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
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+"""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} 条")
+        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()