5972b0e721
- 回忆录细项上限收紧为合计 100 分，去掉 110 折算与 raw_dimension_total - judge_memoir 拼接原始访谈与可选导出基线；无证据时提示保守打真实性相关分 - 自动评测 run 与手动章节/故事评审统一带 transcript 证据（会话/用户聚合、截断） - 访谈打分仍为情绪强化版 15 细项、总分 100 - 评测台默认基准改为 zuckxu 导出 MD；移除逐轮用户句对齐表及相关逻辑 - 新增 judge schema 与 memoir prompt 组装的单元测试
425 lines
16 KiB
Python
425 lines
16 KiB
Python
"""手动触发 GLM 评审（不写 eval_runs）。"""
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from __future__ import annotations
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import re
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from collections.abc import AsyncIterator
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from typing import Any
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.dependencies import get_eval_judge_langchain_llm
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from app.core.logging import get_logger
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from app.features.conversation import repo as conversation_repo
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from app.features.evaluation.errors import (
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EvaluationBadRequestError,
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EvaluationNotFoundError,
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)
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from app.features.evaluation.execution_service import _assistant_text_for_eval_display
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from app.features.evaluation.judge_service import EvalJudgeService
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from app.features.evaluation.schemas import MemoirSectionBaselineOut
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from app.features.evaluation.session_catalog_service import SessionCatalogService
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from app.features.evaluation.user_export_fixtures import read_user_export_fixture
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from app.features.memoir.repo import get_chapters_for_memoir_list
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from app.features.story.repo import get_stories_for_user
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logger = get_logger(__name__)
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_MAX_JUDGE_MARKDOWN_CHARS = 20_000
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_MAX_EVAL_CHAPTERS = 30
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_MAX_EVAL_STORIES = 40
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_MAX_EVIDENCE_CONVERSATIONS = 8
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_MAX_EVIDENCE_TRANSCRIPT_CHARS = 16_000
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def _clip_md_for_judge(text: str, max_chars: int = _MAX_JUDGE_MARKDOWN_CHARS) -> str:
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s = (text or "").strip()
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if len(s) <= max_chars:
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return s
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return f"{s[:max_chars]}\n\n…（已截断供评审）"
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def _transcript_from_export_turns(turns: list[tuple[str, str]]) -> str:
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parts: list[str] = []
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for u, ai in turns:
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u = (u or "").strip()
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ai = (ai or "").strip()
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if u:
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parts.append(f"用户: {u}")
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if ai:
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parts.append(f"AI: {_assistant_text_for_eval_display(ai)}")
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return "\n\n".join(parts)
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def _trim_evidence_text(text: str, max_chars: int = _MAX_EVIDENCE_TRANSCRIPT_CHARS) -> str:
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s = (text or "").strip()
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if len(s) <= max_chars:
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return s
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return f"{s[:max_chars]}\n\n…（访谈证据已截断）"
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async def _conversation_transcript_for_manual(
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db: AsyncSession, conversation_id: str
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) -> str:
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rows = await conversation_repo.get_conversation_messages(conversation_id, db)
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parts: list[str] = []
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for row in rows:
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role = (row.role or "").lower()
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body = (row.content or "").strip()
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if not body:
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continue
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label = "用户" if role == "human" else "AI"
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out = _assistant_text_for_eval_display(body) if role != "human" else body
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parts.append(f"{label}: {out}")
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return "\n\n".join(parts)
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async def _user_transcript_evidence(db: AsyncSession, user_id: str) -> str:
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conversations = await conversation_repo.get_user_conversations(user_id, db)
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if not conversations:
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return ""
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parts: list[str] = []
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for conv in reversed(conversations[:_MAX_EVIDENCE_CONVERSATIONS]):
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transcript = await _conversation_transcript_for_manual(db, str(conv.id))
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if transcript:
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parts.append(f"## 会话 {str(conv.id)}\n{transcript}")
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return _trim_evidence_text("\n\n".join(parts))
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def _normalize_title_key(title: str) -> str:
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t = (title or "").strip().lower()
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t = re.sub(r"^#+\s*", "", t)
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return re.sub(r"\s+", " ", t)
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def _baseline_for_chapter_title(
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baselines: list[MemoirSectionBaselineOut],
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chapter_title: str,
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index: int,
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) -> MemoirSectionBaselineOut | None:
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if baselines:
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key = _normalize_title_key(chapter_title)
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for b in baselines:
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if _normalize_title_key(b.title) == key:
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return b
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if 0 <= index < len(baselines):
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return baselines[index]
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return None
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class EvalJudgeManualService:
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def __init__(self, db: AsyncSession) -> None:
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self._db = db
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async def judge_conversation(
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self,
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conversation_id: str,
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fixture_filename: str | None,
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) -> dict[str, Any]:
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cid = (conversation_id or "").strip()
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if not cid:
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raise EvaluationBadRequestError("conversation_id is required")
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catalog = SessionCatalogService(self._db)
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dialogue = await catalog.get_session_dialogue(cid)
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if not dialogue:
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raise EvaluationNotFoundError("conversation not found")
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parts: list[str] = []
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for m in dialogue.messages:
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r = (m.role or "").lower()
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label = "用户" if r == "human" else "AI"
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raw = m.content or ""
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out = _assistant_text_for_eval_display(raw) if r != "human" else raw
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parts.append(f"{label}: {out}")
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replay_transcript = "\n\n".join(parts)
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if not replay_transcript.strip():
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raise EvaluationBadRequestError("no messages to judge")
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fn = (fixture_filename or "").strip() or None
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baseline_transcript = ""
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if fn:
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try:
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turns, _ = read_user_export_fixture(fn)
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baseline_transcript = _transcript_from_export_turns(turns)
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except ValueError as e:
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raise EvaluationBadRequestError(str(e)) from e
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except FileNotFoundError as e:
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raise EvaluationNotFoundError("fixture not found") from e
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errors: list[str] = []
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judge_llm = get_eval_judge_langchain_llm()
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judge = EvalJudgeService(judge_llm)
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baseline_judge_dict: dict[str, Any] | None = None
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if baseline_transcript.strip():
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bj = await judge.judge_conversation(full_transcript=baseline_transcript)
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if bj:
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baseline_judge_dict = bj.model_dump()
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else:
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errors.append("baseline_glm_failed")
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elif fn:
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errors.append("baseline_transcript_empty")
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rj = await judge.judge_conversation(full_transcript=replay_transcript)
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replay_judge_dict = rj.model_dump() if rj else None
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if not rj:
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errors.append("replay_glm_failed")
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return {
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"conversation_id": cid,
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"fixture_filename": fn,
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"baseline_transcript": baseline_transcript,
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"replay_transcript": replay_transcript,
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"baseline_judge": baseline_judge_dict,
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"replay_judge": replay_judge_dict,
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"errors": errors,
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}
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async def iter_conversation_judge_sse(
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self,
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conversation_id: str,
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fixture_filename: str | None,
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) -> AsyncIterator[dict[str, Any]]:
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"""供 SSE：先整体基准分、再整体回放分，再流式对比与建议。"""
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cid = (conversation_id or "").strip()
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if not cid:
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yield {
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"event": "error",
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"phase": "validate",
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"message": "conversation_id is required",
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}
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return
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catalog = SessionCatalogService(self._db)
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dialogue = await catalog.get_session_dialogue(cid)
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if not dialogue:
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yield {
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"event": "error",
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"phase": "load",
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"message": "conversation not found",
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}
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return
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parts: list[str] = []
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for m in dialogue.messages:
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r = (m.role or "").lower()
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label = "用户" if r == "human" else "AI"
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raw = m.content or ""
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out = _assistant_text_for_eval_display(raw) if r != "human" else raw
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parts.append(f"{label}: {out}")
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replay_transcript = "\n\n".join(parts)
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if not replay_transcript.strip():
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yield {"event": "error", "phase": "load", "message": "no messages to judge"}
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return
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fn = (fixture_filename or "").strip() or None
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baseline_transcript = ""
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if fn:
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try:
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turns, _ = read_user_export_fixture(fn)
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baseline_transcript = _transcript_from_export_turns(turns)
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except ValueError as e:
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yield {"event": "error", "phase": "fixture", "message": str(e)}
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return
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except FileNotFoundError:
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yield {
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"event": "error",
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"phase": "fixture",
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"message": "fixture not found",
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}
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return
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judge_llm = get_eval_judge_langchain_llm()
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if not judge_llm:
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yield {
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"event": "error",
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"phase": "config",
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"message": "评审 LLM 未配置（eval_judge_api_key / zhipu_api_key）",
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}
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return
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judge = EvalJudgeService(judge_llm)
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yield {"event": "meta", "conversation_id": cid, "fixture_filename": fn}
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if not baseline_transcript.strip():
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yield {
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"event": "warning",
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"message": "未提供基准 MD 或基准无文本：仅对回放对话打分并输出单侧改进建议",
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}
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baseline_judge = None
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if baseline_transcript.strip():
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baseline_judge = await judge.judge_conversation(
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full_transcript=baseline_transcript
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)
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yield {
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"event": "baseline_judge",
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"ok": baseline_judge is not None,
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"judge": baseline_judge.model_dump() if baseline_judge else None,
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}
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if not baseline_judge:
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yield {
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"event": "error",
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"phase": "baseline_glm",
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"message": "基准整体打分失败（密钥、限流或 JSON 解析失败，见服务端日志）",
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}
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else:
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yield {
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"event": "baseline_judge",
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"ok": False,
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"skipped": True,
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"judge": None,
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}
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replay_judge = await judge.judge_conversation(full_transcript=replay_transcript)
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yield {
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"event": "replay_judge",
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"ok": replay_judge is not None,
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"judge": replay_judge.model_dump() if replay_judge else None,
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}
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if not replay_judge:
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yield {
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"event": "error",
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"phase": "replay_glm",
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"message": "回放对话整体 GLM 打分失败（空密钥、限流或 JSON 解析失败，见服务端日志）",
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}
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yield {"event": "done"}
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return
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async for piece in judge.stream_conversation_compare(
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baseline_transcript=baseline_transcript,
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replay_transcript=replay_transcript,
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baseline_judge=baseline_judge,
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replay_judge=replay_judge,
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):
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if piece:
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yield {"event": "compare_delta", "text": piece}
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yield {"event": "done"}
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async def judge_memoir_for_user(
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self,
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user_id: str,
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baseline_sections: list[MemoirSectionBaselineOut] | None,
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) -> dict[str, Any]:
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uid = (user_id or "").strip()
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if not uid:
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raise EvaluationBadRequestError("user_id is required")
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judge_llm = get_eval_judge_langchain_llm()
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judge = EvalJudgeService(judge_llm)
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baselines = list(baseline_sections or [])
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evidence_transcript = await _user_transcript_evidence(self._db, uid)
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chapter_results: list[dict[str, Any]] = []
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try:
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chapters = await get_chapters_for_memoir_list(
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uid, self._db, active_only=True, is_new_only=None
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)
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for i, ch in enumerate(chapters[:_MAX_EVAL_CHAPTERS]):
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body = (ch.canonical_markdown or "").strip()
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if not body:
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continue
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bl = _baseline_for_chapter_title(baselines, str(ch.title or ""), i)
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baseline_excerpt = ""
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if bl and (bl.body or "").strip():
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baseline_excerpt = _clip_md_for_judge(bl.body, max_chars=6000)
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md = f"# 章节：{ch.title}\n\n"
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if baseline_excerpt:
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md += f"## 导出基线（节选）\n\n{baseline_excerpt}\n\n"
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md += f"## 当前成稿\n\n{_clip_md_for_judge(body)}"
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cj = await judge.judge_memoir(
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memoir_markdown=md,
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source_transcript=evidence_transcript,
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reference_memoir_markdown=baseline_excerpt,
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evidence_notes=(
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"严格按文档打分；真实性、事实覆盖率、可追溯性必须优先对照该用户历史访谈证据。"
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),
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)
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chapter_results.append(
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{
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"id": ch.id,
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"title": ch.title,
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"order_index": ch.order_index,
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"baseline_title": bl.title if bl else None,
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"judge": cj.model_dump() if cj else None,
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}
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)
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except Exception as e:
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logger.warning("manual memoir chapter judges failed: {}", e)
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story_results: list[dict[str, Any]] = []
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try:
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stories = await get_stories_for_user(self._db, uid, status="active")
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for st in stories[:_MAX_EVAL_STORIES]:
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body = (st.canonical_markdown or "").strip()
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if not body:
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continue
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md = f"# 故事：{st.title}\n\n{_clip_md_for_judge(body)}"
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sj = await judge.judge_memoir(
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memoir_markdown=md,
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source_transcript=evidence_transcript,
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evidence_notes=(
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"严格按文档打分；真实性、事实覆盖率、可追溯性必须优先对照该用户历史访谈证据。"
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),
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)
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story_results.append(
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{
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"id": st.id,
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"title": st.title,
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"stage": st.stage,
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"judge": sj.model_dump() if sj else None,
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}
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)
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except Exception as e:
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logger.warning("manual memoir story judges failed: {}", e)
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return {
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"user_id": uid,
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"chapter_results": chapter_results,
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"story_results": story_results,
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}
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async def memoir_snapshot(self, user_id: str) -> dict[str, Any]:
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uid = (user_id or "").strip()
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if not uid:
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raise EvaluationBadRequestError("user_id is required")
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chapters_out: list[dict[str, Any]] = []
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stories_out: list[dict[str, Any]] = []
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try:
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chapters = await get_chapters_for_memoir_list(
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uid, self._db, active_only=True, is_new_only=None
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)
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for ch in chapters[:_MAX_EVAL_CHAPTERS]:
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chapters_out.append(
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{
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"id": ch.id,
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"title": ch.title,
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"category": ch.category,
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"order_index": ch.order_index,
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"canonical_markdown": ch.canonical_markdown,
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}
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)
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except Exception as e:
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logger.warning("memoir snapshot chapters failed: {}", e)
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try:
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stories = await get_stories_for_user(self._db, uid, status="active")
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for st in stories[:_MAX_EVAL_STORIES]:
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stories_out.append(
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{
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"id": st.id,
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"title": st.title,
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"stage": st.stage,
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"canonical_markdown": st.canonical_markdown,
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}
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)
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except Exception as e:
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logger.warning("memoir snapshot stories failed: {}", e)
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return {
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"user_id": uid,
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"chapters": chapters_out,
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"stories": stories_out,
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}
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