Files
Neo-ZQYY/apps/backend/app/ai/prompts/app7_customer_prompt.py
Neo 2dfc926f96 feat(ai): W1-AI-CLOSURE 超级 Sprint — 9 APP 全链路收口 + chat 上下文真激活
Phase 2.3 chat 上下文捕获链路从未真正激活到完整工作:
- 14 处 ai-float-button 补 sourcePage,chat.ts 三分支同步设 pageFilters.contextId
- 后端 page_context 4 层 BUG 修(列名错位 + RLS site_id 未重设)
- xcx_chat filters.pop 破坏 body.page_context 引用 — dict() 浅拷贝隔离
- chat 流式 markdown 实时解析(表格/标题/列表/加粗 + KPI 富卡)
- reference_card KPI 富卡接入 SSE 路径,db 真写入
- 维客线索 source 显示规则:AI 来源用机器人 icon 替代长文字

数据库:
- public.member_retention_clue 加 emoji + runtime_mode + sandbox_instance_id
- biz.ai_run_logs 加 assistant_id + 复合索引
- chk_ai_cache_type CHECK 约束 8 类应用名
- cache_type / app_type 命名统一(app6_note / app7_customer / app8_consolidation)
- 历史 emoji 抽取脚本 44/44 成功

后端 silent failure 修:
- cleanup_service WHERE app_type → cache_type(90 天清理 + 20K 上限重新生效)
- _build_ai_insight 字段错位修复(app4 → app7 + 字段对齐 prompt schema)
- task_manager talkingPoints 改 app5_tactics + tactics 字段
- task_manager aiSuggestion 改取 one_line_summary
- cache_service.CACHE_EXPIRY_DAYS 加 app2a_finance_area
- WS /ws/ai-cache 加 token + JWT + site_id 校验(P0 信息泄露漏洞)
- internal_ai token 改 hmac.compare_digest

工具/文档:
- main.py 加 RotatingFileHandler logs/backend.log + uvicorn /health 过滤
- 新建 utils/clue_category.py(VI 6 类配色 + emoji fallback + source 显示规则)
- 新建 utils/markdown.ts(轻量 md 转 rich-text 解析 + streaming 容错)
- audit + 数据库变更说明 + backlog §七 #14 收口 + #15-#38 残余子任务
- backlog 追加 §十一 App1 参数/MCP/沙箱审计 + §十二 百炼/SQL MCP 主任务线

实地 MCP 走查:14 入口数据层 + 5 代表入口 sourcePage 注入 + customer-detail 全模块 + chat md 渲染 + reference_card 富卡 都已验证。9 项预先 BUG/UX 登记 §七 #29-#38 后续修复。

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-06 16:39:07 +08:00

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"""应用 7 客户分析 Prompt 拼装。
消费链 App8 完成后串行触发,生成客户全量分析与运营策略。
- 数据源fetch_member_consumption_data + fetch_member_notes
- 备注内容标注【来源XXX请甄别信息真实性】
- 输出字段strategies 数组 + summary
- system prompt 在百炼控制台配置
返回:单个 prompt 字符串。
"""
from __future__ import annotations
import asyncio
import json
import logging
from typing import Any
from app.ai.cache_service import AICacheService
from app.ai.data_fetchers import fetch_member_consumption_data, fetch_member_notes
from app.ai.schemas import CacheTypeEnum
from app.services.runtime_context import as_runtime_business_now_str
logger = logging.getLogger(__name__)
_MAX_PROMPT_LEN = 5000
async def build_prompt(
context: dict,
cache_svc: AICacheService | None = None,
) -> str:
"""构建 App7 prompt 字符串。
Args:
context: site_id, member_id
Returns:
JSON 序列化后的 prompt 字符串
"""
site_id = context["site_id"]
member_id = context["member_id"]
results = await asyncio.gather(
fetch_member_consumption_data(site_id, member_id),
fetch_member_notes(site_id, member_id),
return_exceptions=True,
)
warnings: list[str] = []
if isinstance(results[0], Exception):
member_data = _default_member_data()
warnings.append("消费数据获取失败")
logger.warning("App7 消费数据获取失败: %s", results[0])
else:
member_data = results[0]
notes_raw = results[1] if not isinstance(results[1], Exception) else []
if isinstance(results[1], Exception):
warnings.append("备注获取失败")
logger.warning("App7 备注获取失败: %s", results[1])
# 主观信息标注来源
if notes_raw:
annotated = []
for note in notes_raw:
recorded_by = note.get("recorded_by", "未知")
n = dict(note)
n["content"] = (
f"{note.get('content', '')}"
f"【来源:{recorded_by},请甄别信息真实性】"
)
annotated.append(n)
subjective_notes: Any = annotated
else:
subjective_notes = "该客户暂无主观备注信息"
payload: dict[str, Any] = {
"current_time": as_runtime_business_now_str(site_id, fmt="%Y-%m-%d %H:%M"),
"member_id": member_id,
"member_nickname": member_data.get("member_nickname", ""),
"objective_data": {
"consumption_records": member_data.get("consumption_records", []) or "该客户暂无消费记录",
"member_cards": member_data.get("member_cards", []),
"card_balance_total": member_data.get("card_balance_total", 0),
"stored_value_balance_total": member_data.get("stored_value_balance_total", 0),
"expected_visit_date": member_data.get("expected_visit_date"),
"days_since_last_visit": member_data.get("days_since_last_visit"),
},
"subjective_data": {
"notes": subjective_notes,
},
"reference": _build_reference(site_id, member_id, cache_svc),
}
if warnings:
payload["_data_warnings"] = warnings
return _truncate_payload(payload)
def _default_member_data() -> dict:
return {
"member_nickname": "",
"consumption_records": [],
"member_cards": [],
"card_balance_total": 0,
"stored_value_balance_total": 0,
"expected_visit_date": None,
"days_since_last_visit": None,
}
def _build_reference(
site_id: int,
member_id: int,
cache_svc: AICacheService | None,
) -> dict:
"""组装 App8 最新 + 最近 2 条历史。"""
if cache_svc is None:
return {}
ref: dict = {}
target_id = str(member_id)
latest = cache_svc.get_latest(
CacheTypeEnum.APP8_CONSOLIDATION.value, site_id, target_id,
)
if latest:
ref["app8_latest"] = {
"result_json": latest.get("result_json"),
"generated_at": latest.get("created_at"),
}
history = cache_svc.get_history(
CacheTypeEnum.APP8_CONSOLIDATION.value, site_id, target_id, limit=2,
)
if history:
ref["app8_history"] = [
{"result_json": h.get("result_json"), "generated_at": h.get("created_at")}
for h in history
]
return ref
def _truncate_payload(payload: dict) -> str:
"""按优先级截断 consumption_records → notes。"""
text = json.dumps(payload, ensure_ascii=False, default=str)
if len(text) <= _MAX_PROMPT_LEN:
return text
records = payload["objective_data"].get("consumption_records")
if isinstance(records, list) and len(records) > 5:
payload["objective_data"]["consumption_records"] = records[:5]
payload["objective_data"]["_truncated"] = f"消费记录已截断,原始 {len(records)}"
text = json.dumps(payload, ensure_ascii=False, default=str)
if len(text) > _MAX_PROMPT_LEN:
n = payload["subjective_data"].get("notes")
if isinstance(n, list) and len(n) > 10:
payload["subjective_data"]["notes"] = n[:10]
payload["subjective_data"]["_truncated_notes"] = f"备注已截断,原始 {len(n)}"
text = json.dumps(payload, ensure_ascii=False, default=str)
return text