life-echo/api/app/features/evaluation/execution_service.py

"""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)