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Author SHA1 Message Date
guke 5c6840dd71 feat(compare): 比价记录 LLM token 成本落库与展示(按当时价冻结) (#133)
- comparison_record 加 llm_cost_yuan(元/float)+ llm_price_snapshot(JSON)两列
- _backfill_llm_calls 回填时按 app_config 当时单价逐模型算成本、冻结成本+快照到记录
- app_config 新增 llm_token_price 配置(per_model + default 兜底,运营在系统配置页可改)
- services/llm_cost.py:compute_llm_cost 纯函数(按 model 分桶、error/无 usage 跳过、
  脏价格当 unpriced 不抛异常以免连累 token 回填)+ get_llm_prices reader
- admin schema 暴露成本:列表项带 llm_cost_yuan,详情另带价格快照
- tests/test_llm_cost.py(10 测试);scripts/seed_mock_llm_cost.py(mock seeder)

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

---------

Co-authored-by: guke <guke@autohome.com.cn>
Reviewed-on: #133
2026-07-13 17:46:11 +08:00
18 changed files with 595 additions and 69 deletions
+33
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@@ -0,0 +1,33 @@
"""comparison_record: llm_cost_yuan + llm_price_snapshot(比价 LLM 调用成本 + 当时单价快照)
回填 llm_calls 时按「当时的价」逐模型算出本次比价 LLM 总成本(元),连同所用单价快照一起冻结到
记录上;admin 比价记录详情展示实际成本(旧记录 NULL → 前端回退估算)。见 services/llm_cost.py。
Revision ID: comparison_llm_cost
Revises: ad_ecpm_trace_id
Create Date: 2026-07-13
"""
from collections.abc import Sequence
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
from alembic import op
revision: str = "comparison_llm_cost"
down_revision: str | Sequence[str] | None = "ad_ecpm_trace_id"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
_JSONB = sa.JSON().with_variant(postgresql.JSONB(), "postgresql")
def upgrade() -> None:
# 均可空、无索引;SQLite 原生支持 ADD COLUMN,无需 batch_alter_table(同 comparison_debug_fields)。
op.add_column("comparison_record", sa.Column("llm_cost_yuan", sa.Float(), nullable=True))
op.add_column("comparison_record", sa.Column("llm_price_snapshot", _JSONB, nullable=True))
def downgrade() -> None:
op.drop_column("comparison_record", "llm_price_snapshot")
op.drop_column("comparison_record", "llm_cost_yuan")
@@ -1,32 +0,0 @@
"""allow multiple active withdraw orders per user
Revision ID: withdraw_allow_multiple_active
Revises: ad_ecpm_trace_id
Create Date: 2026-07-12 18:00:00.000000
"""
from collections.abc import Sequence
import sqlalchemy as sa
from alembic import op
revision: str = "withdraw_allow_multiple_active"
down_revision: str | Sequence[str] | None = "ad_ecpm_trace_id"
branch_labels: str | Sequence[str] | None = None
depends_on: str | Sequence[str] | None = None
def upgrade() -> None:
op.drop_index("ux_withdraw_order_user_active", table_name="withdraw_order")
def downgrade() -> None:
op.create_index(
"ux_withdraw_order_user_active",
"withdraw_order",
["user_id"],
unique=True,
sqlite_where=sa.text("status IN ('reviewing', 'pending')"),
postgresql_where=sa.text("status IN ('reviewing', 'pending')"),
)
+4
View File
@@ -36,6 +36,8 @@ class AdminComparisonListItem(BaseModel):
retry_count: int | None = None
input_tokens: int | None = None # Σ usage.prompt_tokens(server 派生)
output_tokens: int | None = None # Σ usage.completion_tokens(server 派生)
# 本次比价 LLM 总成本(元,按当时价冻结);旧记录/未回填为 None → 前端「成本」列回退估算。见 services/llm_cost.py。
llm_cost_yuan: float | None = None
device_model: str | None = None
rom_vendor: str | None = None
rom_name: str | None = None
@@ -72,3 +74,5 @@ class AdminComparisonDetail(AdminComparisonListItem):
# 原始上报全量;「卡在哪一步」从 raw_payload.platform_results[*].status 读
# (store_not_found/items_not_found/below_minimum/unsupported = 卡在 找店/加菜/起送/读价)。
raw_payload: dict | None = None
# 算成本所用单价快照 {mode, prices:{model:{...}}}(llm_cost_yuan 继承自列表项)。见 services/llm_cost.py。
llm_price_snapshot: dict | None = None
+3
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@@ -27,6 +27,7 @@ from app.schemas.compare_record import (
ComparisonRecordOut,
ComparisonRecordPage,
)
from app.services.llm_cost import compute_llm_cost, get_llm_prices
from app.services.pricebot_llm_calls import fetch_llm_calls
logger = logging.getLogger("shagua.compare_record")
@@ -81,6 +82,8 @@ def _backfill_llm_calls(record_id: int, trace_id: str) -> None:
# error 的调用 usage 可能为 None,or {} 兜底)
rec.input_tokens = sum((c.get("usage") or {}).get("prompt_tokens") or 0 for c in calls)
rec.output_tokens = sum((c.get("usage") or {}).get("completion_tokens") or 0 for c in calls)
# 本次比价 LLM 成本(元)+ 当时单价快照:按 app_config 现价逐模型算好冻结(services/llm_cost.py)。
rec.llm_cost_yuan, rec.llm_price_snapshot = compute_llm_cost(calls, get_llm_prices(db))
db.commit()
logger.info(
"backfill llm_calls trace=%s n=%d in_tok=%d out_tok=%d",
+6 -1
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@@ -201,7 +201,7 @@ def withdraw_info(
"/withdraw",
response_model=WithdrawResultOut,
summary="发起提现(扣款建单,待人工审核;审核通过后才打款)",
dependencies=[Depends(rate_limit(5, 60, "withdraw"))], # IP 级粗限流;余额/档位/幂等限制在仓库层
dependencies=[Depends(rate_limit(5, 60, "withdraw"))], # IP 级粗限流;用户级未完成单限制在仓库层
)
def withdraw(req: WithdrawRequest, user: CurrentUser, db: DbSession) -> WithdrawResultOut:
# 提现发起本身不调微信(打款在审核通过后),但仍要求微信支付已配置——否则审核通过也打不了款,提前拦
@@ -222,6 +222,11 @@ def withdraw(req: WithdrawRequest, user: CurrentUser, db: DbSession) -> Withdraw
) from e
except crud_wallet.WechatNotBoundError as e:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="请先绑定微信") from e
except crud_wallet.WithdrawTooFrequentError as e:
raise HTTPException(
status_code=status.HTTP_409_CONFLICT,
detail="已有提现申请正在审核或打款中,请处理完成后再申请",
) from e
except crud_wallet.WithdrawTierUnavailableError as e:
# 福利页档位闸(7-9):次数满/已选其他额度。正常客户端已按 tiers 预拦,此处兜底防绕过。
raise HTTPException(status_code=status.HTTP_409_CONFLICT, detail="今日额度已达上限") from e
+15
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@@ -96,4 +96,19 @@ CONFIG_DEFS: dict[str, dict[str, Any]] = {
"group": "首页轮播", "type": "enum", "hidden": True,
"help": "mixed=真实优先+种子补位(默认);real=只用真实比价记录;seed=只用种子/合成(演示)。",
},
# 比价 LLM 调用成本计价。值是嵌套 JSON(非 str→int),借 dict_str_int 类型在配置页走原始 JSON
# 编辑框;set_value 不校验类型,嵌套 JSON 照存。
"llm_token_price": {
"default": {
"per_model": {"qwen3.5-flash": {"input_per_1m": 0.8, "output_per_1m": 2.0}},
"default": {"input_per_1m": 3.0, "output_per_1m": 15.0},
"currency": "CNY", "unit": "per_1m_tokens",
},
"label": "LLM 模型单价(元/百万 token)",
"group": "LLM 成本", "type": "dict_str_int",
"help": (
"比价 LLM 调用成本计价。JSON:per_model 按模型配 input/output 单价(元/1M token),"
"default 兜底未登记的模型。改价只影响之后回填的新记录,历史记录用当时价格快照。"
),
},
}
+6
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@@ -137,6 +137,12 @@ class ComparisonRecord(Base):
# 每次 LLM 调用明细 [{scene,model,input_messages,output,usage,latency_ms,error}];
# server 收上报后按 trace_id 同机拉 pricebot 落库(见 compare_record 端点)。旧记录/未采集为 None。
llm_calls: Mapped[list | None] = mapped_column(_JSON, nullable=True)
# 本次比价 LLM 总成本(元):回填时按「当时的价」逐模型算好冻结(见 services/llm_cost.py)。
# 单次亚分级 → float「元」(不用 *_cents)。旧记录/未回填为 None,前端回退「估算成本」。
llm_cost_yuan: Mapped[float | None] = mapped_column(Float, nullable=True)
# 算成本所用单价快照 {mode, prices:{model:{input_per_1m,output_per_1m,_source}}}:app_config 只存
# 当前价、不留历史,故把当时价冻结进来供审计/复算。
llm_price_snapshot: Mapped[dict | None] = mapped_column(_JSON, nullable=True)
created_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True), server_default=func.now(), index=True, nullable=False
+10
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@@ -96,6 +96,16 @@ class WithdrawOrder(Base):
"""
__tablename__ = "withdraw_order"
__table_args__ = (
Index(
"ux_withdraw_order_user_active",
"user_id",
unique=True,
sqlite_where=text("status IN ('reviewing', 'pending')"),
postgresql_where=text("status IN ('reviewing', 'pending')"),
),
)
id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
user_id: Mapped[int] = mapped_column(
Integer, ForeignKey("user.id"), index=True, nullable=False
+22
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@@ -33,6 +33,7 @@ from app.models.wallet import (
_WX_STATE_SUCCESS = "SUCCESS"
_WX_STATE_FAILED = {"FAIL", "CANCELLED", "CLOSED"}
_WX_STATE_WAIT_CONFIRM = "WAIT_USER_CONFIRM" # 用户还没在微信确认页确认
_WITHDRAW_ACTIVE_STATUSES = {"reviewing", "pending"}
# 免确认收款授权状态
_WX_AUTH_ACTIVE = "TAKING_EFFECT" # 已生效,可免确认转账
_WX_AUTH_CLOSED = "CLOSED" # 已关闭(用户/商户/风控),需重新开启
@@ -62,6 +63,10 @@ class InsufficientCashError(Exception):
"""现金余额不足。"""
class WithdrawTooFrequentError(Exception):
"""提现申请过于频繁,或已有未完成提现单。"""
class WithdrawTierUnavailableError(Exception):
"""该档位今日不可提:次数已满,或今天已选了其他额度(7-9 福利页档位规则)。"""
@@ -733,6 +738,15 @@ def create_withdraw(
else:
out_bill_no = uuid.uuid4().hex
active_order_id = db.execute(
select(WithdrawOrder.id).where(
WithdrawOrder.user_id == user_id,
WithdrawOrder.status.in_(_WITHDRAW_ACTIVE_STATUSES),
).limit(1)
).scalar_one_or_none()
if active_order_id is not None:
raise WithdrawTooFrequentError
# 福利页档位闸(7-9):coin_cash 只能提预设档位,且该档今日可提(服务端权威口径,防绕过
# 客户端刷)。放在幂等返回/在途互斥之后:同号重试仍原样返回旧单,不被档位闸误杀。
# allow_sub_min(0.01 调试直发)保持原样放行,不受档位约束;invite_cash 本轮无档位概念不校验。
@@ -791,6 +805,14 @@ def create_withdraw(
).scalar_one_or_none()
if existing is not None:
return existing
active_order_id = db.execute(
select(WithdrawOrder.id).where(
WithdrawOrder.user_id == user_id,
WithdrawOrder.status.in_(_WITHDRAW_ACTIVE_STATUSES),
).limit(1)
).scalar_one_or_none()
if active_order_id is not None:
raise WithdrawTooFrequentError from None
raise
db.refresh(order)
return order # 待管理员审核;**不在此处打款**
+53
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@@ -0,0 +1,53 @@
"""LLM 调用成本计算(纯逻辑,无 DB):按 model 分桶累加 token × 单价,返回总成本(元)+ 价格快照。
用量取自 comparison_record.llm_calls[].usage(pricebot 已归一为 prompt/completion_tokens);
error / 无 usage 的调用跳过。price_cfg = {per_model:{model:{input_per_1m,output_per_1m}}, default:{...}}。
成本单位「元」——单次亚分级,用 float(不用 *_cents);snapshot 只含本次用到的模型的价(审计用,
不存整张价表)。用到但没配价(既无 per_model 又无 default)的模型 → 快照标 unpriced,成本按 0 计。
"""
from __future__ import annotations
_PRICE_KEY = "llm_token_price"
def get_llm_prices(db) -> dict:
"""读 LLM 单价配置(app_config;表内无则回退 CONFIG_DEFS 默认)。返回 compute_llm_cost 的 price_cfg。"""
from app.repositories import app_config # 延迟 import:compute_llm_cost 纯逻辑不牵连 DB 层
return app_config.get_value(db, _PRICE_KEY)
def compute_llm_cost(calls: list[dict], price_cfg: dict) -> tuple[float | None, dict | None]:
"""遍历 calls 按 model 分桶,cost = Σ(入/1e6*入价 + 出/1e6*出价);无有效调用 → (None, None)。"""
if not calls:
return None, None
per_model = price_cfg.get("per_model") or {}
default = price_cfg.get("default")
buckets: dict[str, list[int]] = {} # model -> [Σprompt_tokens, Σcompletion_tokens]
for c in calls:
if c.get("error"):
continue
usage = c.get("usage") or {}
model = c.get("model") or "unknown"
b = buckets.setdefault(model, [0, 0])
b[0] += usage.get("prompt_tokens") or 0
b[1] += usage.get("completion_tokens") or 0
if not buckets: # 全是 error / 无 usage
return None, None
total = 0.0
prices: dict[str, dict] = {}
for model, (tin, tout) in buckets.items():
price = per_model.get(model, default)
in_p = price.get("input_per_1m") if isinstance(price, dict) else None
out_p = price.get("output_per_1m") if isinstance(price, dict) else None
# 没配价 / 无 default / 单价残缺或非法(配置页手改 JSON 可能存出脏数据)→ 标记待补价、
# 不计入成本;绝不抛异常,以免连累同一回填里的 token/llm_calls 落库。
if not isinstance(in_p, (int, float)) or not isinstance(out_p, (int, float)):
prices[model] = {"input_per_1m": in_p, "output_per_1m": out_p, "unpriced": True}
continue
total += tin / 1e6 * in_p + tout / 1e6 * out_p
prices[model] = {
"input_per_1m": in_p,
"output_per_1m": out_p,
"_source": "per_model" if model in per_model else "default",
}
return round(total, 6), {"mode": "per_model", "prices": prices}
+2
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@@ -40,6 +40,8 @@
| `raw_payload` | JSON(PG: JSONB) | nullable | 客户端原始上报全量(calibration + done.params),取数兜底 |
| `input_tokens` | Integer | nullable | 本次 LLM 累计输入 token = Σ `llm_calls[].usage.prompt_tokens`(server 收上报后从 `llm_calls` 累加;旧记录/未采集为 null) |
| `output_tokens` | Integer | nullable | 本次 LLM 累计输出 token = Σ `llm_calls[].usage.completion_tokens`(同上) |
| `llm_cost_yuan` | Float | nullable | 本次比价 LLM 总成本(元),回填时按「当时价」逐模型算好冻结(见 `services/llm_cost.py`);旧记录/未回填为 null → 前端回退「估算成本」 |
| `llm_price_snapshot` | JSON(PG: JSONB) | nullable | 算成本所用单价快照 `{mode, prices:{model:{input_per_1m,output_per_1m,_source}}}`;`app_config` 只存当前价、不留历史,故冻结当时价供审计/复算 |
| `created_at` | DateTime(tz) | server_default now(), index | 时间 |
> `ordered`(已下单)是**瞬态字段**,不在表里:`list_records` 读取时按 `store_name ∈ 该用户 source='compare' 的 savings_record.shop_name 集合` 现挂到实例上供出参用。
+2 -2
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@@ -45,9 +45,9 @@ reviewing ──admin 审核拒绝──▶ rejected(已退款)
## 索引与约束
- PK `id`;UNIQUE+index `out_bill_no`;index `user_id``created_at`
- 同一用户允许同时存在多笔 `reviewing` / `pending` 提现单;每笔以唯一 `out_bill_no` 独立审核、打款和对账
- 部分唯一索引 `ux_withdraw_order_user_active`(`user_id`),条件 `status IN ('reviewing', 'pending')`:每个用户同时只能有一笔在途(待审核 / 打款中)提现单,DB 层挡并发重复提现
## 注意
- **资金安全**:允许多笔在途不等于重复扣款。仍由原子扣款(`WHERE cash_balance_cents >= amount`)+ `out_bill_no` 幂等 + 结果不明时**先查单再决定,绝不盲目退款**(防退款后又到账)+ 孤儿 pending 单 `reconcile_pending_withdraws` 对账兜底。
- **资金安全**:原子扣款(`WHERE cash_balance_cents >= amount`)+ `out_bill_no` 幂等 + 结果不明时**先查单再决定,绝不盲目退款**(防退款后又到账)+ 孤儿 pending 单 `reconcile_pending_withdraws` 对账兜底。
- `WITHDRAW_MIN_CENTS=10`(0.1 元,微信商家转账地板价),可经 `app_config.withdraw_min_cents` 后台调。
- "待审核期间钱已扣减",防用户拿同一笔余额重复发起多笔提现。
@@ -134,7 +134,7 @@ App 用户主表。两种登录(极光一键 / 短信验证码)都映射到
## `withdraw_order` — 提现单
现金 → 微信零钱。状态机:reviewing(待审核)→ pending(打款在途)→ success / failedreviewing →(拒绝)→ rejected(已退款)。同一用户同时存在多笔进行中的提现单,每次提交通过唯一 `out_bill_no` 保证幂等
现金 → 微信零钱。状态机:reviewing(待审核)→ pending(打款在途)→ success / failedreviewing →(拒绝)→ rejected(已退款)。同一用户同时只能有一笔进行中
| 字段 | 类型 | 说明 |
|---|---|---|
+248
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@@ -0,0 +1,248 @@
"""一次性 mock:造带 LLM token 成本的比价记录 + 配好 app_config 模型单价,用于测「管理后端」LLM 成本展示。
覆盖 admin「比价记录」详情抽屉的「LLM 成本」展示分支:
• app_config.llm_token_price ← 写一条多模型单价(= 配置页「LLM 成本」卡片「已改」态,get_llm_prices 读它)
• comparison_record ← 造 5 条,逐条**复用生产的 compute_llm_cost + 与 _backfill_llm_calls 同款派生**
(llm_call_count/retry_count/input_tokens/output_tokens/llm_cost_yuan/llm_price_snapshot),
确保 mock 行 = 真实回填产出。5 条刻意覆盖:
① 单模型真实样本(qwen3.5-flash ×4) → ¥0.006184(核对精确值)
② 多模型(flash + plus) → 快照含两个模型、各自 _source=per_model
③ 未登记模型(deepseek-v3) → 走 default,快照 _source=default
④ 旧记录(有 token、无 cost) → llm_cost_yuan=NULL → 前端回退「估算成本」
⑤ 含 error 调用 → error 那次跳过计费、retry_count+1
记录挂到库里第一个真实用户(admin 列表能显示手机号);无用户则 user_id=NULL(孤儿行,admin 照样全看)。
created_at 用北京 naive、最近几分钟内错开,详情列表倒序即 ①→⑤ 置顶。
幂等:重跑先按 trace_id 前缀「MOCKLLM-」清旧再建。app_config 单价是 upsert(不随 --clean-only 删,
因该 key 本就是本需求新增、无历史真实值;要改价直接去配置页或重跑本脚本)。
python -m scripts.seed_mock_llm_cost # 造价格 + 5 条记录
python -m scripts.seed_mock_llm_cost --clean-only # 只清 MOCKLLM- 记录(保留单价)
验收:admin「比价记录」→ 找 trace「MOCKLLM-」的 5 条 → 点开详情看「LLM 成本」:
①②③⑤ 显示「实际·当时价」+ 价格快照;④ 显示「估算」。
"""
from __future__ import annotations
import argparse
import sys
from datetime import datetime, timedelta, timezone
from sqlalchemy import delete, select
from app.db.session import SessionLocal
from app.models.comparison import ComparisonRecord
from app.models.user import User
from app.repositories import app_config
from app.services.llm_cost import compute_llm_cost
if hasattr(sys.stdout, "reconfigure"):
sys.stdout.reconfigure(encoding="utf-8") # Windows 控制台输出中文/¥
_BJ = timezone(timedelta(hours=8))
ID_PREFIX = "MOCKLLM-"
# ── 写进 app_config 的模型单价(get_llm_prices 读它;配置页「LLM 成本」卡片可再改)──
PRICE_CFG = {
"per_model": {
"qwen3.5-flash": {"input_per_1m": 0.8, "output_per_1m": 2.0},
"qwen3.5-plus": {"input_per_1m": 4.0, "output_per_1m": 12.0},
},
"default": {"input_per_1m": 3.0, "output_per_1m": 15.0},
"currency": "CNY",
"unit": "per_1m_tokens",
}
def _c(scene: str, model: str, pin: int, cout: int, error: str | None = None) -> dict:
"""一条 llm_calls 明细,结构对齐真实 pricebot 归一后契约:
{scene, model, input_messages:[{role,content}], output, usage:{prompt/completion/total_tokens},
latency_ms, error}(详情抽屉会遍历 input_messages,缺了会崩)。error 的调用无 usage/output。"""
return {
"scene": scene,
"model": model,
"error": error,
"input_messages": [
{"role": "system", "content": f"你是比价助手,负责 {scene} 环节。"},
{"role": "user", "content": f"[mock] 请处理本次比价的 {scene} 任务。"},
],
"output": None if error else f"[mock] {scene} 环节完成。",
"usage": None if error else {
"prompt_tokens": pin, "completion_tokens": cout, "total_tokens": pin + cout,
},
"latency_ms": 780,
}
# ── 5 条记录蓝本:calls 决定成本;freeze=False 模拟旧记录(有 token 无 cost)──
RECORDS = [
{
"label": "①单模型·真实样本",
"source": ("美团外卖", 4280), "best": ("京东秒送", 3680),
"store": "肯德基(建国路店)", "product": "疯狂星期四全家桶",
"info": "在京东秒送找到同款,到手价 ¥36.80,省 ¥6.00",
"freeze": True,
"calls": [
_c("store_match", "qwen3.5-flash", 1512, 22),
_c("dish_match", "qwen3.5-flash", 2111, 160),
_c("dish_match", "qwen3.5-flash", 1940, 142),
_c("summary", "qwen3.5-flash", 1325, 13),
],
},
{
"label": "②多模型·flash+plus",
"source": ("淘宝闪购", 5900), "best": ("美团外卖", 5200),
"store": "瑞幸咖啡(国贸店)", "product": "生椰拿铁×2、丝绒拿铁",
"info": "在美团外卖找到同款,到手价 ¥52.00,省 ¥7.00",
"freeze": True,
"calls": [
_c("store_match", "qwen3.5-flash", 2000, 50),
_c("dish_match", "qwen3.5-flash", 1800, 40),
_c("reasoning", "qwen3.5-plus", 3000, 500),
],
},
{
"label": "③未登记模型走 default",
"source": ("京东秒送", 3100), "best": ("美团外卖", 2650),
"store": "麦当劳(soho店)", "product": "麦辣鸡腿堡套餐",
"info": "在美团外卖找到同款,到手价 ¥26.50,省 ¥4.50",
"freeze": True,
"calls": [
_c("store_match", "deepseek-v3", 5000, 800),
],
},
{
"label": "④旧记录·有token无成本(回退估算)",
"source": ("美团外卖", 3600), "best": ("淘宝闪购", 3200),
"store": "华莱士(双井店)", "product": "全鸡汉堡套餐",
"info": "在淘宝闪购找到同款,到手价 ¥32.00,省 ¥4.00",
"freeze": False, # 模拟本需求上线前的老记录:llm_cost_yuan=NULL → 前端回退估算
"calls": [
_c("store_match", "qwen3.5-flash", 2000, 100),
],
},
{
"label": "⑤含 error 调用(跳过计费)",
"source": ("淘宝闪购", 4100), "best": ("京东秒送", 3750),
"store": "海底捞(合生汇店)", "product": "番茄锅底、肥牛卷",
"info": "在京东秒送找到同款,到手价 ¥37.50,省 ¥3.50",
"freeze": True,
"calls": [
_c("store_match", "qwen3.5-flash", 0, 0, error="timeout"),
_c("store_match", "qwen3.5-flash", 1500, 30),
],
},
]
_PLATFORM_ID = { # 展示名 → 平台代号(comparison_results / source/best 列用)
"美团外卖": "meituan", "京东秒送": "jd", "淘宝闪购": "taobao",
}
def _naive_bj_now() -> datetime:
return datetime.now(_BJ).replace(tzinfo=None)
def clean(db) -> int:
n = db.execute(
delete(ComparisonRecord).where(ComparisonRecord.trace_id.like(f"{ID_PREFIX}%"))
).rowcount or 0
db.commit()
return n
def _build_record(spec: dict, owner_id: int | None, created_at: datetime) -> tuple[ComparisonRecord, float | None]:
"""按蓝本造一条记录,LLM 派生完全对齐 _backfill_llm_calls;返回 (记录, 冻结成本或 None)。"""
calls = spec["calls"]
src_name, src_cents = spec["source"]
best_name, best_cents = spec["best"]
# —— 与 _backfill_llm_calls 同款派生 ——
llm_call_count = len(calls)
retry_count = sum(1 for c in calls if c.get("error"))
input_tokens = sum((c.get("usage") or {}).get("prompt_tokens") or 0 for c in calls)
output_tokens = sum((c.get("usage") or {}).get("completion_tokens") or 0 for c in calls)
if spec["freeze"]:
cost, snapshot = compute_llm_cost(calls, PRICE_CFG) # 复用生产纯函数
else:
cost, snapshot = None, None # 旧记录:回填这段代码上线前就有,只有 token 没成本
rec = ComparisonRecord(
user_id=owner_id,
device_id=f"{ID_PREFIX.lower()}dev",
business_type="food",
trace_id=f"{ID_PREFIX}{spec['label'][0]}", # ①..⑤ 各一,唯一
status="success",
source_platform_id=_PLATFORM_ID.get(src_name), source_platform_name=src_name,
source_price_cents=src_cents,
best_platform_id=_PLATFORM_ID.get(best_name), best_platform_name=best_name,
best_price_cents=best_cents,
saved_amount_cents=src_cents - best_cents,
is_source_best=False,
store_name=spec["store"],
product_names=spec["product"],
information=spec["info"],
items=[{"name": spec["product"], "qty": 1}],
comparison_results=[
{"platform_id": _PLATFORM_ID.get(src_name), "platform_name": src_name,
"price": src_cents / 100, "is_source": True, "rank": 2},
{"platform_id": _PLATFORM_ID.get(best_name), "platform_name": best_name,
"price": best_cents / 100, "is_source": False, "rank": 1},
],
total_ms=90_000 + llm_call_count * 1000,
step_count=llm_call_count * 3,
llm_call_count=llm_call_count,
retry_count=retry_count,
input_tokens=input_tokens,
output_tokens=output_tokens,
llm_calls=calls,
llm_cost_yuan=cost,
llm_price_snapshot=snapshot,
created_at=created_at,
)
return rec, cost
def seed(db) -> list[tuple[str, float | None]]:
app_config.set_value(db, "llm_token_price", PRICE_CFG, admin_id=None) # upsert 单价
owner_id = db.execute(select(User.id).order_by(User.id).limit(1)).scalar()
base = _naive_bj_now()
out: list[tuple[str, float | None]] = []
for i, spec in enumerate(RECORDS):
rec, cost = _build_record(spec, owner_id, base - timedelta(minutes=i * 3))
db.add(rec)
out.append((spec["label"], cost))
db.commit()
return out, owner_id
def main() -> None:
parser = argparse.ArgumentParser(description="造带 LLM 成本的比价记录 + app_config 模型单价(测管理后端)")
parser.add_argument("--clean-only", action="store_true", help="只清 MOCKLLM- 记录,不重建(保留单价)")
args = parser.parse_args()
db = SessionLocal()
try:
removed = clean(db)
if removed:
print(f"🧹 已清理旧 mock 记录 {removed}")
if args.clean_only:
print("✅ 仅清理,已完成(app_config 单价保留)。")
return
results, owner_id = seed(db)
print(f"\n✅ 已写入 app_config.llm_token_price(单价)+ {len(results)} 条比价记录"
f"(挂 user_id={owner_id or 'NULL(孤儿行)'})")
print("\n📋 每条冻结成本(admin 详情「LLM 成本」应显示):")
for label, cost in results:
shown = "NULL → 前端回退「估算」" if cost is None else f"¥{cost}"
print(f" {label:<20} {shown}")
print("\n👉 验收:admin「比价记录」→ trace 搜「MOCKLLM-」→ 点开详情核对 LLM 成本 + 价格快照。")
print(" 配置页「系统配置」→「福利页」Tab →「LLM 成本」卡片,单价应为「已改」态。")
finally:
db.close()
if __name__ == "__main__":
main()
+1 -1
View File
@@ -7,7 +7,7 @@
- 配套双分录现金流水(withdraw / withdraw_refund / exchange_in)+ 账户余额,
让顶部「账本校验」保持绿色、详情抽屉的现金余额/流水也真实。
约束:同一 user 可有多笔 reviewing/pending 单,每笔 out_bill_no 唯一且独立对账,
约束:withdraw_order 有部分唯一索引(同一 user reviewing/pending 最多 1 单),
本脚本每个 mock 用户至多 1 个活动单,满足约束。
幂等:每次运行先按固定 mock 手机号清掉旧 mock 再重建。仅清理用 --clean-only。
+5 -4
View File
@@ -128,7 +128,8 @@ def test_invite_cash_reject_refunds_invite_account(client, monkeypatch) -> None:
def test_two_accounts_withdraw_independent(client, monkeypatch) -> None:
"""两账户各提各的不串:invite_cash 与 coin_cash 可同时保留 reviewing 单。"""
"""两账户各提各的不串:先提 invite_cash(拒绝结清),再提 cash,各扣各账户。
注:一个用户同一时间只能一个活跃提现单(跨账户),故第二笔需先结清第一笔。"""
_patch_userinfo(monkeypatch, "openid_ic_3")
token = _login(client, "13800004003")
_seed_balances(client, token, "13800004003", cash=400, invite_cash=500)
@@ -139,17 +140,17 @@ def test_two_accounts_withdraw_independent(client, monkeypatch) -> None:
json={"amount_cents": 200, "source": "invite_cash"},
headers=_auth(token),
)
assert r1.status_code == 200 and r1.json()["status"] == "reviewing", r1.text
_reject(r1.json()["out_bill_no"]) # 退回 invite_cash + 结清活跃单
r2 = client.post(
"/api/v1/wallet/withdraw",
# 50 分 = 0.5 元档(7-9 起 coin_cash 只能提预设档位)
json={"amount_cents": 50, "source": "coin_cash"},
headers=_auth(token),
)
assert r2.status_code == 200 and r2.json()["status"] == "reviewing", r2.text
assert r2.json()["status"] == "reviewing"
cash, invite_cash = _balances(client, token)
assert invite_cash == 300 # invite_cash 单仍待审核,已扣 200
assert invite_cash == 500 # 已退回
assert cash == 350 # 扣了 cash 50
+184
View File
@@ -0,0 +1,184 @@
"""LLM 调用成本计算 compute_llm_cost:按模型分桶累加 token × 单价;error/无 usage 跳过。"""
from __future__ import annotations
from app.services.llm_cost import compute_llm_cost
_PRICE = {
"per_model": {"qwen3.5-flash": {"input_per_1m": 0.8, "output_per_1m": 2.0}},
"default": {"input_per_1m": 3.0, "output_per_1m": 15.0},
}
def test_sums_per_model_single_model():
# 真实样本:4 次 qwen3.5-flash;Σprompt=6888、Σcompletion=337
calls = [
{"model": "qwen3.5-flash", "error": None, "usage": {"prompt_tokens": 1512, "completion_tokens": 22}},
{"model": "qwen3.5-flash", "error": None, "usage": {"prompt_tokens": 2111, "completion_tokens": 160}},
{"model": "qwen3.5-flash", "error": None, "usage": {"prompt_tokens": 1940, "completion_tokens": 142}},
{"model": "qwen3.5-flash", "error": None, "usage": {"prompt_tokens": 1325, "completion_tokens": 13}},
]
cost, snapshot = compute_llm_cost(calls, _PRICE)
# 6888/1e6*0.8 + 337/1e6*2.0 = 0.0055104 + 0.000674 = 0.0061844 → round(6)
assert cost == 0.006184
assert snapshot == {
"mode": "per_model",
"prices": {
"qwen3.5-flash": {"input_per_1m": 0.8, "output_per_1m": 2.0, "_source": "per_model"},
},
}
def test_multi_model_prices_each_bucket_separately():
calls = [
{"model": "qwen3.5-flash", "error": None, "usage": {"prompt_tokens": 1_000_000, "completion_tokens": 0}},
{"model": "gpt-x", "error": None, "usage": {"prompt_tokens": 0, "completion_tokens": 1_000_000}},
]
price = {
"per_model": {
"qwen3.5-flash": {"input_per_1m": 0.8, "output_per_1m": 2.0},
"gpt-x": {"input_per_1m": 10.0, "output_per_1m": 30.0},
},
"default": {"input_per_1m": 3.0, "output_per_1m": 15.0},
}
cost, snap = compute_llm_cost(calls, price)
assert cost == 30.8 # qwen 1M入×0.8=0.8 + gpt-x 1M出×30=30.0
assert set(snap["prices"]) == {"qwen3.5-flash", "gpt-x"}
def test_unknown_model_falls_back_to_default():
calls = [{"model": "mystery", "error": None, "usage": {"prompt_tokens": 1_000_000, "completion_tokens": 0}}]
price = {"per_model": {}, "default": {"input_per_1m": 3.0, "output_per_1m": 15.0}}
cost, snap = compute_llm_cost(calls, price)
assert cost == 3.0
assert snap["prices"]["mystery"]["_source"] == "default"
def test_unpriced_model_marked_and_zero_cost():
calls = [{"model": "mystery", "error": None, "usage": {"prompt_tokens": 1_000_000, "completion_tokens": 999}}]
cost, snap = compute_llm_cost(calls, {"per_model": {}}) # 无 default
assert cost == 0.0
assert snap["prices"]["mystery"]["unpriced"] is True
def test_error_and_missing_usage_calls_skipped():
calls = [
{"model": "qwen3.5-flash", "error": "boom", "usage": {"prompt_tokens": 9_999_999, "completion_tokens": 9_999_999}},
{"model": "qwen3.5-flash", "error": None, "usage": None}, # 无 usage
{"model": "qwen3.5-flash", "error": None, "usage": {"prompt_tokens": 1_000_000, "completion_tokens": 0}},
]
cost, _ = compute_llm_cost(calls, _PRICE)
assert cost == 0.8 # 只有第 3 条计入
def test_empty_or_all_error_returns_none():
assert compute_llm_cost([], _PRICE) == (None, None)
assert compute_llm_cost(None, _PRICE) == (None, None)
all_error = [{"model": "x", "error": "boom", "usage": {"prompt_tokens": 100, "completion_tokens": 100}}]
assert compute_llm_cost(all_error, _PRICE) == (None, None)
def test_malformed_price_entry_is_treated_as_unpriced_not_raised():
# 手改配置页可能存出残缺/非法单价(缺 output_per_1m、非 dict);不能抛异常连累 token 回填。
calls = [
{"model": "bad-a", "error": None, "usage": {"prompt_tokens": 1_000_000, "completion_tokens": 5}},
{"model": "bad-b", "error": None, "usage": {"prompt_tokens": 1_000_000, "completion_tokens": 5}},
{"model": "ok", "error": None, "usage": {"prompt_tokens": 1_000_000, "completion_tokens": 0}},
]
price = {
"per_model": {
"bad-a": {"input_per_1m": 0.8}, # 缺 output_per_1m
"bad-b": 5, # 非 dict
"ok": {"input_per_1m": 3.0, "output_per_1m": 15.0},
},
}
cost, snap = compute_llm_cost(calls, price) # 不得抛异常
assert cost == 3.0 # 只有 ok(1M 入 × 3.0)计入;两个残缺项按 unpriced
assert snap["prices"]["bad-a"].get("unpriced") is True
assert snap["prices"]["bad-b"].get("unpriced") is True
def test_get_llm_prices_falls_back_to_default_then_uses_override():
from app.db.session import SessionLocal
from app.models.app_config import AppConfig
from app.repositories import app_config
from app.services.llm_cost import get_llm_prices
db = SessionLocal()
try:
# 无 override → CONFIG_DEFS 默认(含 per_model / default)
prices = get_llm_prices(db)
assert "per_model" in prices and "default" in prices
# 有 override → 用 DB 值
app_config.set_value(
db, "llm_token_price",
{"per_model": {"m": {"input_per_1m": 1.0, "output_per_1m": 2.0}},
"default": {"input_per_1m": 0.0, "output_per_1m": 0.0}},
admin_id=1,
)
assert get_llm_prices(db)["per_model"]["m"]["input_per_1m"] == 1.0
finally:
row = db.get(AppConfig, "llm_token_price")
if row is not None:
db.delete(row)
db.commit()
db.close()
def test_backfill_llm_calls_stores_cost_and_snapshot(monkeypatch):
from datetime import UTC, datetime
from app.api.v1 import compare_record
from app.db.session import SessionLocal
from app.models.app_config import AppConfig
from app.models.comparison import ComparisonRecord
from app.repositories import app_config
sample = [
{"model": "qwen3.5-flash", "error": None, "usage": {"prompt_tokens": 1512, "completion_tokens": 22}},
{"model": "qwen3.5-flash", "error": None, "usage": {"prompt_tokens": 2111, "completion_tokens": 160}},
{"model": "qwen3.5-flash", "error": None, "usage": {"prompt_tokens": 1940, "completion_tokens": 142}},
{"model": "qwen3.5-flash", "error": None, "usage": {"prompt_tokens": 1325, "completion_tokens": 13}},
]
monkeypatch.setattr(compare_record, "fetch_llm_calls", lambda trace_id: sample)
db = SessionLocal()
try:
app_config.set_value(
db, "llm_token_price",
{"per_model": {"qwen3.5-flash": {"input_per_1m": 0.8, "output_per_1m": 2.0}},
"default": {"input_per_1m": 3.0, "output_per_1m": 15.0}},
admin_id=1,
)
rec = ComparisonRecord(
trace_id="llmcost-bf-1", status="success",
created_at=datetime.now(UTC).replace(tzinfo=None),
)
db.add(rec)
db.commit()
rid = rec.id
finally:
db.close()
compare_record._backfill_llm_calls(rid, "llmcost-bf-1") # 独立 session 内回填
db = SessionLocal()
try:
rec = db.get(ComparisonRecord, rid)
assert rec.llm_cost_yuan == 0.006184
assert rec.llm_price_snapshot["prices"]["qwen3.5-flash"]["input_per_1m"] == 0.8
assert rec.input_tokens == 6888 # 现有 token 派生仍在
finally:
db.delete(db.get(ComparisonRecord, rid))
row = db.get(AppConfig, "llm_token_price")
if row is not None:
db.delete(row)
db.commit()
db.close()
def test_admin_detail_schema_exposes_llm_cost_fields():
from app.admin.schemas.comparison import AdminComparisonDetail
fields = AdminComparisonDetail.model_fields
assert "llm_cost_yuan" in fields
assert "llm_price_snapshot" in fields
-28
View File
@@ -268,34 +268,6 @@ def test_withdraw_idempotent_same_bill_no(client, monkeypatch) -> None:
assert sum(1 for t in r.json()["items"] if t["biz_type"] == "withdraw") == 1
def test_withdraw_allows_multiple_active_orders_with_distinct_bill_no(client, monkeypatch) -> None:
"""不同 out_bill_no 是不同提现意图,允许同时处于 reviewing;每笔分别扣款建单。"""
_patch_userinfo(monkeypatch, "openid_multi_active")
token = _login(client, "13800002017")
_seed_cash(client, token, "13800002017", 200)
client.post("/api/v1/wallet/bind-wechat", json={"code": "c"}, headers=_auth(token))
r1 = client.post(
"/api/v1/wallet/withdraw",
json={"amount_cents": 50, "out_bill_no": "multi_active_bill_1"},
headers=_auth(token),
)
r2 = client.post(
"/api/v1/wallet/withdraw",
json={"amount_cents": 50, "out_bill_no": "multi_active_bill_2"},
headers=_auth(token),
)
assert r1.status_code == 200 and r1.json()["status"] == "reviewing", r1.text
assert r2.status_code == 200 and r2.json()["status"] == "reviewing", r2.text
assert r1.json()["out_bill_no"] != r2.json()["out_bill_no"]
account = client.get("/api/v1/wallet/account", headers=_auth(token)).json()
assert account["cash_balance_cents"] == 100
txns = client.get("/api/v1/wallet/cash-transactions", headers=_auth(token)).json()["items"]
assert sum(1 for t in txns if t["biz_type"] == "withdraw") == 2
def test_withdraw_ambiguous_timeout_then_success_no_refund(client, monkeypatch) -> None:
"""#3 转账调用超时(异常),但查单确认已 SUCCESS → 不退款,单置 success。"""
monkeypatch.setattr("app.integrations.wxpay.code_to_userinfo", lambda code: {"openid": "openid_amb", "nickname": None, "avatar_url": None, "raw": {}})