7eae6de52d
合并两个不可分割的深化： Phase 2 — 智能作业结算统一（ADR-0012） - intelligence_jobs.execute(job_kind, campaign_id, ...) 作为结算的 唯一实现：建行 → 认领 → 校验 → generating → 落账，一处编排、 一处截断（500 字符）。两个 executor 退化为 ensure_queued / validate / work_fn 三个小 adapter。 - analysis.validate_analysis_request() 共享校验入口（活动终态 → 模型），路由捕获映射 400、executor 捕获落 failed 行，与 validate_comparison_request 先例同构。 - campaign_runner._auto_start_analysis 的跳过守卫收敛至 auto_intelligence_eligible 单一判断点。 - comparison.py 删除零调用的 build_comparison_payload； load_comparison_view 投影归位至 campaign_read_model。 - 新增 characterization 测试（认领竞争、重复触发、截断、恢复上限）。 Phase 3 — storage/repository.py 拆分 - AsyncJobRepository 及两个子类迁至 storage/async_job_repository.py（Phase 2 的 intelligence_jobs 与 comparison 必须 import 自该路径，故与 Phase 2 同 commit）。 - ExplorationSession / ExplorationMessage 迁至 storage/exploration_repository.py；repository.py 由 1180 行降至 约 814 行，grep 确认无残留符号。 - exploration 子模块与路由 import 全部更新；测试 import 跟随。 刻意不做：CAS 共享原语、app.py 五 registry 关停顺序归一 （ADR-0006 精神，等真实需求出现再议）。
277 lines
11 KiB
Python
277 lines
11 KiB
Python
"""Period comparison — baseline pairing, metric diff and LLM narrative (周期对比).
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机械半边：计划指纹（同活动串的判据）、自动基线解析、两期报告的指标
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diff——全部确定性计算（ADR-0004 口径，经 ``generate_campaign_report`` 的
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既有聚合结果现算）。叙述半边：单次 LLM 调用，在两期已消化的分析 JSON 与
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机械 diff 之上产出结构化演进叙述（CONTEXT.md「周期对比」）。
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"""
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import json
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from datetime import datetime, timezone
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from typing import Any, Optional
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from sqlmodel import Session
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from agenteval.evaluation.analysis import ChatClient, resolve_analysis_model
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from agenteval.models import Campaign, CampaignStatus
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from agenteval.storage.async_job_repository import CampaignAnalysisRepository
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from agenteval.storage.db import utc_now
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from agenteval.storage.repository import CampaignRepository
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from agenteval.utils.llm import parse_json_from_llm_text
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_SAME_MOMENT_EPS = 1e-3
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_VALID_TRENDS = {"improving", "stable", "regressing"}
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_VALID_EVOLUTION_STATUSES = {"new", "persisting", "resolved"}
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_VALID_SUGGESTION_STATUSES = {"addressed", "partial", "unaddressed", "new"}
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class ComparisonError(RuntimeError):
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"""对比生成失败（数据缺失或模型输出无法解析），可重试。"""
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def campaign_plan_fingerprint(campaign: Campaign) -> str:
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"""活动串判据：评测对象 + 计划条目集合 + 窗口秒数（条目顺序无关）。
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指纹相等的活动构成同一活动串（CONTEXT.md「周期对比」）。档位
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（time_scale）不参与指纹——正式/加速由基线解析另行过滤。
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"""
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entries = sorted(
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((entry.scenario_id, entry.offset_seconds, entry.count) for entry in campaign.plan),
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key=lambda e: (e[0], e[1], e[2]),
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)
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return json.dumps(
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{"target": campaign.target_id, "window": campaign.window_seconds, "plan": entries},
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separators=(",", ":"),
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)
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def _to_utc(dt: Optional[datetime]) -> Optional[datetime]:
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if dt is None:
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return None
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return dt.replace(tzinfo=timezone.utc) if dt.tzinfo is None else dt
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def resolve_auto_baseline(campaign: Campaign, session: Session) -> Optional[Campaign]:
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"""自动基线：同活动串中最近一期已完成的正式线活动（须有 completed 分析）。
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条件（全部满足才可作基线）：指纹相等、双方均为正式线
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（time_scale == 1）、候选完成时间早于本期、候选已有 completed 分析。
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本期为加速线或无匹配候选时返回 None。
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"""
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if campaign.time_scale != 1:
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return None
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reference = _to_utc(campaign.completed_at) or utc_now()
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fingerprint = campaign_plan_fingerprint(campaign)
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best: Optional[Campaign] = None
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best_completed: Optional[datetime] = None
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for candidate in CampaignRepository(session).list_all():
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if candidate.id == campaign.id or candidate.time_scale != 1:
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continue
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if campaign_plan_fingerprint(candidate) != fingerprint:
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continue
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completed = _to_utc(candidate.completed_at)
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if completed is None:
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continue
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if (reference - completed).total_seconds() < _SAME_MOMENT_EPS:
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continue
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analysis_row = CampaignAnalysisRepository(session).get_by_campaign(candidate.id)
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if analysis_row is None or analysis_row.status != "completed":
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continue
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if best_completed is None or completed > best_completed:
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best = candidate
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best_completed = completed
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return best
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def _delta(base: Optional[float], current: Optional[float], ndigits: int) -> Optional[float]:
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if base is None or current is None:
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return None
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return round(current - base, ndigits)
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def compute_metric_diff(
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baseline_report: dict[str, Any],
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current_report: dict[str, Any],
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) -> dict[str, Any]:
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"""两期 ``generate_campaign_report`` 结果 → 整窗 + 分场景指标 delta。
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通过率/可用性按 4 位小数、时延按 1 位小数取 delta；任一侧指标为 None
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或该侧缺失该场景时 delta 为 None（只保留存在的 baseline/current 值）。
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纯函数，无 I/O。
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"""
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def _pair(base: Optional[float], current: Optional[float], ndigits: int) -> dict[str, Optional[float]]:
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return {"baseline": base, "current": current, "delta": _delta(base, current, ndigits)}
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base_summary = baseline_report.get("summary") or {}
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cur_summary = current_report.get("summary") or {}
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overall = {
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"pass_rate": _pair(base_summary.get("overall_pass_rate"), cur_summary.get("overall_pass_rate"), 4),
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"availability": _pair(base_summary.get("overall_availability"), cur_summary.get("overall_availability"), 4),
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"avg_latency_ms": _pair(base_summary.get("avg_latency_ms"), cur_summary.get("avg_latency_ms"), 1),
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}
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base_scenarios = {s.get("scenario_id"): s for s in baseline_report.get("capability_summary") or []}
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cur_scenarios = {s.get("scenario_id"): s for s in current_report.get("capability_summary") or []}
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scenarios = []
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for sid in sorted(set(base_scenarios) | set(cur_scenarios), key=lambda x: x or ""):
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base = base_scenarios.get(sid)
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cur = cur_scenarios.get(sid)
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entry: dict[str, Any] = {
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"scenario_id": sid,
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"scenario_name": (cur or base or {}).get("scenario_name") or "",
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"pass_rate": _pair(
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base.get("pass_rate") if base else None,
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cur.get("pass_rate") if cur else None,
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4,
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),
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"availability": _pair(
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base.get("availability") if base else None,
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cur.get("availability") if cur else None,
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4,
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),
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"avg_latency_ms": _pair(
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base.get("avg_latency_ms") if base else None,
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cur.get("avg_latency_ms") if cur else None,
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1,
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),
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}
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scenarios.append(entry)
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return {"overall": overall, "scenarios": scenarios}
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def validate_comparison_request(
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session: Session,
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campaign: Campaign,
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*,
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explicit_baseline_id: Optional[str] = None,
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) -> Campaign:
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"""共享校验入口：活动终态 → 模型 → 基线 → 分析。违规抛 ComparisonError。
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router 触发端点捕获映射 400；执行器捕获落 failed 行。校验顺序权威，
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两处不再漂移。
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"""
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if campaign.status in (CampaignStatus.PLANNED, CampaignStatus.RUNNING):
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raise ComparisonError("活动完成后才能生成周期对比")
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if resolve_analysis_model(campaign, session) is None:
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raise ComparisonError(
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"未配置分析模型：请在模型配置中心将某个 chat 配置设为「分析默认」，或为该活动指定分析模型"
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)
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current_analysis = CampaignAnalysisRepository(session).get_by_campaign(campaign.id)
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if current_analysis is None or current_analysis.status != "completed":
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raise ComparisonError("请先生成本期活动的智能分析")
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if explicit_baseline_id:
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baseline = CampaignRepository(session).get(explicit_baseline_id)
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if baseline is None:
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raise ComparisonError(f"基线活动不存在: {explicit_baseline_id}")
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else:
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baseline = resolve_auto_baseline(campaign, session)
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if baseline is None:
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raise ComparisonError("未找到自动基线：历史活动中没有同计划指纹且已完成分析的活动，可手动选择基线活动")
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baseline_analysis = CampaignAnalysisRepository(session).get_by_campaign(baseline.id)
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if baseline_analysis is None or baseline_analysis.status != "completed":
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raise ComparisonError("基线活动没有已完成的智能分析，请先生成基线活动的分析")
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return baseline
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# ── 叙述半边：单次 LLM 调用编排 ─────────────────────────────────────────
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async def narrate_period_comparison(
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*,
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baseline_analysis: dict[str, Any],
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current_analysis: dict[str, Any],
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metric_diff: dict[str, Any],
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valid_scenario_ids: set[str],
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chat_client: ChatClient,
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) -> dict[str, Any]:
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"""在两期分析结论与机械 diff 之上产出结构化演进叙述（单次调用）。
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输出 schema：trend / summary / problem_evolution / suggestion_tracking。
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模型虚构的 scenario_id 按白名单剔除；非法枚举归一（trend→stable、
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演化 status→persisting、建议 status→unaddressed）；解析失败抛
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``ComparisonError``（由调用方落 failed 状态）。
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"""
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system_prompt = (
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"你是智能客服质量评估平台的趋势研判专家，负责对照相邻两期评估活动的"
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"智能分析结论，产出结构化的演进叙述。只输出一个 JSON 对象："
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'{"trend": "improving|stable|regressing", '
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'"summary": "总体演进结论（一段话）", '
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'"problem_evolution": [{"status": "new|persisting|resolved", "title": "...", '
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'"detail": "该问题的演化说明", "scenario_ids": ["涉及场景 id"]}], '
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'"suggestion_tracking": [{"text": "上期建议原文", '
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'"status": "addressed|partial|unaddressed|new", "note": "落实情况的说明"}]}'
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"；problem_evolution 覆盖上期问题在本期的新增/持续/消解，"
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"suggestion_tracking 追踪上期每条建议的落实情况并列出本期新增建议。"
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"全部使用中文。"
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)
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user_prompt = json.dumps(
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{
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"上期分析": baseline_analysis,
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"本期分析": current_analysis,
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"指标变化（确定性计算，可信）": metric_diff,
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},
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ensure_ascii=False,
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)
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content = await chat_client(
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[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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]
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)
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try:
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parsed = parse_json_from_llm_text(content)
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except Exception as exc:
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raise ComparisonError(f"对比叙述输出解析失败: {exc}") from exc
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if not isinstance(parsed, dict):
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raise ComparisonError("对比叙述输出不是 JSON 对象")
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summary = parsed.get("summary")
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if not isinstance(summary, str) or not summary.strip():
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raise ComparisonError("对比叙述缺少 summary")
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trend = parsed.get("trend")
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if trend not in _VALID_TRENDS:
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trend = "stable"
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problem_evolution = []
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for item in parsed.get("problem_evolution") or []:
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if not isinstance(item, dict):
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continue
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status = item.get("status")
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problem_evolution.append(
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{
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"status": status if status in _VALID_EVOLUTION_STATUSES else "persisting",
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"title": str(item.get("title", "")),
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"detail": str(item.get("detail", "")),
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"scenario_ids": [s for s in item.get("scenario_ids") or [] if s in valid_scenario_ids],
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}
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)
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suggestion_tracking = []
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for item in parsed.get("suggestion_tracking") or []:
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if not isinstance(item, dict):
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continue
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status = item.get("status")
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suggestion_tracking.append(
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{
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"text": str(item.get("text", "")),
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"status": status if status in _VALID_SUGGESTION_STATUSES else "unaddressed",
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"note": str(item.get("note", "")),
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}
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)
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return {
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"trend": trend,
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"summary": summary,
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"problem_evolution": problem_evolution,
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"suggestion_tracking": suggestion_tracking,
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}
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