feat(ai): AI 教练 —— 晨间简报、运动处方、趋势归因与 Copilot
数值全部在服务端算好再交给模型，模型只做解读。让模型从 CSV 里自己推 z 分数，它算错的次数足以让简报引用图表反驳它的数字。 - services/insights.py：z 分数（28 天个人基线，且**排除当天**——用一个 值参与算出来的均值去衡量它自己，会把真实离群点摊平）、13 个月趋势斜率 （按序数日期最小二乘，手表放充电器上一周不会压缩 x 轴）、近 7 天活动量 对比。 - services/coach.py：三套提示词 + 回复解析，每套都配一个规则引擎版本。 网关一次生成要几分钟，上游被限流时给一个朴素的答案，好过给一张空卡片。 - services/ai.py：多轮 chat()、SSE stream()、complete()/stream_chat()， 以及 extract_json()——上游是推理模型，可见输出以思维链开头，所以从末尾 倒着找最后一个配平的 JSON（字符串感知，扛得住引号里的 } 和转义引号）。 - 接口 briefing / trend-insight / copilot(SSE)，缓存表 ai_insights。 - 前端：今日页晨报卡（后台生成 + 轮询升级）、全局 Copilot 浮窗、指标详情 页归因面板。features.ai 打开。 实测（对着自建 ai-gateway）：晨报一次 273 秒，缓存命中 18 毫秒——所以简报 绝不能同步阻塞首屏。网关的流式通道比阻塞通道更不可靠：同一条提示词流式 139 秒后返回「所有模型均不可用」，阻塞则成功，因此 stream_chat() 在流式零 输出时对同一模型退回非流式重试。Copilot 实测 TTFB 9ms、全程 40 秒。 顺带修两处：refresh 原来只跳过缓存读、不删行，导致「重新生成」后的轮询读 到旧行、看到 cached 就停了，用户一直盯着他刚要求替换掉的那段字；基线零方差 时原来返回 z=0.0，把「和每一条观测都不同」标成「完全正常」，改为 z=null。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
ericwyuan
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backend/tests/test_coach.py
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706
backend/tests/test_coach.py
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"""
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Unit tests for the AI coach: feature engineering, prompt parsing, and the
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briefing / trend-insight / Copilot endpoints.
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Every model call is mocked. The suite never reaches the ai-gateway, so it is
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neither slow nor dependent on that box being up.
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"""
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import json
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import pytest
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from services import ai as ai_svc
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from services import analysis as analysis_svc
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from services import coach
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from services import insights
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def day(date, **metrics):
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"""One row in the shape `health.get_summary` returns."""
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sleep = metrics.pop("sleep", None)
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row = {"date": date, **metrics}
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row["sleep"] = sleep
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return row
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def flat_days(n, start=1, **series):
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"""`n` consecutive days from 2026-08-01, each metric a constant or list."""
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rows = []
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for i in range(n):
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values = {}
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for key, value in series.items():
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values[key] = value[i] if isinstance(value, list) else value
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rows.append(day(f"2026-08-{start + i:02d}", **values))
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return rows
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# --- feature engineering ----------------------------------------------------
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class TestFlatten:
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def test_sleep_stages_become_percentages_of_time_asleep(self):
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row = day("2026-08-01", sleep={
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"duration": 8.0, "quality": 80, "deepSeconds": 3600,
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"remSeconds": 7200, "lightSeconds": None, "awakeSeconds": None,
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})
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flat = insights._flatten(row)
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assert flat["sleepDeepPct"] == 12.5
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assert flat["sleepRemPct"] == 25.0
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def test_missing_stage_is_absent_not_zero(self):
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flat = insights._flatten(day("2026-08-01", sleep={"duration": 7.0}))
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assert "sleepDeepPct" not in flat
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def test_no_sleep_record_leaves_no_sleep_fields(self):
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flat = insights._flatten(day("2026-08-01", steps=100))
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assert "sleepDuration" not in flat
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def test_sedentary_seconds_become_hours(self):
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flat = insights._flatten(day("2026-08-01", sedentarySeconds=5400))
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assert flat["sedentaryHours"] == 1.5
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class TestDeviations:
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def test_z_score_measures_departure_from_the_personal_baseline(self):
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# A baseline that varies, as real data does: mean 1000, sd 100.
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baseline = [900, 1000, 1100, 900, 1000, 1100, 900, 1000, 1100, 1000]
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history = [
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insights._flatten(r) for r in flat_days(11, steps=baseline + [1800])
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]
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result = {d["metric"]: d for d in insights.deviations(history, history[-1])}
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assert result["steps"]["baselineMean"] == 1000
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assert result["steps"]["z"] > 3
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def test_today_is_excluded_from_its_own_baseline(self):
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rows = [insights._flatten(r) for r in flat_days(
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8, heartRate=[60, 60, 60, 60, 60, 60, 60, 70]
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)]
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result = {d["metric"]: d for d in insights.deviations(rows, rows[-1])}
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# Including today would pull the mean up to 61.25 and shrink the z.
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assert result["heartRate"]["baselineMean"] == 60
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def test_too_little_history_reports_insufficient_baseline(self):
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rows = [insights._flatten(r) for r in flat_days(3, steps=5000)]
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result = {d["metric"]: d for d in insights.deviations(rows, rows[-1])}
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assert result["steps"]["verdict"] == "基线不足"
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assert result["steps"]["z"] is None
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def test_a_flat_baseline_reports_no_z_rather_than_a_fabricated_zero(self):
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"""Dividing by a zero standard deviation is undefined; calling the day
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'z = 0' would label a genuine departure as perfectly typical."""
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rows = [insights._flatten(r) for r in flat_days(8, steps=[5000] * 7 + [9000])]
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result = {d["metric"]: d for d in insights.deviations(rows, rows[-1])}
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assert result["steps"]["z"] is None
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assert result["steps"]["verdict"] == "基线无波动"
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def test_a_flat_baseline_matched_exactly_is_just_normal(self):
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rows = [insights._flatten(r) for r in flat_days(8, steps=5000)]
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result = {d["metric"]: d for d in insights.deviations(rows, rows[-1])}
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assert result["steps"]["verdict"] == "正常"
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def test_direction_is_judged_per_metric_not_by_sign(self):
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wobble = [58, 60, 62, 58, 60, 62, 60]
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rows = [insights._flatten(r) for r in flat_days(
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8,
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heartRate=wobble + [75],
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heartRateVariability=[38, 40, 42, 38, 40, 42, 40] + [55],
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)]
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result = {d["metric"]: d for d in insights.deviations(rows, rows[-1])}
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# Both moved up; only one of them is good news.
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assert result["heartRate"]["verdict"] == "偏差"
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assert result["heartRateVariability"]["verdict"] == "偏好"
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def test_largest_departure_comes_first(self):
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rows = [insights._flatten(r) for r in flat_days(
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8, steps=[5000] * 7 + [5100], heartRate=[60] * 7 + [80]
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)]
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result = insights.deviations(rows, rows[-1])
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assert result[0]["metric"] == "heartRate"
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def test_metrics_absent_today_are_omitted(self):
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rows = [insights._flatten(r) for r in flat_days(8, steps=5000)]
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assert all(d["metric"] == "steps" for d in insights.deviations(rows, rows[-1]))
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class TestTrends:
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def test_slope_is_reported_per_thirty_days(self):
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rows = [insights._flatten(r) for r in flat_days(
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30, heartRateVariability=[40 + i for i in range(30)]
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)]
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entry = {t["metric"]: t for t in insights.trends(rows)}
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# One unit a day is 30 per 30 days.
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assert entry["heartRateVariability"]["slopePer30d"] == pytest.approx(30, abs=0.5)
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def test_endpoint_windows_do_not_overlap_on_short_histories(self):
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"""A 30-day history must not report delta 0 for a line that moved."""
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rows = [insights._flatten(r) for r in flat_days(
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30, steps=[1000 + i * 100 for i in range(30)]
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)]
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entry = {t["metric"]: t for t in insights.trends(rows)}["steps"]
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assert entry["firstMean"] < entry["lastMean"]
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assert entry["delta"] > 0
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def test_direction_respects_which_way_is_better(self):
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rows = [insights._flatten(r) for r in flat_days(
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30, heartRate=[80 - i for i in range(30)]
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)]
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entry = {t["metric"]: t for t in insights.trends(rows)}["heartRate"]
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assert entry["direction"] == "改善"
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def test_metrics_with_too_few_samples_are_skipped(self):
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rows = [insights._flatten(r) for r in flat_days(5, steps=1000)]
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assert insights.trends(rows) == []
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def test_gaps_do_not_compress_the_x_axis(self):
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"""Ordinal dates, not indices: the same rise spread over a longer span
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is a gentler slope, and indices would score the two identically."""
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values = [1000 + i * 100 for i in range(10)]
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dense = [(f"2026-08-{1 + i:02d}", v) for i, v in enumerate(values)]
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# Same ten readings, but the last five sit a month later.
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gapped = dense[:5] + [
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(f"2026-09-{6 + i:02d}", v) for i, v in enumerate(values[5:])
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]
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assert insights._slope_per_30d(gapped) < insights._slope_per_30d(dense)
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class TestActivityShift:
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def test_compares_the_last_week_with_the_weeks_before_it(self):
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rows = [insights._flatten(r) for r in flat_days(
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20, steps=[10000] * 13 + [5000] * 7
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)]
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shift = insights.activity_shift(rows)
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assert shift["steps"]["recentMean"] == 5000
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assert shift["steps"]["priorMean"] == 10000
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assert shift["steps"]["changePct"] == -50.0
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def test_absent_when_there_is_not_enough_history(self):
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rows = [insights._flatten(r) for r in flat_days(6, steps=8000)]
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assert "steps" not in insights.activity_shift(rows)
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class TestWindowContext:
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def test_unknown_metric_returns_nothing(self, db, user):
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assert insights.window_context(
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user["id"], "notAMetric", "2026-08-01", "2026-08-30"
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) is None
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def test_empty_span_returns_nothing(self, db, user):
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assert insights.window_context(
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user["id"], "steps", "2020-01-01", "2020-01-31"
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) is None
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class TestBuildContext:
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def test_returns_nothing_without_data(self, db, user):
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assert insights.build_context(user["id"]) is None
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def test_defaults_to_the_newest_recorded_day(self, db, user, seed_health):
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seed_health([
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{"date": "2026-08-01", "steps": 5000},
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{"date": "2026-08-02", "steps": 6000},
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])
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context = insights.build_context(user["id"])
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assert context["snapshotDate"] == "2026-08-02"
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def test_an_unknown_date_is_not_silently_replaced(self, db, user, seed_health):
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seed_health([{"date": "2026-08-01", "steps": 5000}])
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assert insights.build_context(user["id"], "2026-08-09") is None
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def test_stays_small_enough_to_prompt_with(self, db, user, seed_health):
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seed_health([
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{"date": f"2026-08-{d:02d}", "steps": 8000 + d, "heart_rate": 60,
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"hrv": 45, "sleep_duration": 7, "stress": 30}
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for d in range(1, 31)
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])
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context = insights.build_context(user["id"])
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blob = json.dumps(context, ensure_ascii=False)
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# A month of history has to cost thousands of characters, not tens of
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# thousands — the whole point of computing features server-side.
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assert len(blob) < 20_000
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# --- reply parsing ----------------------------------------------------------
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GOOD_BRIEFING = {
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"status": "中等偏上",
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"headline": "恢复尚可，睡眠偏短。",
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"diagnosis": [{"title": "睡眠结构", "detail": "睡眠 6 小时，低于目标。"}],
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"shortfall": "睡眠不足",
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"prescription": {
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"intensity": "中等", "hrZone": "Zone 2~Zone 3",
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"suggestion": "40 分钟慢跑", "durationMin": 40, "avoid": "高强度间歇",
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},
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"actions": ["提前 30 分钟入睡", "午后避免咖啡因"],
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}
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class TestParseBriefing:
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def test_plain_json(self):
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out = coach.parse_briefing(json.dumps(GOOD_BRIEFING, ensure_ascii=False))
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assert out["status"] == "中等偏上"
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assert out["prescription"]["durationMin"] == 40
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assert out["actions"] == ["提前 30 分钟入睡", "午后避免咖啡因"]
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def test_answer_is_taken_from_after_a_reasoning_preamble(self):
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"""The gateway's primary upstream narrates its thinking first."""
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reply = (
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'The user wants a briefing. Let me consider {"draft": true} first.\n'
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"Actually I should output the final object now:\n"
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+ json.dumps(GOOD_BRIEFING, ensure_ascii=False)
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)
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assert coach.parse_briefing(reply)["status"] == "中等偏上"
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def test_markdown_fences_are_tolerated(self):
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reply = "```json\n" + json.dumps(GOOD_BRIEFING, ensure_ascii=False) + "\n```"
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assert coach.parse_briefing(reply)["headline"] == "恢复尚可，睡眠偏短。"
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def test_missing_prescription_does_not_raise(self):
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payload = {k: v for k, v in GOOD_BRIEFING.items() if k != "prescription"}
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out = coach.parse_briefing(json.dumps(payload, ensure_ascii=False))
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assert out["prescription"]["suggestion"] is None
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def test_non_numeric_duration_becomes_none(self):
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payload = json.loads(json.dumps(GOOD_BRIEFING))
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payload["prescription"]["durationMin"] = "四十分钟"
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assert coach.parse_briefing(json.dumps(payload))["prescription"]["durationMin"] is None
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def test_diagnosis_written_as_plain_strings_is_accepted(self):
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payload = json.loads(json.dumps(GOOD_BRIEFING))
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payload["diagnosis"] = ["睡眠偏短。"]
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out = coach.parse_briefing(json.dumps(payload, ensure_ascii=False))
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assert out["diagnosis"][0]["detail"] == "睡眠偏短。"
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def test_an_empty_briefing_is_rejected_rather_than_rendered_blank(self):
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with pytest.raises(ai_svc.AIError):
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coach.parse_briefing(json.dumps({"status": "好"}))
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def test_prose_without_json_raises(self):
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with pytest.raises(ai_svc.AIError):
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coach.parse_briefing("今天状态不错，可以正常训练。")
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class TestParseTrendInsight:
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def test_valid_reply(self):
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reply = json.dumps({
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"summary": "HRV 稳步上升。",
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"drivers": [{"factor": "有氧负荷", "detail": "区间内 8 次有氧。"}],
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"caution": None, "confidence": "high",
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}, ensure_ascii=False)
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out = coach.parse_trend_insight(reply)
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assert out["confidence"] == "high"
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assert out["drivers"][0]["factor"] == "有氧负荷"
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def test_unknown_confidence_falls_back_to_medium(self):
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reply = json.dumps({"summary": "上升。", "confidence": "很高"}, ensure_ascii=False)
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assert coach.parse_trend_insight(reply)["confidence"] == "medium"
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def test_empty_reply_raises(self):
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with pytest.raises(ai_svc.AIError):
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coach.parse_trend_insight(json.dumps({"confidence": "high"}))
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class TestExtractJson:
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def test_last_object_wins_over_an_earlier_draft(self):
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assert ai_svc.extract_json('{"a": 1} then {"a": 2}') == {"a": 2}
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def test_braces_inside_strings_do_not_break_the_scan(self):
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assert ai_svc.extract_json('思考 } 中。{"t": "含 } 的文本"}')["t"] == "含 } 的文本"
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def test_escaped_quote_inside_a_string(self):
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assert ai_svc.extract_json(r'x {"t": "a \" b"}')["t"] == 'a " b'
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def test_arrays_are_extracted_too(self):
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assert ai_svc.extract_json("preamble [1, 2, 3]") == [1, 2, 3]
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def test_empty_reply_raises(self):
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with pytest.raises(ai_svc.AIError):
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ai_svc.extract_json(" ")
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# --- prompt assembly --------------------------------------------------------
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class TestPrompts:
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def test_system_prompt_forbids_inventing_numbers(self):
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assert "禁止编造" in coach.SYSTEM
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def test_system_prompt_disclaims_medical_diagnosis(self):
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assert "不做医疗诊断" in coach.SYSTEM
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def test_model_is_told_not_to_recompute_the_z_scores(self):
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assert "不要自行重算" in coach.SYSTEM
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def test_briefing_prompt_carries_the_context_as_json(self):
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messages = coach.briefing_messages({"snapshotDate": "2026-08-01", "x": 1})
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assert messages[0]["role"] == "system"
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assert '"snapshotDate":"2026-08-01"' in messages[1]["content"]
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def test_context_is_not_ascii_escaped(self):
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"""Escaping Chinese to \\uXXXX roughly triples its token cost."""
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assert "睡眠" in coach._payload({"label": "睡眠"})
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def test_copilot_keeps_the_context_out_of_the_visible_transcript(self):
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messages = coach.copilot_messages(
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{"snapshotDate": "2026-08-01"}, [], "我今天能练吗？"
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)
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assert [m["role"] for m in messages] == ["system", "system", "user"]
|
||||
assert messages[-1]["content"] == "我今天能练吗？"
|
||||
|
||||
def test_copilot_history_is_capped_and_role_filtered(self):
|
||||
history = [{"role": "user", "content": f"q{i}"} for i in range(20)]
|
||||
history.append({"role": "tool", "content": "ignored"})
|
||||
messages = coach.copilot_messages({}, history, "最后一问")
|
||||
turns = [m for m in messages if m["role"] != "system"]
|
||||
assert len(turns) == 9 # eight remembered turns plus the new question
|
||||
assert "ignored" not in json.dumps(messages, ensure_ascii=False)
|
||||
|
||||
def test_copilot_asks_for_markdown_not_json(self):
|
||||
assert "Markdown" in coach.COPILOT_SYSTEM
|
||||
assert "最后出现的 JSON" not in coach.COPILOT_SYSTEM
|
||||
|
||||
|
||||
# --- rule-based counterparts ------------------------------------------------
|
||||
def context_with(**today):
|
||||
base = {
|
||||
"snapshotDate": "2026-08-30",
|
||||
"todayMetrics": {
|
||||
"sleep": None,
|
||||
"autonomicNervous": {},
|
||||
"recovery": {},
|
||||
"activityToday": {},
|
||||
},
|
||||
"deviations": [],
|
||||
"trends": [],
|
||||
"activityShift": {},
|
||||
}
|
||||
base["todayMetrics"].update(today)
|
||||
return base
|
||||
|
||||
|
||||
class TestRuleBriefing:
|
||||
def test_readiness_drives_the_prescription(self):
|
||||
low = coach.rule_briefing(context_with(recovery={"trainingReadiness": 30}))
|
||||
high = coach.rule_briefing(context_with(recovery={"trainingReadiness": 85}))
|
||||
assert low["prescription"]["intensity"] == "低"
|
||||
assert high["prescription"]["intensity"] == "高"
|
||||
|
||||
def test_short_sleep_is_named_as_the_shortfall(self):
|
||||
out = coach.rule_briefing(context_with(
|
||||
sleep={"durationHours": 5.0, "targetHours": 7.0}
|
||||
))
|
||||
assert "睡眠" in out["shortfall"]
|
||||
|
||||
def test_no_shortfall_is_stated_explicitly(self):
|
||||
out = coach.rule_briefing(context_with(
|
||||
sleep={"durationHours": 8.0, "targetHours": 7.0}
|
||||
))
|
||||
assert out["shortfall"] == "无明显短板"
|
||||
|
||||
def test_it_never_invents_a_metric_the_watch_did_not_record(self):
|
||||
out = coach.rule_briefing(context_with())
|
||||
assert out["diagnosis"] == []
|
||||
assert out["actions"]
|
||||
|
||||
def test_the_headline_names_the_largest_departure(self):
|
||||
context = context_with(autonomicNervous={"restingHr": 80})
|
||||
context["deviations"] = [{
|
||||
"metric": "heartRate", "label": "静息心率", "unit": "bpm",
|
||||
"value": 80, "baselineMean": 60, "sd": 5, "baselineDays": 28,
|
||||
"z": 4.0, "verdict": "偏差",
|
||||
}]
|
||||
assert "静息心率" in coach.rule_briefing(context)["headline"]
|
||||
|
||||
def test_a_sustained_drop_in_steps_becomes_an_action(self):
|
||||
context = context_with()
|
||||
context["activityShift"] = {
|
||||
"steps": {"label": "步数", "recentMean": 4000,
|
||||
"priorMean": 10000, "changePct": -60.0}
|
||||
}
|
||||
assert any("60" in a for a in coach.rule_briefing(context)["actions"])
|
||||
|
||||
|
||||
# --- orchestration ----------------------------------------------------------
|
||||
@pytest.fixture
|
||||
def gateway(monkeypatch):
|
||||
monkeypatch.setenv("AI_GATEWAY_TOKEN", "test-token")
|
||||
monkeypatch.setenv("AI_GATEWAY_BASE_URL", "http://gateway.test/v1")
|
||||
monkeypatch.setenv("AI_MODEL_CHAIN", "gateway")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def month(db, user, seed_health):
|
||||
seed_health([
|
||||
{"date": f"2026-08-{d:02d}", "steps": 8000, "heart_rate": 60, "hrv": 45,
|
||||
"sleep_duration": 7, "sleep_quality": 80, "stress": 30}
|
||||
for d in range(1, 31)
|
||||
])
|
||||
return user
|
||||
|
||||
|
||||
def answer(monkeypatch, text):
|
||||
"""Make every model reply with `text`, and count the calls."""
|
||||
calls = []
|
||||
|
||||
def fake_chat(self, messages, timeout=None, max_tokens=None):
|
||||
calls.append(messages)
|
||||
return ai_svc.Completion(text, "nvidia")
|
||||
|
||||
monkeypatch.setattr(ai_svc.Provider, "chat", fake_chat, raising=False)
|
||||
monkeypatch.setattr(ai_svc.OpenAICompatProvider, "chat", fake_chat)
|
||||
return calls
|
||||
|
||||
|
||||
class TestGetBriefing:
|
||||
def test_no_data_says_so_rather_than_guessing(self, db, user, gateway):
|
||||
out = analysis_svc.get_briefing(user["id"])
|
||||
assert out["meta"]["source"] == "none"
|
||||
assert out["briefing"] is None
|
||||
|
||||
def test_blocking_mode_returns_the_model_answer(self, month, gateway, monkeypatch):
|
||||
answer(monkeypatch, json.dumps(GOOD_BRIEFING, ensure_ascii=False))
|
||||
out = analysis_svc.get_briefing(month["id"], wait=True)
|
||||
assert out["meta"]["source"] == "ai"
|
||||
assert out["briefing"]["status"] == "中等偏上"
|
||||
|
||||
def test_a_stored_answer_is_reused(self, month, gateway, monkeypatch):
|
||||
calls = answer(monkeypatch, json.dumps(GOOD_BRIEFING, ensure_ascii=False))
|
||||
analysis_svc.get_briefing(month["id"], wait=True)
|
||||
out = analysis_svc.get_briefing(month["id"])
|
||||
assert out["meta"]["cached"] is True
|
||||
assert len(calls) == 1, "the cached answer must not trigger a second call"
|
||||
|
||||
def test_new_health_data_expires_the_stored_answer(
|
||||
self, month, gateway, monkeypatch, seed_health
|
||||
):
|
||||
answer(monkeypatch, json.dumps(GOOD_BRIEFING, ensure_ascii=False))
|
||||
analysis_svc.get_briefing(month["id"], wait=True)
|
||||
seed_health([{"date": "2026-08-31", "steps": 12000}])
|
||||
out = analysis_svc.get_briefing(month["id"])
|
||||
assert out["meta"].get("cached") is not True
|
||||
|
||||
def test_a_failing_model_degrades_to_the_rule_engine(
|
||||
self, month, gateway, monkeypatch
|
||||
):
|
||||
def boom(self, messages, timeout=None, max_tokens=None):
|
||||
raise ai_svc.AIError("upstream down")
|
||||
|
||||
monkeypatch.setattr(ai_svc.OpenAICompatProvider, "chat", boom)
|
||||
out = analysis_svc.get_briefing(month["id"], wait=True)
|
||||
assert out["meta"]["source"] == "rules"
|
||||
assert out["briefing"] is not None
|
||||
|
||||
def test_the_non_blocking_path_answers_without_calling_a_model(
|
||||
self, month, gateway, monkeypatch
|
||||
):
|
||||
calls = answer(monkeypatch, json.dumps(GOOD_BRIEFING, ensure_ascii=False))
|
||||
monkeypatch.setattr(analysis_svc, "_run_in_background", lambda key, fn: True)
|
||||
out = analysis_svc.get_briefing(month["id"])
|
||||
assert out["meta"]["pending"] is True
|
||||
assert out["briefing"]["status"]
|
||||
assert calls == []
|
||||
|
||||
def test_one_generation_per_key_no_matter_how_often_it_is_polled(self):
|
||||
"""The poll runs every few seconds; a generation takes minutes."""
|
||||
started = []
|
||||
# A job that never finishes, so the key stays claimed across polls.
|
||||
blocked = analysis_svc.threading.Event()
|
||||
analysis_svc._run_in_background("test-key", lambda: (
|
||||
started.append(1), blocked.wait(5)
|
||||
))
|
||||
try:
|
||||
for _ in range(5):
|
||||
analysis_svc._run_in_background("test-key", lambda: started.append(1))
|
||||
assert len(started) == 1
|
||||
finally:
|
||||
blocked.set()
|
||||
|
||||
|
||||
class TestGetTrendInsight:
|
||||
def test_empty_span_is_reported_not_analysed(self, month, gateway):
|
||||
out = analysis_svc.get_trend_insight(
|
||||
month["id"], "steps", "2020-01-01", "2020-01-31"
|
||||
)
|
||||
assert out["meta"]["source"] == "none"
|
||||
|
||||
def test_model_answer_is_returned_and_cached(self, month, gateway, monkeypatch):
|
||||
reply = json.dumps({
|
||||
"summary": "步数稳定。", "drivers": [], "caution": None,
|
||||
"confidence": "medium",
|
||||
}, ensure_ascii=False)
|
||||
calls = answer(monkeypatch, reply)
|
||||
first = analysis_svc.get_trend_insight(
|
||||
month["id"], "steps", "2026-08-01", "2026-08-30"
|
||||
)
|
||||
second = analysis_svc.get_trend_insight(
|
||||
month["id"], "steps", "2026-08-01", "2026-08-30"
|
||||
)
|
||||
assert first["insight"]["summary"] == "步数稳定。"
|
||||
assert second["meta"]["cached"] is True
|
||||
assert len(calls) == 1
|
||||
|
||||
def test_a_failing_model_degrades_to_the_rule_engine(
|
||||
self, month, gateway, monkeypatch
|
||||
):
|
||||
def boom(self, messages, timeout=None, max_tokens=None):
|
||||
raise ai_svc.AIError("down")
|
||||
|
||||
monkeypatch.setattr(ai_svc.OpenAICompatProvider, "chat", boom)
|
||||
out = analysis_svc.get_trend_insight(
|
||||
month["id"], "steps", "2026-08-01", "2026-08-30"
|
||||
)
|
||||
assert out["meta"]["source"] == "rules"
|
||||
assert out["insight"]["summary"]
|
||||
|
||||
|
||||
class TestStreamChat:
|
||||
"""The gateway's streaming path is measurably less reliable than its
|
||||
blocking one, so a stream that produces nothing must not end the attempt."""
|
||||
|
||||
def test_deltas_are_forwarded(self, gateway, monkeypatch):
|
||||
def fake_stream(self, messages, timeout=None, max_tokens=None):
|
||||
yield ai_svc.Completion("你好", "nvidia")
|
||||
yield ai_svc.Completion("，世界", "nvidia")
|
||||
|
||||
monkeypatch.setattr(ai_svc.OpenAICompatProvider, "stream", fake_stream)
|
||||
text = "".join(d.text for d in ai_svc.stream_chat([{"role": "user", "content": "hi"}]))
|
||||
assert text == "你好，世界"
|
||||
|
||||
def test_a_failed_stream_retries_the_same_model_without_streaming(
|
||||
self, gateway, monkeypatch
|
||||
):
|
||||
def fake_stream(self, messages, timeout=None, max_tokens=None):
|
||||
raise ai_svc.AIError("所有模型均不可用")
|
||||
yield # pragma: no cover - generator marker
|
||||
|
||||
monkeypatch.setattr(ai_svc.OpenAICompatProvider, "stream", fake_stream)
|
||||
answer(monkeypatch, "完整回答")
|
||||
deltas = list(ai_svc.stream_chat([{"role": "user", "content": "hi"}]))
|
||||
assert "".join(d.text for d in deltas) == "完整回答"
|
||||
|
||||
def test_no_model_switch_once_text_has_been_sent(self, gateway, monkeypatch):
|
||||
def fake_stream(self, messages, timeout=None, max_tokens=None):
|
||||
yield ai_svc.Completion("半句", "nvidia")
|
||||
raise ai_svc.AIError("断流")
|
||||
|
||||
monkeypatch.setattr(ai_svc.OpenAICompatProvider, "stream", fake_stream)
|
||||
with pytest.raises(ai_svc.AIError):
|
||||
list(ai_svc.stream_chat([{"role": "user", "content": "hi"}]))
|
||||
|
||||
|
||||
class TestCopilotStream:
|
||||
def test_no_data_yields_an_error_event(self, db, user, gateway):
|
||||
events = list(analysis_svc.copilot_stream(user["id"], "我今天能练吗"))
|
||||
assert events[0][0] == "error"
|
||||
|
||||
def test_a_successful_answer_is_framed_start_delta_done(
|
||||
self, month, gateway, monkeypatch
|
||||
):
|
||||
def fake_stream(self, messages, timeout=None, max_tokens=None):
|
||||
yield ai_svc.Completion("可以。", "nvidia")
|
||||
|
||||
monkeypatch.setattr(ai_svc.OpenAICompatProvider, "stream", fake_stream)
|
||||
events = list(analysis_svc.copilot_stream(month["id"], "我今天能练吗"))
|
||||
assert [e for e, _ in events] == ["start", "delta", "done"]
|
||||
assert events[-1][1]["upstream"] == "nvidia"
|
||||
|
||||
|
||||
# --- endpoints --------------------------------------------------------------
|
||||
class TestEndpoints:
|
||||
def test_briefing_requires_auth(self, client):
|
||||
assert client.get("/api/analysis/briefing").status_code == 401
|
||||
|
||||
def test_trend_insight_requires_auth(self, client):
|
||||
assert client.get("/api/analysis/trend-insight").status_code == 401
|
||||
|
||||
def test_copilot_requires_auth(self, client):
|
||||
assert client.post("/api/analysis/copilot", json={}).status_code == 401
|
||||
|
||||
def test_briefing_answers_even_with_no_model_configured(self, client, auth, month):
|
||||
resp = client.get("/api/analysis/briefing", headers=auth)
|
||||
assert resp.status_code == 200
|
||||
assert resp.get_json()["briefing"] is not None
|
||||
|
||||
def test_trend_insight_rejects_an_unknown_metric(self, client, auth, month):
|
||||
resp = client.get(
|
||||
"/api/analysis/trend-insight",
|
||||
query_string={"metric": "nope", "startDate": "2026-08-01",
|
||||
"endDate": "2026-08-30"},
|
||||
headers=auth,
|
||||
)
|
||||
assert resp.status_code == 400
|
||||
assert "supported" in resp.get_json()
|
||||
|
||||
def test_trend_insight_requires_a_range(self, client, auth, month):
|
||||
resp = client.get(
|
||||
"/api/analysis/trend-insight",
|
||||
query_string={"metric": "steps"}, headers=auth,
|
||||
)
|
||||
assert resp.status_code == 400
|
||||
|
||||
def test_copilot_requires_a_question(self, client, auth, month):
|
||||
resp = client.post("/api/analysis/copilot", json={}, headers=auth)
|
||||
assert resp.status_code == 400
|
||||
|
||||
def test_copilot_streams_server_sent_events(
|
||||
self, client, auth, month, gateway, monkeypatch
|
||||
):
|
||||
def fake_stream(self, messages, timeout=None, max_tokens=None):
|
||||
yield ai_svc.Completion("可以，注意强度。", "nvidia")
|
||||
|
||||
monkeypatch.setattr(ai_svc.OpenAICompatProvider, "stream", fake_stream)
|
||||
resp = client.post(
|
||||
"/api/analysis/copilot",
|
||||
json={"question": "我今天能练吗"}, headers=auth,
|
||||
)
|
||||
assert resp.status_code == 200
|
||||
assert resp.mimetype == "text/event-stream"
|
||||
body = resp.get_data(as_text=True)
|
||||
assert "event: delta" in body
|
||||
assert "可以，注意强度。" in body
|
||||
|
||||
def test_briefing_never_leaks_the_gateway_token(
|
||||
self, client, auth, month, gateway, monkeypatch
|
||||
):
|
||||
# No real background generation: this suite must not reach the network.
|
||||
monkeypatch.setattr(analysis_svc, "_run_in_background", lambda key, fn: True)
|
||||
body = client.get("/api/analysis/briefing", headers=auth).get_data(as_text=True)
|
||||
assert "test-token" not in body
|
||||
|
||||
|
||||
class TestStreamRetryScope:
|
||||
"""The blind non-streaming retry is only worth doing for endpoints that
|
||||
actually have a separate streaming transport."""
|
||||
|
||||
def test_a_provider_without_streaming_is_not_called_twice(
|
||||
self, monkeypatch
|
||||
):
|
||||
monkeypatch.setenv("GEMINI_API_KEY", "k")
|
||||
monkeypatch.setenv("AI_MODEL_CHAIN", "gemini-flash")
|
||||
calls = []
|
||||
|
||||
def boom(self, messages, timeout=None, max_tokens=None):
|
||||
calls.append(1)
|
||||
raise ai_svc.AIError("down")
|
||||
|
||||
monkeypatch.setattr(ai_svc.GeminiProvider, "chat", boom)
|
||||
with pytest.raises(ai_svc.AIError):
|
||||
list(ai_svc.stream_chat([{"role": "user", "content": "hi"}]))
|
||||
assert len(calls) == 1
|
||||
|
||||
def test_the_gateway_declares_a_streaming_transport(self):
|
||||
assert ai_svc.CATALOG["gateway"].streaming is True
|
||||
assert ai_svc.CATALOG["gemini-flash"].streaming is False
|
||||
|
||||
|
||||
class TestRegenerate:
|
||||
def test_refresh_evicts_the_stored_answer_so_the_poll_can_see_the_new_one(
|
||||
self, month, gateway, monkeypatch
|
||||
):
|
||||
"""Without eviction the poll after 重新生成 reads the row it was asked
|
||||
to replace, reports `cached`, and stops — leaving the old text on
|
||||
screen."""
|
||||
answer(monkeypatch, json.dumps(GOOD_BRIEFING, ensure_ascii=False))
|
||||
analysis_svc.get_briefing(month["id"], wait=True)
|
||||
assert analysis_svc.get_briefing(month["id"])["meta"]["cached"] is True
|
||||
|
||||
monkeypatch.setattr(analysis_svc, "_run_in_background", lambda key, fn: True)
|
||||
analysis_svc.get_briefing(month["id"], refresh=True)
|
||||
|
||||
after = analysis_svc.get_briefing(month["id"])
|
||||
assert after["meta"].get("cached") is not True
|
||||
assert after["meta"]["pending"] is True
|
||||
Reference in New Issue
Block a user