5d04455664
The scheduler loop enqueues the analysis task when a realtime campaign completes; accelerated or cancelled campaigns and a missing analysis model skip silently. The campaign markdown export appends the analysis appendix (overall, problems, narratives, suggestions) when a completed analysis exists.
249 lines
8.5 KiB
Python
249 lines
8.5 KiB
Python
"""Unit tests for the pure renderers: dict in, HTML/Markdown/JSON out (no DB)."""
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import json
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from agenteval.evaluation.report_render import (
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render_campaign_markdown,
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render_html,
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render_json,
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render_markdown,
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)
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def _run_report(**overrides) -> dict:
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"""A hand-built report dict matching generate_report's shape."""
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report = {
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"run_id": "run-1",
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"target_id": "t-1",
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"target_name": "客服机器人",
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"scenario_id": "s-1",
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"scenario_name": "售后场景",
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"scenario_version": 2,
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"triggered_by": "manual",
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"status": "completed",
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"started_at": "2026-07-30T10:00:00+00:00",
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"completed_at": "2026-07-30T10:05:00+00:00",
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"summary": {
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"total_cases": 2,
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"passed_cases": 1,
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"failed_cases": 1,
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"total_rules": 3,
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"passed_rules": 2,
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"pass_rate": 0.5,
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"connectivity_cases": 1,
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"judged_pass_rate": 0.0,
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},
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"cases": [
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{
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"case_id": "case-a",
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"passed": True,
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"connectivity": True,
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"turns": [
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{
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"round": 0,
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"sent_text": "你好",
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"reply_text": "您好，请问有什么可以帮您？",
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"latency_ms": 120,
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"question_msg_id": "m-1",
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}
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],
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"results": [],
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},
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{
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"case_id": "case-b",
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"passed": False,
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"connectivity": False,
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"turns": [
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{
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"round": 0,
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"sent_text": "退货流程",
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"reply_text": None,
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"latency_ms": None,
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"question_msg_id": "m-2",
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}
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],
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"results": [
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{"rule_type": "keyword_match", "passed": False, "score": 0.0, "reason": "缺少关键词"},
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],
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},
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],
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}
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report.update(overrides)
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return report
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def _campaign_report() -> dict:
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"""A hand-built dict matching generate_campaign_report's shape."""
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return {
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"campaign_id": "c-1",
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"name": "夜间巡检",
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"target_id": "t-1",
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"status": "completed",
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"window_seconds": 7200,
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"time_scale": 1.0,
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"started_at": "2026-07-30T00:00:00+00:00",
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"completed_at": "2026-07-30T02:00:00+00:00",
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"summary": {
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"total_runs": 2,
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"completed_runs": 2,
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"overall_pass_rate": 0.75,
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"overall_availability": 1.0,
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"avg_latency_ms": 150.0,
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},
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"time_trend": [
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{
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"bucket_index": 0,
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"start_seconds": 0.0,
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"end_seconds": 3600.0,
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"run_count": 2,
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"pass_rate": 0.75,
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"availability": 1.0,
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"avg_latency_ms": 150.0,
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},
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{
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"bucket_index": 1,
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"start_seconds": 3600.0,
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"end_seconds": 7200.0,
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"run_count": 0,
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"pass_rate": None,
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"availability": None,
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"avg_latency_ms": None,
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},
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],
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"capability_summary": [
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{
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"scenario_id": "s-1",
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"scenario_name": "售后场景",
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"run_count": 2,
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"pass_rate": 0.75,
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"availability": 1.0,
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"avg_latency_ms": 150.0,
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},
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],
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}
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# ── render_html ─────────────────────────────────────────────────────────────
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def test_render_html_contains_names_and_summary():
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html = render_html(_run_report())
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assert "客服机器人" in html
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assert "售后场景" in html
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assert "50.00%" in html # pass_rate 0.5
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def test_render_html_contains_turns_and_rule_badges():
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html = render_html(_run_report())
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assert "退货流程" in html
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assert "keyword_match" in html
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assert "失败" in html
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# ── render_markdown ─────────────────────────────────────────────────────────
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def test_render_markdown_summary_table():
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md = render_markdown(_run_report())
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assert "| 总用例数 | 2 |" in md
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assert "| 通过率 | 50.0% |" in md
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assert "| 连通用例 | 1 |" in md
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def test_render_markdown_connectivity_badge_and_no_reply():
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md = render_markdown(_run_report())
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assert "🔗" in md # connectivity case badge
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assert "（连通用例，未配置判定标准）" in md
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assert "（无回复）" in md
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def test_render_markdown_judged_pass_rate_dash_when_none():
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report = _run_report()
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report["summary"]["judged_pass_rate"] = None
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md = render_markdown(report)
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assert "| 判定型通过率 | — |" in md
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# ── render_json ─────────────────────────────────────────────────────────────
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def test_render_json_roundtrips():
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report = _run_report()
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parsed = json.loads(render_json(report))
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assert parsed == report
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# ── render_campaign_markdown ────────────────────────────────────────────────
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def test_render_campaign_markdown_summary_and_axes():
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md = render_campaign_markdown(_campaign_report())
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assert "# 活动周期报告 — 夜间巡检" in md
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assert "| 整窗通过率 | 75.0% |" in md
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assert "## 时间趋势" in md
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assert "## 能力汇总" in md
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assert "| 售后场景 | 2 | 75.0% | 100.0% | 150ms |" in md
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def test_render_campaign_markdown_empty_bucket_dashes():
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md = render_campaign_markdown(_campaign_report())
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# bucket 1 has no runs: pass_rate/availability/latency all render as —
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assert "| 3600–7200 | 0 | — | — | — |" in md
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def _analysis() -> dict:
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"""A hand-built dict matching the stored campaign analysis result shape."""
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return {
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"overall": "整窗通过率偏低，售后场景拖后腿",
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"problems": [
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{
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"severity": "high",
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"title": "售后答非所问",
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"description": "多轮对话中反复偏离用户问题",
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"scenario_ids": ["s-1"],
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"evidence_run_ids": ["run-abc", "run-def"],
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},
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{
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"severity": "low",
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"title": "响应偏慢",
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"description": "高峰时段时延偏高",
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"scenario_ids": ["s-2"],
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"evidence_run_ids": [],
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},
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],
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"scenario_narratives": [
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{"scenario_id": "s-1", "narrative": "售后场景表现不稳定"},
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{"scenario_id": "s-2", "narrative": "售前场景表现稳定"},
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],
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"suggestions": [
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{"priority": 2, "text": "次要建议：扩容"},
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{"priority": 1, "text": "首要建议：补充售后知识库"},
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],
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}
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def test_render_campaign_markdown_appends_analysis_sections():
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md = render_campaign_markdown(
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_campaign_report(),
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analysis=_analysis(),
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scenario_names={"s-1": "售后场景", "s-2": "售前场景"},
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)
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assert "## 智能分析" in md
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assert "### 总体结论" in md
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assert "整窗通过率偏低，售后场景拖后腿" in md
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assert "### 问题诊断" in md
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assert "**[高] 售后答非所问**（场景：售后场景）" in md
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assert "`run-abc`" in md and "`run-def`" in md
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assert "**[低] 响应偏慢**（场景：售前场景）" in md
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assert "### 分场景叙述" in md
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assert "**售后场景**：售后场景表现不稳定" in md
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assert "### 改善建议" in md
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# 建议按 priority 升序
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assert md.index("首要建议") < md.index("次要建议")
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def test_render_campaign_markdown_analysis_falls_back_to_id_prefix():
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md = render_campaign_markdown(_campaign_report(), analysis=_analysis(), scenario_names={})
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assert "**[高] 售后答非所问**（场景：s-1）" in md
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def test_render_campaign_markdown_without_analysis_unchanged():
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md = render_campaign_markdown(_campaign_report())
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assert "智能分析" not in md
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