构成
+ 助教成本 + 区域级单位经济 + 按星期聚合 + 日粒度异常 + 行业基线
- 5 个区域级辅助函数：_fetch_area_daily_series / _build_area_unit_economics
/ _aggregate_by_weekday_area / _detect_anomaly_days_area / _fetch_area_share
- AREA_INDUSTRY_TRAITS 字典（7 业态 trait + peer 描述）
- 复用 app2_finance_prompt 的 _build_coach_kpi / _build_discount_kpi 等公共函数
2. config.py: AIConfig 增加 app_id_2a_finance_area + DASHSCOPE_APP_ID_2A_FINANCE_AREA
3. schemas.py: CacheTypeEnum 增加 APP2A_FINANCE_AREA
4. dispatcher.py:
- APP2A_AREA_OPTIONS 常量（8 业态 · area != 'all'）
- _handle_dws_completed 72 循环拆分：
area='all' 走 app2_finance · 其他 8 业态走 app2a_finance_area
- run_single_app 新增 elif 'app2a_finance_area' 分支（拒绝 area='all'）
5. admin_ai.py: _SUPPORTED_APP_TYPES 加 'app2a_finance_area'
6. prompts/__init__.py: 导出 build_app2a_area_prompt
7. .env: 追加 DASHSCOPE_APP_ID_2A_FINANCE_AREA 百炼 APP ID
实测：7 项集成单测全通过（config/cache_type/router/prompts/dispatcher 常量/
4 业态 prompt 构建/拒绝 area=all）· 端到端实调 vip 组合返回 12 条高质量洞察
严格遵守 v1.2 system prompt 全部 7 项硬约束（H1-H7）。
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
159 lines
3.1 KiB
Python
159 lines
3.1 KiB
Python
"""AI 集成层 Pydantic 模型定义。
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覆盖请求/响应体、缓存类型枚举、线索模型、各应用结果模型。
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"""
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from __future__ import annotations
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import enum
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from pydantic import BaseModel, Field
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# ── 请求/响应 ──
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class ChatStreamRequest(BaseModel):
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"""SSE 流式对话请求体"""
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message: str
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source_page: str | None = None
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page_context: dict | None = None
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screen_content: str | None = None
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class SSEEvent(BaseModel):
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"""SSE 事件"""
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type: str # chunk / done / error
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content: str | None = None
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conversation_id: int | None = None
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tokens_used: int | None = None
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message: str | None = None
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# ── 缓存类型枚举 ──
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class CacheTypeEnum(str, enum.Enum):
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APP2_FINANCE = "app2_finance"
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APP2A_FINANCE_AREA = "app2a_finance_area" # 2026-04-23 新增：区域财务洞察（64 组合）
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APP3_CLUE = "app3_clue"
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APP4_ANALYSIS = "app4_analysis"
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APP5_TACTICS = "app5_tactics"
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APP6_NOTE_ANALYSIS = "app6_note_analysis"
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APP7_CUSTOMER_ANALYSIS = "app7_customer_analysis"
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APP8_CLUE_CONSOLIDATED = "app8_clue_consolidated"
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# ── 线索相关 ──
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class App3CategoryEnum(str, enum.Enum):
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"""App3 线索分类（3 个枚举值）"""
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CUSTOMER_BASIC = "客户基础"
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CONSUMPTION_HABIT = "消费习惯"
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PLAY_PREFERENCE = "玩法偏好"
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class App6CategoryEnum(str, enum.Enum):
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"""App6/8 线索分类（6 个枚举值）"""
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CUSTOMER_BASIC = "客户基础"
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CONSUMPTION_HABIT = "消费习惯"
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PLAY_PREFERENCE = "玩法偏好"
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PROMO_PREFERENCE = "促销偏好"
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SOCIAL_RELATION = "社交关系"
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IMPORTANT_FEEDBACK = "重要反馈"
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class ClueItem(BaseModel):
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"""单条线索（App3/App6 共用）"""
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category: str
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summary: str
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detail: str
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emoji: str
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class ConsolidatedClueItem(BaseModel):
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"""整合后线索（App8，含 providers）"""
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category: str
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summary: str
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detail: str
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emoji: str
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providers: str
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# ── 各应用结果模型 ──
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class App2InsightItem(BaseModel):
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"""App2 财务洞察单条"""
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seq: int
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title: str
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body: str
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class App2Result(BaseModel):
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"""App2 财务洞察结果"""
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insights: list[App2InsightItem]
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class App3Result(BaseModel):
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"""App3 客户数据维客线索结果"""
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clues: list[ClueItem]
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class App4Result(BaseModel):
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"""App4 关系分析结果"""
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task_description: str
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action_suggestions: list[str]
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one_line_summary: str
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class App5TacticsItem(BaseModel):
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"""App5 话术单条"""
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scenario: str
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script: str
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class App5Result(BaseModel):
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"""App5 话术参考结果"""
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tactics: list[App5TacticsItem]
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class App6Result(BaseModel):
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"""App6 备注分析结果"""
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score: int = Field(ge=1, le=10)
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clues: list[ClueItem]
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class App7StrategyItem(BaseModel):
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"""App7 客户分析策略单条"""
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title: str
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content: str
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class App7Result(BaseModel):
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"""App7 客户分析结果"""
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strategies: list[App7StrategyItem]
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summary: str
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class App8Result(BaseModel):
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"""App8 维客线索整理结果"""
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clues: list[ConsolidatedClueItem]
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