241ae0d6a3
原来只有今日页有晨报、指标详情页有归因，其余页面一片空白。现在除设置外 的 10 个页面都有：健康、睡眠、运动、趋势、每日、身体成分、成绩预测、 身体年龄、挑战赛、运动详情。 不是给每个页面写一套，而是一个通用管线： - services/scopes.py：一个页面一个 context builder，返回同一个信封。 context["highlights"] 是已经算好的白话事实——模型负责解读它们，模型不 可用时规则引擎原样渲染。两者引用同一批数字，所以降级读起来不像换了个 App。 没数据的页面返回 None，宁可不出卡片，也不让模型对着空表格发挥。 - coach.scope_messages / parse_scope_insight：一套提示词吃所有页面，页面 的差异全在 context 里，加页面 = 加一个 builder。 - 前端 <AiPanel scope="…">：一个组件渲染所有页面，轮询逻辑抽成 lib/insight.ts 的 usePolledInsight，晨报卡也改用它。 ## 队列 一次生成 40 秒到 4.5 分钟，所以什么都不能在请求里生成。页面只负责入队， worker 负责消费（services/jobs.py）。 优先级才是用队列而不是后台线程的理由：同步完成后 prefetch 把所有页面按 背景优先级排进去，可能要跑半小时；而用户一打开某个页面，那个页面的任务 立刻提到队首、下一个就跑。你在看什么，队列就在算什么。 队列放在数据库而不是内存里，因为 gunicorn 有两个 worker：任务带 holder 声明后回读确认，和 scheduler.py 抢 tick 是同一套做法。id 由 user+kind+subject 推导，所以每几秒一次的轮询是幂等的入队，不会每几秒堆一 个任务。 ## 网关中断时踩到的两个坑（当场修了） 写完正好赶上 oracle 那台机器不通，于是看到： - 三次失败后任务被永久标 failed，网关恢复了也不会重试——一次瞬时中断就把 那个页面的解读判了死刑，直到它的数据碰巧变化。加了冷却期，过期后重置 尝试次数再排一次。 - 队列已经放弃了，页面还在 pending 转圈，要转满 8 分钟才停。meta.pending 现在跟着队列状态走，并把失败原因带给卡片。 顺带把 BAND_SOURCES 从 routes/settings.py 下沉到 services/insights.py： 教练要拿它做参照，而 services 不该反向依赖 routes。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
504 lines
21 KiB
Python
504 lines
21 KiB
Python
"""
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The AI coach: morning briefing, trend attribution, and the Copilot chat.
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Division of labour with `insights.py`: every number quoted here was already
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computed there. This module only turns a structured context into a prompt and
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turns the reply back into a structured answer. Nothing asks the model to do
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arithmetic, because a model asked to derive a z-score from a CSV gets it wrong
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often enough that the briefing would quote figures the charts contradict.
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Each feature has a rule-based counterpart. A model round-trip through the
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gateway costs minutes (its primary upstream is a large reasoning model), and a
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health screen that shows nothing when an upstream is rate-limited is worse than
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one that shows a plainer answer — so `meta.source` says which one answered
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rather than the failure being invisible.
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"""
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import json
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from services import ai as ai_svc
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from services import insights
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SYSTEM = """# 角色
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你是一名资深运动生理学专家与佳明（Garmin）数据分析教练。你解读用户的可穿戴设备
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数据，输出严谨、精炼、无废话的生理状态解读与行动指导。
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# 生理学原则
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1. 训练准备度综合睡眠分数、HRV 状态、恢复时间、急性负荷与压力历史。
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2. HRV 反映副交感神经活跃度；HRV 高且静息心率低通常代表恢复良好。
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3. 身体电量的充电量受睡眠质量与深睡/REM 比例影响：深睡负责肌肉与体力恢复，
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REM 负责认知与精神修复。
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4. 强度分钟与运动记录代表急性负荷；负荷骤增后 HRV 短暂下降属正常应激反应。
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# 数据纪律
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- 只使用输入 JSON 中出现的数值，禁止编造或估算任何未给出的数字。
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- 字段为 null 表示该项未采集，要么略过，要么明确说明"未采集"，不要当作 0。
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- z 值（z）是该指标相对用户自身近 28 天基线的偏离程度，已经算好，直接引用即可，
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不要自行重算。|z| < 1 属正常波动，不要渲染成异常。
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- 你不是医生，不做医疗诊断；只从运动恢复、疲劳管理与作息角度给建议。发现明显
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异常时提示用户咨询专业医师。
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# 输出
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- 简体中文。
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- 逻辑严谨、直接明确，禁止客套、禁止情绪化修辞。
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- 最终答案必须是一个 JSON 对象，且是你整段输出中最后出现的 JSON。
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JSON 之外的任何文字都会被丢弃。"""
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BRIEFING_SCHEMA = """{
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"status": "对整体恢复状态的定性，不超过 8 字，例如 '恢复良好' / '中等偏上' / '疲劳累积'",
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"headline": "一句话总结今日身体状态，不超过 40 字",
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"diagnosis": [
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{"title": "维度名，如 睡眠结构 / 自主神经 / 电量与就绪度", "detail": "该维度的判断与依据，引用具体数值，不超过 60 字"}
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],
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"shortfall": "今日最主要的短板，一句话；若无明显短板则写 '无明显短板'",
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"prescription": {
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"intensity": "今日运动强度上限，如 低 / 中等 / 中等偏高 / 高",
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"hrZone": "建议心率区间，如 'Zone 2~Zone 3'；无法判断填 null",
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"suggestion": "具体运动处方，含项目与时长，不超过 40 字",
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"durationMin": 建议时长的分钟数（整数）或 null,
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"avoid": "今日应避免的内容，不超过 20 字；无则填 null"
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},
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"actions": ["今日可执行的具体行动，2~4 条，每条不超过 30 字"]
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}"""
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TREND_SCHEMA = """{
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"summary": "这段区间内该指标发生了什么，一句话，不超过 50 字",
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"drivers": [
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{"factor": "关联因素名", "detail": "它与该指标的关系及依据，引用数值，不超过 60 字"}
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],
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"caution": "需要留意的风险或误读；没有则填 null",
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"confidence": "high|medium|low —— 取决于样本量与关联证据强度"
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}"""
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def _payload(context):
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"""The context as compact JSON.
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`ensure_ascii=False` matters for size as much as readability: escaping
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Chinese labels to \\uXXXX roughly triples their token cost.
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"""
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return json.dumps(context, ensure_ascii=False, separators=(",", ":"))
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def briefing_messages(context):
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return [
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{"role": "system", "content": SYSTEM},
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{
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"role": "user",
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"content": (
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"以下是我的健康数据快照。deviations 中的 z 值是相对我自身近 28 天\n"
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"基线的偏离，trends 是长周期走势，activityShift 是近 7 天与之前的\n"
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"活动量对比。\n\n"
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f"```json\n{_payload(context)}\n```\n\n"
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"请给出今日晨间简报与运动处方，严格按以下 JSON 结构输出：\n\n"
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f"{BRIEFING_SCHEMA}"
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),
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},
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]
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def trend_messages(window):
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return [
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{"role": "system", "content": SYSTEM},
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{
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"role": "user",
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"content": (
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f"以下是我 {window['label']} 指标在 {window['start']} ~ {window['end']}\n"
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"区间的数据，companions 是同区间内其它指标的均值，activities 是该区间\n"
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"内的运动记录，baselineBefore 是该区间之前的基线。\n\n"
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f"```json\n{_payload(window)}\n```\n\n"
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"请解释这段区间内该指标的变化及其可能的驱动因素，严格按以下 JSON\n"
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f"结构输出：\n\n{TREND_SCHEMA}"
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),
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},
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]
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COPILOT_SYSTEM = SYSTEM.replace(
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"""# 输出
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- 简体中文。
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- 逻辑严谨、直接明确，禁止客套、禁止情绪化修辞。
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- 最终答案必须是一个 JSON 对象，且是你整段输出中最后出现的 JSON。
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JSON 之外的任何文字都会被丢弃。""",
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"""# 输出
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- 简体中文，Markdown 格式。
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- 逻辑严谨、直接明确，禁止客套、禁止情绪化修辞。
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- 控制在 300 字以内，先给结论再给依据。
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- 引用数值时写明是哪一天或哪个区间的值。
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- 问题超出所给数据能回答的范围时，直接说明数据里没有，不要猜。""",
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)
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def copilot_messages(context, history, question):
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"""Chat turns for the Copilot.
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The health context rides in the system turn rather than being prepended to
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the user's question: it stays out of the visible transcript, and the same
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snapshot governs every turn instead of being re-sent (and re-charged) with
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each follow-up.
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"""
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messages = [
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{"role": "system", "content": COPILOT_SYSTEM},
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{
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"role": "system",
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"content": (
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"以下是提问者的健康数据快照，回答时以它为唯一事实来源：\n"
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f"```json\n{_payload(context)}\n```"
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),
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},
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]
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# Filtered first, then capped: capping first lets a single unusable entry
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# in the tail — a tool frame, an empty message — silently cost the model a
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# remembered turn.
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usable = [
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{"role": t["role"], "content": (t.get("content") or "").strip()[:2000]}
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for t in history
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if t.get("role") in ("user", "assistant") and (t.get("content") or "").strip()
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]
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messages.extend(usable[-8:])
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messages.append({"role": "user", "content": question[:2000]})
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return messages
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# --- reply validation -------------------------------------------------------
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def _text(value, limit):
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if value is None:
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return None
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text = str(value).strip()
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return text[:limit] if text else None
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def parse_briefing(reply):
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data = ai_svc.extract_json(reply)
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if not isinstance(data, dict):
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raise ai_svc.AIError("模型未返回 JSON 对象")
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prescription = data.get("prescription")
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if not isinstance(prescription, dict):
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prescription = {}
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duration = prescription.get("durationMin")
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try:
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duration = int(duration) if duration is not None else None
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except (TypeError, ValueError):
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duration = None
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diagnosis = []
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for item in data.get("diagnosis") or []:
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if isinstance(item, dict):
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title = _text(item.get("title"), 20)
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detail = _text(item.get("detail"), 200)
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else:
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title, detail = None, _text(item, 200)
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if detail:
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diagnosis.append({"title": title or "综合", "detail": detail})
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actions = [
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_text(a, 60) for a in (data.get("actions") or []) if _text(a, 60)
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]
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out = {
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"status": _text(data.get("status"), 20) or "状态未定性",
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"headline": _text(data.get("headline"), 120),
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"diagnosis": diagnosis[:5],
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"shortfall": _text(data.get("shortfall"), 120),
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"prescription": {
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"intensity": _text(prescription.get("intensity"), 20),
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"hrZone": _text(prescription.get("hrZone"), 40),
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"suggestion": _text(prescription.get("suggestion"), 120),
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"durationMin": duration,
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"avoid": _text(prescription.get("avoid"), 60),
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},
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"actions": actions[:4],
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}
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# A briefing with neither a headline nor any diagnosis is an empty card;
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# rejecting it here lets the caller fall back to the rule engine instead
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# of rendering blank space.
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if not out["headline"] and not out["diagnosis"]:
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raise ai_svc.AIError("模型返回的简报没有可用内容")
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return out
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def parse_trend_insight(reply):
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data = ai_svc.extract_json(reply)
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if not isinstance(data, dict):
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raise ai_svc.AIError("模型未返回 JSON 对象")
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drivers = []
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for item in data.get("drivers") or []:
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if isinstance(item, dict):
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factor = _text(item.get("factor"), 30)
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detail = _text(item.get("detail"), 200)
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else:
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factor, detail = None, _text(item, 200)
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if detail:
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drivers.append({"factor": factor or "关联因素", "detail": detail})
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confidence = str(data.get("confidence", "medium")).lower()
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if confidence not in ("high", "medium", "low"):
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confidence = "medium"
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summary = _text(data.get("summary"), 200)
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if not summary and not drivers:
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raise ai_svc.AIError("模型返回的归因没有可用内容")
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return {
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"summary": summary,
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"drivers": drivers[:5],
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"caution": _text(data.get("caution"), 200),
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"confidence": confidence,
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}
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# --- rule-based counterparts ------------------------------------------------
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def rule_briefing(context):
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"""A briefing assembled from the computed features alone.
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Deliberately quotes the same numbers the AI version would, so a fallback
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reads as a plainer answer rather than a different one.
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"""
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today = context["todayMetrics"]
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sleep = today["sleep"] or {}
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nervous = today["autonomicNervous"]
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recovery = today["recovery"]
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activity = today["activityToday"]
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by_metric = {d["metric"]: d for d in context["deviations"]}
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diagnosis = []
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concerns = []
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duration = sleep.get("durationHours")
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if duration is not None:
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target = sleep.get("targetHours") or insights.SLEEP_TARGET_HOURS
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parts = [f"睡眠 {duration:.1f} 小时（目标 {target:g}）"]
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rem = sleep.get("remPercent")
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if rem is not None:
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low, high = insights.REM_REFERENCE_PCT
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parts.append(f"REM {rem:g}%{'（偏低）' if rem < low else ''}")
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deep = sleep.get("deepPercent")
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if deep is not None:
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low, _ = insights.DEEP_REFERENCE_PCT
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parts.append(f"深睡 {deep:g}%{'（偏低）' if deep < low else '（达标）'}")
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diagnosis.append({"title": "睡眠结构", "detail": "，".join(parts) + "。"})
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if duration < target:
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concerns.append(f"睡眠比目标少 {target - duration:.1f} 小时")
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hrv, rhr = nervous.get("hrvMs"), nervous.get("restingHr")
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if hrv is not None or rhr is not None:
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parts = []
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if hrv is not None:
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base = by_metric.get("heartRateVariability", {}).get("baselineMean")
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parts.append(
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f"HRV {hrv:g} ms" + (f"（基线 {base:g}）" if base is not None else "")
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)
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if rhr is not None:
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base = by_metric.get("heartRate", {}).get("baselineMean")
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parts.append(
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f"静息心率 {rhr:g} bpm" + (f"（基线 {base:g}）" if base is not None else "")
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)
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diagnosis.append({"title": "自主神经", "detail": "，".join(parts) + "。"})
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readiness = recovery.get("trainingReadiness")
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battery = recovery.get("bodyBatteryPeak")
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if readiness is not None or battery is not None:
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parts = []
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if readiness is not None:
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parts.append(f"训练准备度 {readiness:g}/100")
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if battery is not None:
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parts.append(f"身体电量充至 {battery:g}")
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diagnosis.append({"title": "恢复与就绪度", "detail": "，".join(parts) + "。"})
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# Readiness is Garmin's own composite of sleep, HRV, recovery time and
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# acute load, so it drives the prescription wherever it exists; the
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# sleep/HRV fallback below is only for watches that do not report it.
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if readiness is not None:
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if readiness >= 75:
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intensity, zone, suggestion = "高", "Zone 3~Zone 4", "可安排高强度或长时间训练"
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elif readiness >= 50:
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intensity, zone, suggestion = "中等", "Zone 2~Zone 3", "30-45 分钟中低强度有氧"
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else:
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intensity, zone, suggestion = "低", "Zone 1~Zone 2", "以走路或拉伸为主，优先恢复"
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elif duration is not None and duration < (sleep.get("targetHours") or 7):
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intensity, zone, suggestion = "中等偏低", "Zone 2", "30 分钟低强度有氧，避免加练"
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else:
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intensity, zone, suggestion = "中等", "Zone 2~Zone 3", "30-45 分钟中低强度有氧"
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actions = []
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steps, goal = activity.get("steps"), activity.get("stepGoal")
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if steps is not None and goal and steps < goal:
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actions.append(f"步数 {steps:,} / 目标 {goal:,}，补一段快走")
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elif steps is not None and steps < 6000:
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actions.append(f"今日步数 {steps:,}，偏低，安排一次散步")
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if concerns:
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actions.append("提前 30 分钟入睡，补回睡眠缺口")
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sedentary = activity.get("sedentaryHours")
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if sedentary and sedentary >= 8:
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actions.append(f"久坐 {sedentary:g} 小时，每小时起身活动 3 分钟")
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shift = context.get("activityShift", {}).get("steps")
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if shift and shift.get("changePct") is not None and shift["changePct"] <= -20:
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actions.append(f"近 7 天步数较此前下降 {abs(shift['changePct']):g}%，注意活动量")
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if not actions:
|
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actions.append("各项指标处于常态，保持当前作息与训练安排")
|
||
|
||
notable = [
|
||
d for d in context["deviations"]
|
||
if d.get("z") is not None and abs(d["z"]) >= insights.Z_NOTABLE
|
||
]
|
||
if notable:
|
||
top = notable[0]
|
||
status = "存在偏离"
|
||
headline = (
|
||
f"{top['label']} {top['value']:g}{top['unit']}，"
|
||
f"偏离近 {top['baselineDays']} 天基线 {abs(top['z']):.1f} 个标准差。"
|
||
)
|
||
else:
|
||
status = "状态平稳"
|
||
headline = "各项指标均在个人基线的正常波动范围内。"
|
||
|
||
return {
|
||
"status": status,
|
||
"headline": headline,
|
||
"diagnosis": diagnosis,
|
||
"shortfall": "；".join(concerns) if concerns else "无明显短板",
|
||
"prescription": {
|
||
"intensity": intensity,
|
||
"hrZone": zone,
|
||
"suggestion": suggestion,
|
||
"durationMin": None,
|
||
"avoid": None,
|
||
},
|
||
"actions": actions[:4],
|
||
}
|
||
|
||
|
||
def rule_trend_insight(window):
|
||
"""Trend attribution without a model: direction, size, and co-movement."""
|
||
slope = window.get("slopePer30d")
|
||
label, unit = window["label"], window["unit"]
|
||
if slope is None:
|
||
summary = f"{window['start']} ~ {window['end']} 区间内 {label} 样本不足，无法判断趋势。"
|
||
else:
|
||
direction = "上升" if slope > 0 else ("下降" if slope < 0 else "基本持平")
|
||
summary = (
|
||
f"{label} 在该区间{direction}，拟合斜率约 {slope:g}{unit}/30 天，"
|
||
f"均值 {window['mean']:g}{unit}。"
|
||
)
|
||
|
||
drivers = []
|
||
baseline = window.get("baselineBefore")
|
||
if baseline and window.get("mean") is not None:
|
||
delta = window["mean"] - baseline["mean"]
|
||
drivers.append({
|
||
"factor": "区间前基线",
|
||
"detail": (
|
||
f"区间前 {baseline['days']} 天均值 {baseline['mean']:g}{unit}，"
|
||
f"区间内{'高出' if delta >= 0 else '低于'} {abs(delta):.2f}{unit}。"
|
||
),
|
||
})
|
||
activities = window.get("activities") or []
|
||
if activities:
|
||
minutes = sum(a.get("durationMin") or 0 for a in activities)
|
||
drivers.append({
|
||
"factor": "运动负荷",
|
||
"detail": f"该区间共 {len(activities)} 次运动，合计约 {minutes} 分钟。",
|
||
})
|
||
|
||
return {
|
||
"summary": summary,
|
||
"drivers": drivers,
|
||
"caution": "该结论由规则计算得出，未经模型归因，仅描述相关性而非因果。",
|
||
"confidence": "low",
|
||
}
|
||
|
||
|
||
# --- per-screen insights ----------------------------------------------------
|
||
# One prompt for every screen. The screen-specific part is entirely in the
|
||
# context `scopes.py` builds, so a new screen needs a builder and nothing here.
|
||
SCOPE_SCHEMA = """{
|
||
"headline": "针对这个页面的一句话结论，不超过 45 字",
|
||
"points": [
|
||
{"title": "维度名，不超过 8 字", "detail": "该维度的判断与依据，引用具体数值，不超过 70 字"}
|
||
],
|
||
"actions": ["可执行的建议，1~3 条，每条不超过 30 字；没有值得建议的就给空数组"],
|
||
"caution": "需要留意的风险或容易误读之处；没有就填 null",
|
||
"confidence": "high|medium|low —— 取决于样本量与证据强度"
|
||
}"""
|
||
|
||
|
||
def scope_messages(context):
|
||
"""Prompt for one screen's insight.
|
||
|
||
`highlights` goes in ahead of the raw data on purpose: they are the facts
|
||
already computed from it, and leading with them is what stops the model
|
||
re-deriving (and mis-deriving) numbers that are sitting right there.
|
||
"""
|
||
highlights = "\n".join(
|
||
f"- {h['title']}：{h['detail']}" for h in context.get("highlights") or []
|
||
)
|
||
return [
|
||
{"role": "system", "content": SYSTEM},
|
||
{
|
||
"role": "user",
|
||
"content": (
|
||
f"我正在看「{context.get('label')}」这个页面。以下是已经算好的要点：\n\n"
|
||
f"{highlights}\n\n"
|
||
"完整数据如下（deviations 的 z 值是相对我自身近 28 天基线的偏离，"
|
||
"trends 是长周期走势，两者都已算好，直接引用即可）：\n\n"
|
||
f"```json\n{_payload(context)}\n```\n\n"
|
||
"请针对这个页面给出解读与建议，只谈这个页面涉及的内容，"
|
||
f"严格按以下 JSON 结构输出：\n\n{SCOPE_SCHEMA}"
|
||
),
|
||
},
|
||
]
|
||
|
||
|
||
def parse_scope_insight(reply):
|
||
data = ai_svc.extract_json(reply)
|
||
if not isinstance(data, dict):
|
||
raise ai_svc.AIError("模型未返回 JSON 对象")
|
||
|
||
points = []
|
||
for item in data.get("points") or []:
|
||
if isinstance(item, dict):
|
||
title = _text(item.get("title"), 20)
|
||
detail = _text(item.get("detail"), 220)
|
||
else:
|
||
title, detail = None, _text(item, 220)
|
||
if detail:
|
||
points.append({"title": title or "要点", "detail": detail})
|
||
|
||
actions = [_text(a, 60) for a in (data.get("actions") or []) if _text(a, 60)]
|
||
confidence = str(data.get("confidence", "medium")).lower()
|
||
if confidence not in ("high", "medium", "low"):
|
||
confidence = "medium"
|
||
|
||
headline = _text(data.get("headline"), 140)
|
||
# A card with no headline and no points is blank space; rejecting it lets
|
||
# the caller fall back to the rule engine rather than render nothing.
|
||
if not headline and not points:
|
||
raise ai_svc.AIError("模型返回的解读没有可用内容")
|
||
|
||
return {
|
||
"headline": headline,
|
||
"points": points[:5],
|
||
"actions": actions[:3],
|
||
"caution": _text(data.get("caution"), 200),
|
||
"confidence": confidence,
|
||
}
|
||
|
||
|
||
def rule_scope_insight(context):
|
||
"""The computed highlights, rendered as-is.
|
||
|
||
No interpretation, and it says so: the honest fallback is the facts without
|
||
the reading of them, not a guess at what a model would have said.
|
||
"""
|
||
highlights = context.get("highlights") or []
|
||
return {
|
||
# The first highlight leads and is then dropped from the list: showing
|
||
# it in both places printed the same sentence twice.
|
||
"headline": highlights[0]["detail"] if highlights else None,
|
||
"points": [dict(h) for h in highlights[1:6]],
|
||
"actions": [],
|
||
"caution": "以下为直接计算结果，尚未经过模型解读。",
|
||
"confidence": "low",
|
||
}
|