Files
Neo-ZQYY/apps/backend/app/ai/prompts/app7_customer_prompt.py
Neo caf179a5da feat: 2026-04-15~05-02 累积变更基线 — AI 重构 + Runtime Context + DWS 修复
涵盖(每条对应已存的审计记录):
- AI 模块拆分:apps/backend/app/ai/apps -> prompts/(8 个 APP + app2a 派生)
  audit: 2026-04-20__ai-module-complete.md
- admin-web AI 管理套件:AIDashboard / AIOperations / AIRunLogs / AITriggers / TriggerManager
  audit: 2026-04-21__admin-web-ai-management-suite.md
- App2 财务洞察 prompt v3 -> v5.1 + 小程序 AI 接入(chat / board-finance)
  audit: 2026-04-22__app2_prompt_v5_1_and_miniprogram_ai_insight.md
- App2 prewarm 全过滤器 + AI 触发器 cron reschedule
  audit: 2026-04-21__app2-finance-prewarm-all-filters.md
  migration: 20260420_ai_trigger_jobs_and_app2_prewarm.sql / 20260421_app2_prewarm_cron_reschedule.sql
- AppType 联合类型对齐 + adminAiAppTypes.test.ts
  audit: 2026-04-30__admin_web_ai_app_type_alignment.md
- DashScope tokens_used 提取修复
  audit: 2026-04-30__backend_dashscope_tokens_used_extraction.md
- App3 线索完整详情 prompt
  audit: 2026-05-01__backend_app3_full_detail_prompt.md
- Runtime Context 沙箱(5-1~5-2 主线):
  - 后端 schema/service + admin_runtime_context / xcx_runtime_clock 两个 router
  - admin-web RuntimeContext.tsx + miniprogram runtime-clock.ts
  - migration: 20260501__runtime_context_sandbox.sql
  - tools/db/verify_admin_web_sandbox.py + verify_sandbox_end_to_end.py
  - database/changes: 7 份 sandbox_* 验证报告
- 飞球 DWS 修复:finance_area_daily 区域汇总 + task_engine 调整
  + RLS 视图业务日上界(migration 20260502 + scripts/ops/gen_rls_business_date_migration.py)

合规:
- .gitignore 启用 tmp/ 排除
- 不入仓:apps/etl/connectors/feiqiu/.env(API_TOKEN secret,本地修改保留)

待验证清单:
- docs/audit/changes/2026-05-04__cumulative_baseline_pending_verification.md
  每个主题的功能完整性 / 上线验证几乎都未收口,按优先级 P0~P3 逐一处理
2026-05-04 02:30:19 +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_CLUE_CONSOLIDATED.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_CLUE_CONSOLIDATED.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