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221 lines
7.2 KiB
Python
221 lines
7.2 KiB
Python
"""POST /api/v1/track-price —— 价保哨兵的价格查询接口(LLM mock 实现)。
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## 场景
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用户在 App 内告知"我在 X 平台 ¥XXX 买了 Y 商品",客户端每天后台调一次此接口
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查"该商品当前在该平台的合理价"。客户端按 `savings > 0` 弹"该申请价保了"通知。
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## 为什么是 mock + LLM
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占坑期没有真实平台价 API。爬虫法规风险大。OCR 用户主动截图比较被动 — 不适合
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"每天自动监控"场景。
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折中方案:LLM 估"该商品在该平台的日常常见价"(锚价),再用哈希抖动模拟"今天降价
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/ 持平 / 涨价"。抖动是**确定性的**(seed = title+platform+date),同一商品同一天
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结果一致,跨天才会变化,不会让用户看到"通知反复抖动"。
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未来切真 API 时客户端 0 改动(接口签名/响应固定)。
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## 抖动设计
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seed = sha256(title + platform + YYYY-MM-DD).hexdigest()[:8]
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h = int(seed, 16) % 100
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- h ∈ [0, 60) → 降价 3-8%, trend="down" (60% 概率,让通知活跃)
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- h ∈ [60, 90) → 持平 ±2%, trend="flat"
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- h ∈ [90, 100)→ 涨价 3-8%, trend="up"
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60% 降价概率是策略选择:demo 体验里用户经常能看到"该申请价保了"通知,
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功能"动"得多。真实降价频率比这低很多,但 mock 阶段优先体验。
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"""
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from __future__ import annotations
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import hashlib
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import json
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import logging
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import re
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import time
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from datetime import datetime, timezone
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from typing import Optional
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from fastapi import APIRouter, HTTPException
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from app.llm_client import MOCK_LLM, chat
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from app.schemas import TrackPriceRequest, TrackPriceResponse
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logger = logging.getLogger("shagua.protect")
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router = APIRouter(prefix="/api/v1")
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SYSTEM_PROMPT = """你是一个商品当前价格估算助手。
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# 输入
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用户会告诉你:
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- 平台: 例如 京东 / 淘宝 / 拼多多 / 抖音
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- 商品标题: 商品完整名称
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- 用户购买价: 参考用,不一定是当前价
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- 购买距今天数: 例如 7天
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# 任务
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估算该商品在该平台目前的**日常常见售价**(不含特殊大促),作为"基础价"输出。
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# 估算思路
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- 考虑该平台的常态价位区间(例:京东自营常比拼多多百亿补贴高 5-10%)
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- 考虑时间维度:新品 3 个月内可能略涨,旧品逐渐降
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- 考虑大促节奏:618 / 双11 前后日常价会下浮
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- 不熟悉的商品 → 根据品类 + 品牌常识估算合理日常价
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- 完全陌生 → 取用户购买价的 ±5% 区间合理值
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# 输出格式
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严格 JSON,无 markdown,无解释,无任何其他文字:
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{"base_price": 数字, "reasoning": "一句话"}
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字段:
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- base_price: 商品在该平台的日常常见价数字,**必须正数,不允许 null**
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- reasoning: 一句话理由(给排查 LLM 行为用,客户端不展示)
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"""
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def _parse_llm_output(s: str) -> tuple[Optional[float], str]:
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"""从 LLM 输出剥出 base_price + reasoning。返回 (base_price | None, reasoning)。"""
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s = s.strip()
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s = re.sub(r"^```(?:json)?\s*", "", s)
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s = re.sub(r"\s*```$", "", s)
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try:
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data = json.loads(s)
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except json.JSONDecodeError:
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m = re.search(r"\{[^{}]*\}", s)
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if not m:
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return None, ""
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try:
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data = json.loads(m.group(0))
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except json.JSONDecodeError:
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return None, ""
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if not isinstance(data, dict):
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return None, ""
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bp_raw = data.get("base_price")
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if isinstance(bp_raw, bool):
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base_price = None
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elif isinstance(bp_raw, (int, float)):
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base_price = float(bp_raw)
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elif isinstance(bp_raw, str):
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try:
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base_price = float(bp_raw.strip())
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except ValueError:
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base_price = None
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else:
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base_price = None
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reasoning = data.get("reasoning")
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if not isinstance(reasoning, str):
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reasoning = ""
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return base_price, reasoning
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def _compute_jitter(title: str, platform: str, today_str: str) -> tuple[float, str]:
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"""确定性抖动 — 同 title+platform+date 结果一致,跨天才变。
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返回 (factor, trend),factor 是乘到 base_price 上的系数。
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"""
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seed_str = f"{title}|{platform}|{today_str}"
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digest = hashlib.sha256(seed_str.encode("utf-8")).hexdigest()
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h = int(digest[:8], 16) % 100
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if h < 60:
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# 降价 3-8%。在 [0, 60) 内线性插值到 [0.92, 0.97]
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factor = 0.92 + (h / 60.0) * 0.05
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trend = "down"
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elif h < 90:
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# 持平 ±2%。在 [60, 90) 内线性插值到 [0.98, 1.02]
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factor = 0.98 + ((h - 60) / 30.0) * 0.04
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trend = "flat"
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else:
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# 涨价 3-8%。在 [90, 100) 内线性插值到 [1.03, 1.08]
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factor = 1.03 + ((h - 90) / 10.0) * 0.05
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trend = "up"
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return factor, trend
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@router.post("/track-price", response_model=TrackPriceResponse)
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def track_price(req: TrackPriceRequest) -> TrackPriceResponse:
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title = req.product_title.strip()
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platform = req.platform.strip()
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if not title or not platform:
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raise HTTPException(status_code=422, detail="empty_title_or_platform")
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if req.purchase_price <= 0:
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raise HTTPException(status_code=422, detail="invalid_purchase_price")
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# 计算"购买距今多少天",给 LLM 当上下文
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now_ts = int(time.time())
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days_since = max(0, (now_ts - req.purchase_at) // 86400)
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user_msg = (
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f"平台: {platform}\n"
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f"商品标题: {title}\n"
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f"用户购买价: ¥{req.purchase_price:.2f}\n"
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f"购买距今: {days_since}天"
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)
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if MOCK_LLM:
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# mock:跳过 LLM,下方 base_price 回退到 purchase_price 锚 + 确定性抖动。
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raw = ""
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else:
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try:
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raw = chat(
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messages=[
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": user_msg},
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]
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)
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logger.debug("track-price LLM raw: %s", raw[:300])
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except Exception as e:
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# LLM 不可用(限流/网络/超时):不 500,用 purchase_price 作锚。
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logger.warning("track-price LLM call failed, using purchase_price anchor: %s", e)
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raw = ""
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base_price, reasoning = _parse_llm_output(raw)
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# base_price 兜底:LLM 不听话或给 <=0,用 purchase_price 作为锚
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# (occurence 极少;占坑期 mock 不能让接口因 LLM 抽风就 500)
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if base_price is None or base_price <= 0:
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logger.warning(
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"LLM did not return valid base_price for %r, fallback to purchase_price",
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title,
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)
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base_price = req.purchase_price
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# 抖动 — seed = title + platform + 服务器当前日期(UTC,日切清晰)
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today_str = datetime.now(timezone.utc).strftime("%Y-%m-%d")
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factor, trend = _compute_jitter(title, platform, today_str)
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current_price = round(base_price * factor, 2)
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savings = round(req.purchase_price - current_price, 2)
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logger.info(
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"track-price: platform=%s title=%r purchase=¥%.2f base=¥%.2f current=¥%.2f"
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" trend=%s savings=¥%.2f reasoning=%r",
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platform,
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title,
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req.purchase_price,
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base_price,
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current_price,
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trend,
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savings,
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reasoning[:80],
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)
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return TrackPriceResponse(
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current_price=current_price,
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base_price=base_price,
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trend=trend,
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savings=savings,
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checked_at=now_ts,
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)
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