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Neo
2026-03-15 10:15:02 +08:00
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"""应用 2 财务洞察 Prompt 模板。
构建包含当期和上期收入结构的完整 Prompt供百炼 API 生成财务洞察。
收入字段映射(严格遵守 items_sum 口径):
- table_fee = table_charge_money台费
- assistant_pd = assistant_pd_money陪打费
- assistant_cx = assistant_cx_money超休费
- goods = goods_money商品收入
- recharge = 充值 pay_amount settle_type=5充值收入
禁止使用 consume_money统一使用
items_sum = table_charge_money + goods_money + assistant_pd_money
+ assistant_cx_money + electricity_money
"""
from __future__ import annotations
import json
def build_prompt(context: dict) -> list[dict]:
"""构建 App2 财务洞察 Prompt 消息列表。
Args:
context: 包含以下字段:
- site_id: int门店 ID
- time_dimension: str时间维度编码
- current_data: dict当期数据
- previous_data: dict上期数据
Returns:
messages 列表system + user供 BailianClient.chat_json 调用
"""
site_id = context.get("site_id", 0)
time_dimension = context.get("time_dimension", "")
current_data = context.get("current_data", {})
previous_data = context.get("previous_data", {})
system_content = _build_system_content(
site_id=site_id,
time_dimension=time_dimension,
current_data=current_data,
previous_data=previous_data,
)
user_content = (
f"请根据以上数据,为门店 {site_id} 生成 {_dimension_label(time_dimension)} 的财务洞察分析。"
"以 JSON 格式返回,包含 insights 数组,每项含 seq序号、title标题、body正文"
)
return [
{"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
{"role": "user", "content": user_content},
]
def _build_system_content(
*,
site_id: int,
time_dimension: str,
current_data: dict,
previous_data: dict,
) -> dict:
"""构建 system prompt JSON 结构。"""
return {
"task": (
"你是台球门店的财务分析 AI 助手。"
"根据提供的当期和上期经营数据,生成结构化的财务洞察。"
"分析维度包括:收入结构变化、各收入项占比、环比趋势、异常波动。"
"输出 JSON 格式:{\"insights\": [{\"seq\": 1, \"title\": \"...\", \"body\": \"...\"}]}"
),
"data": {
"site_id": site_id,
"time_dimension": time_dimension,
"time_dimension_label": _dimension_label(time_dimension),
"current_period": _build_period_data(current_data),
"previous_period": _build_period_data(previous_data),
},
"reference": {
"field_mapping": {
"items_sum": (
"table_charge_money + goods_money + assistant_pd_money"
" + assistant_cx_money + electricity_money"
),
"table_fee": "table_charge_money台费收入",
"assistant_pd": "assistant_pd_money陪打费",
"assistant_cx": "assistant_cx_money超休费",
"goods": "goods_money商品收入",
"recharge": "充值 pay_amountsettle_type=5充值收入",
"electricity": "electricity_money电费当前未启用全为 0",
},
"rules": [
"统一使用 items_sum 口径计算营收总额",
"助教费用必须拆分为 assistant_pd_money陪打和 assistant_cx_money超休",
"支付渠道恒等式balance_amount = recharge_card_amount + gift_card_amount",
"金额单位CNY保留两位小数",
],
},
}
def _build_period_data(data: dict) -> dict:
"""构建单期数据结构,确保字段名遵守 items_sum 口径。"""
return {
# 收入结构items_sum 口径)
"table_charge_money": data.get("table_charge_money", 0),
"goods_money": data.get("goods_money", 0),
"assistant_pd_money": data.get("assistant_pd_money", 0),
"assistant_cx_money": data.get("assistant_cx_money", 0),
"electricity_money": data.get("electricity_money", 0),
# 充值收入
"recharge_income": data.get("recharge_income", 0),
# 储值资产
"balance_pay": data.get("balance_pay", 0),
"recharge_card_pay": data.get("recharge_card_pay", 0),
"gift_card_pay": data.get("gift_card_pay", 0),
# 费用汇总
"discount_amount": data.get("discount_amount", 0),
"adjust_amount": data.get("adjust_amount", 0),
# 平台结算
"platform_settlement_amount": data.get("platform_settlement_amount", 0),
"groupbuy_pay_amount": data.get("groupbuy_pay_amount", 0),
# 汇总
"order_count": data.get("order_count", 0),
"member_count": data.get("member_count", 0),
}
# 时间维度编码 → 中文标签
_DIMENSION_LABELS: dict[str, str] = {
"this_month": "本月",
"last_month": "上月",
"this_week": "本周",
"last_week": "上周",
"last_3_months": "近三个月",
"this_quarter": "本季度",
"last_quarter": "上季度",
"last_6_months": "近六个月",
}
def _dimension_label(dimension: str) -> str:
"""将时间维度编码转为中文标签。"""
return _DIMENSION_LABELS.get(dimension, dimension)

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"""应用 8维客线索整理 Prompt 模板。
接收 App3消费分析和 App6备注分析的全部线索
整合去重后输出统一维客线索。
分类标签限定 6 个枚举值(与 member_retention_clue CHECK 约束一致):
客户基础、消费习惯、玩法偏好、促销偏好、社交关系、重要反馈。
合并规则:
- 相似线索合并providers 以逗号分隔
- 其余线索原文返回
- 最小改动原则
"""
from __future__ import annotations
import json
def build_prompt(context: dict) -> list[dict]:
"""构建 App8 维客线索整理 Prompt。
Args:
context: 包含以下字段:
- site_id: int
- member_id: int
- app3_clues: list[dict] — App3 产出的线索列表
- app6_clues: list[dict] — App6 产出的线索列表
- app3_generated_at: str | None — App3 线索生成时间
- app6_generated_at: str | None — App6 线索生成时间
Returns:
消息列表 [{"role": "system", ...}, {"role": "user", ...}]
"""
member_id = context["member_id"]
app3_clues = context.get("app3_clues", [])
app6_clues = context.get("app6_clues", [])
app3_generated_at = context.get("app3_generated_at")
app6_generated_at = context.get("app6_generated_at")
system_content = {
"task": "整合去重来自消费分析和备注分析的维客线索,输出统一线索列表。",
"app_id": "app8_consolidation",
"rules": {
"category_enum": [
"客户基础", "消费习惯", "玩法偏好",
"促销偏好", "社交关系", "重要反馈",
],
"merge_strategy": (
"相似线索合并为一条providers 以逗号分隔(如 '系统,张三'"
"不相似的线索原文保留,不做修改。最小改动原则。"
),
"output_format": {
"clues": [
{
"category": "枚举值6 选 1",
"summary": "一句话摘要",
"detail": "详细说明",
"emoji": "表情符号",
"providers": "提供者(逗号分隔)",
}
]
},
},
"input": {
"app3_clues": {
"source": "消费数据分析App3",
"generated_at": app3_generated_at,
"clues": app3_clues,
},
"app6_clues": {
"source": "备注分析App6",
"generated_at": app6_generated_at,
"clues": app6_clues,
},
},
}
user_content = (
f"请整合会员 {member_id} 的维客线索。\n"
"输入包含两个来源的线索App3消费数据分析和 App6备注分析\n"
"规则:\n"
"1. 相似线索合并为一条providers 字段以逗号分隔多个提供者\n"
"2. 不相似的线索原文保留\n"
"3. category 必须是:客户基础、消费习惯、玩法偏好、促销偏好、社交关系、重要反馈 之一\n"
"4. 每条线索包含 category、summary、detail、emoji、providers 五个字段\n"
"5. 最小改动原则,尽量保留原始表述"
)
return [
{"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
{"role": "user", "content": user_content},
]