"""执行单次评测 run 与整实验（供 Celery / 内联调试）。""" from __future__ import annotations from datetime import datetime, timezone from typing import Any from sqlalchemy.ext.asyncio import AsyncSession from app.core.config import settings from app.core.db import AsyncSessionLocal from app.core.dependencies import get_eval_judge_langchain_llm, get_llm_provider from app.core.logging import get_logger from app.features.evaluation import repo as eval_repo from app.features.evaluation.candidate_runner import ( EvalCandidateRunner, simple_memoir_from_transcript, ) from app.features.evaluation.gate_report_service import gate_result_to_details from app.features.evaluation.gating_service import compute_gate from app.features.evaluation.judge_service import EvalJudgeService from app.features.evaluation.models import EvalCase, EvalRun, EvalVersion logger = get_logger(__name__) _MAX_JUDGE_MARKDOWN_CHARS = 20_000 _MAX_EVAL_CHAPTERS = 30 _MAX_EVAL_STORIES = 40 _MAX_EVIDENCE_CONVERSATIONS = 8 _MAX_EVIDENCE_TRANSCRIPT_CHARS = 16_000 def _clip_md_for_judge(text: str, max_chars: int = _MAX_JUDGE_MARKDOWN_CHARS) -> str: s = (text or "").strip() if len(s) <= max_chars: return s return f"{s[:max_chars]}\n\n…（已截断供评审）" def _composite( conv: float | None, mem: float | None, weights: dict[str, Any] | None ) -> float: w = weights or {} wc = float(w.get("conversation", 0.5)) wm = float(w.get("memoir", 0.5)) c = float(conv or 0) m = float(mem or 0) return wc * c + wm * m def _utterances_for_case(case: EvalCase) -> list[str]: raw = case.user_utterances or [] return [str(u).strip() for u in raw if str(u).strip()] def _assistant_text_for_eval_display(raw: str) -> str: """评审与 transcript 展示：避免字面量 [SPLIT] 干扰 judge 阅读。""" return (raw or "").replace("[SPLIT]", "\n") def _trim_evidence_text(text: str, max_chars: int = _MAX_EVIDENCE_TRANSCRIPT_CHARS) -> str: s = (text or "").strip() if len(s) <= max_chars: return s return f"{s[:max_chars]}\n\n…（访谈证据已截断）" def _dialogue_transcript_from_pairs(pairs: list[tuple[str, str]]) -> str: parts: list[str] = [] for role, content in pairs: body = (content or "").strip() if not body: continue label = "用户" if role == "human" else "AI" out = _assistant_text_for_eval_display(body) if role != "human" else body parts.append(f"{label}: {out}") return "\n\n".join(parts) async def _conversation_transcript_for_eval( db: AsyncSession, conversation_id: str ) -> str: from app.features.conversation import repo as conversation_repo rows = await conversation_repo.get_conversation_messages(conversation_id, db) return _dialogue_transcript_from_pairs( [(str(row.role or "").lower(), str(row.content or "")) for row in rows] ) async def _user_transcript_evidence(db: AsyncSession, user_id: str) -> str: from app.features.conversation import repo as conversation_repo conversations = await conversation_repo.get_user_conversations(user_id, db) if not conversations: return "" parts: list[str] = [] for conv in reversed(conversations[:_MAX_EVIDENCE_CONVERSATIONS]): transcript = await _conversation_transcript_for_eval(db, str(conv.id)) if transcript: parts.append(f"## 会话 {str(conv.id)}\n{transcript}") return _trim_evidence_text("\n\n".join(parts)) async def execute_eval_run( db: AsyncSession, *, run: EvalRun, case: EvalCase, version: EvalVersion, ) -> None: if not settings.eval_execution_enabled: await eval_repo.update_run( db, run, status="failed", error_message="EVAL_EXECUTION_ENABLED=false", completed_at=datetime.now(timezone.utc), ) return utterances = _utterances_for_case(case) if not utterances: await eval_repo.update_run( db, run, status="failed", error_message="empty user_utterances", completed_at=datetime.now(timezone.utc), ) return await eval_repo.update_run( db, run, status="running", started_at=datetime.now(timezone.utc), error_message=None, ) await db.commit() provider_llm = getattr(get_llm_provider(), "langchain_llm", None) if provider_llm is None: await eval_repo.update_run( db, run, status="failed", error_message="生产 LLM 未配置", completed_at=datetime.now(timezone.utc), ) await db.commit() return judge_llm = get_eval_judge_langchain_llm() judge = EvalJudgeService(judge_llm) runner = EvalCandidateRunner(provider_llm) cfg = version.config_json if isinstance(version.config_json, dict) else None try: replies, latencies = await runner.replay_utterances( utterances, version_config=cfg, temperature=settings.eval_candidate_temperature, ) except Exception as e: logger.exception("eval replay failed: {}", e) await eval_repo.update_run( db, run, status="failed", error_message=str(e)[:2000], completed_at=datetime.now(timezone.utc), ) await db.commit() return transcript_parts: list[str] = [] for i, u in enumerate(utterances): if i >= len(replies): break transcript_parts.append( f"用户: {u}\nAI: {_assistant_text_for_eval_display(replies[i])}" ) prior = "" for idx, u in enumerate(utterances): if idx >= len(replies): break reply = _assistant_text_for_eval_display(replies[idx]) lat = latencies[idx] if idx < len(latencies) else None tj = await judge.judge_turn( prior_transcript=prior, user_utterance=u, assistant_reply=reply, ) scores = tj.model_dump() if tj else None rationale = tj.rationale if tj else None await eval_repo.add_turn( db, run_id=str(run.id), turn_index=idx, user_utterance=u, assistant_reply=replies[idx], duration_ms=lat, judge_scores_json=scores, judge_rationale=rationale, ) await db.commit() prior = (prior + f"\n用户: {u}\nAI: {reply}")[-8000:] full_transcript = "\n\n".join(transcript_parts) conv_out = await judge.judge_conversation(full_transcript=full_transcript) conv_total = conv_out.total_score if conv_out else None memoir_md = simple_memoir_from_transcript(utterances, replies) source_transcript = _trim_evidence_text(full_transcript) reference_memoir = (case.reference_memoir_markdown or "").strip() mem_out = await judge.judge_memoir( memoir_markdown=memoir_md, source_transcript=source_transcript, reference_memoir_markdown=reference_memoir, evidence_notes="严格按文档核对真实性、覆盖率、可追溯性；以原始访谈为主，参考基线仅作辅助。", ) chapter_entries: list[dict[str, Any]] = [] story_entries: list[dict[str, Any]] = [] uid = (case.source_user_id or "").strip() source_conversation_id = (case.source_conversation_id or "").strip() evidence_transcript = source_transcript if source_conversation_id: try: conversation_evidence = await _conversation_transcript_for_eval( db, source_conversation_id ) if conversation_evidence: evidence_transcript = _trim_evidence_text(conversation_evidence) except Exception as e: logger.warning("eval source conversation evidence skipped: {}", e) elif uid: try: user_evidence = await _user_transcript_evidence(db, uid) if user_evidence: evidence_transcript = user_evidence except Exception as e: logger.warning("eval user transcript evidence skipped: {}", e) if uid: from app.features.memoir.repo import get_chapters_for_memoir_list from app.features.story.repo import get_stories_for_user try: chapters = await get_chapters_for_memoir_list( uid, db, active_only=True, is_new_only=None ) for ch in chapters[:_MAX_EVAL_CHAPTERS]: body = (ch.canonical_markdown or "").strip() if not body: continue md = f"# 章节：{ch.title}\n\n{_clip_md_for_judge(body)}" cj = await judge.judge_memoir( memoir_markdown=md, source_transcript=evidence_transcript, reference_memoir_markdown=reference_memoir, evidence_notes=( "这是用户现有章节的严格评审；真实性、覆盖率、可追溯性必须对照原始访谈证据。" ), ) chapter_entries.append( { "id": ch.id, "title": ch.title, "order_index": ch.order_index, "judge": cj.model_dump() if cj else None, } ) except Exception as e: logger.warning("eval chapter judges skipped: {}", e) try: stories = await get_stories_for_user(db, uid, status="active") for st in stories[:_MAX_EVAL_STORIES]: body = (st.canonical_markdown or "").strip() if not body: continue md = f"# 故事：{st.title}\n\n{_clip_md_for_judge(body)}" sj = await judge.judge_memoir( memoir_markdown=md, source_transcript=evidence_transcript, reference_memoir_markdown=reference_memoir, evidence_notes=( "这是用户现有故事的严格评审；真实性、覆盖率、可追溯性必须对照原始访谈证据。" ), ) story_entries.append( { "id": st.id, "title": st.title, "stage": st.stage, "judge": sj.model_dump() if sj else None, } ) except Exception as e: logger.warning("eval story judges skipped: {}", e) mem_parts: list[float] = [] if mem_out is not None: mem_parts.append(float(mem_out.total_score)) for row in chapter_entries: j = row.get("judge") if isinstance(j, dict) and j.get("total_score") is not None: mem_parts.append(float(j["total_score"])) for row in story_entries: j = row.get("judge") if isinstance(j, dict) and j.get("total_score") is not None: mem_parts.append(float(j["total_score"])) mem_total = sum(mem_parts) / len(mem_parts) if mem_parts else None exp = await eval_repo.get_experiment(db, str(run.experiment_id)) weights = ( exp.composite_weights_json if exp and isinstance(exp.composite_weights_json, dict) else None ) comp = _composite(conv_total, mem_total, weights) bundle: dict[str, Any] = { "conversation_judge": conv_out.model_dump() if conv_out else None, "memoir_judge": mem_out.model_dump() if mem_out else None, "chapters": chapter_entries, "stories": story_entries, } await eval_repo.update_run( db, run, status="completed", memoir_markdown=memoir_md, conversation_score_total=conv_total, memoir_score_total=mem_total, composite_score=comp, judge_bundle_json=bundle, completed_at=datetime.now(timezone.utc), ) await db.commit() async def _finalize_experiment_gate(db: AsyncSession, experiment_id: str) -> None: runs = await eval_repo.list_runs_for_experiment(db, experiment_id) exp = await eval_repo.get_experiment(db, experiment_id) if not exp: return cases = await eval_repo.list_cases(db, str(exp.regression_set_id)) incomplete = [r for r in runs if str(r.status) not in ("completed", "failed")] if incomplete: return failed = [r for r in runs if str(r.status) == "failed"] if failed: await eval_repo.update_experiment( db, exp, status="failed", error_message="部分 run 失败", completed_at=datetime.now(timezone.utc), ) await db.commit() return gr = compute_gate(cases=cases, runs=runs) await eval_repo.upsert_gate_verdict( db, experiment_id=experiment_id, passed=gr.passed, mean_composite_delta=gr.mean_delta, protected_regressions_json=gr.protected_regressions, details_json=gate_result_to_details(gr), ) await eval_repo.update_experiment( db, exp, status="completed", completed_at=datetime.now(timezone.utc), ) await db.commit() async def execute_experiment_full(experiment_id: str) -> None: async with AsyncSessionLocal() as db: exp = await eval_repo.get_experiment(db, experiment_id) if not exp: return await eval_repo.update_experiment(db, exp, status="running") await db.commit() cases = await eval_repo.list_cases(db, str(exp.regression_set_id)) base_v = await eval_repo.get_version(db, str(exp.baseline_version_id)) cand_v = await eval_repo.get_version(db, str(exp.candidate_version_id)) if base_v is None or cand_v is None: await eval_repo.update_experiment( db, exp, status="failed", error_message="version 不存在", completed_at=datetime.now(timezone.utc), ) await db.commit() return for case in cases: for side, ver in ("baseline", base_v), ("candidate", cand_v): run = await eval_repo.get_run(db, experiment_id, str(case.id), side) if not run: run = await eval_repo.create_run( db, experiment_id=experiment_id, case_id=str(case.id), side=side, ) await db.commit() await execute_eval_run(db, run=run, case=case, version=ver) await _finalize_experiment_gate(db, experiment_id)