99543d04c6
- Merge internal-eval into development.sh (single Celery/infra); internal-eval.sh wraps with LIFE_ECHO_WITH_INTERNAL_EVAL; EVAL_ATTACH_ONLY for attaching 8001 when :8000 is already up; document in api/docs/internal-eval.md. - Evaluation: transcript_for_judge, judge error surfacing, rubric/schema tweaks, execution_service and router updates; tests for judge and composite eval. - Memory: ingest nested transaction for embedding/enrichment rollback safety. - Conversation WS: logger.exception for pipeline errors (avoid loguru KeyError). - app-eval-web: Playground saved replays, dialogue turns helper, hash user_id for Memoir; Memoir chapter baseline↔DB row compare with title heuristics; Stories page (#memoir-stories); Markdown + copy buttons; toolbar/panel UI; react-markdown; development proxy and fixture updates.
272 lines
9.1 KiB
Python
272 lines
9.1 KiB
Python
"""GLM 评审调用（结构化 JSON）。"""
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from __future__ import annotations
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from collections.abc import AsyncIterator
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from dataclasses import dataclass
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from typing import Any, Generic, TypeVar
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from app.core.llm_call import LLMCallError, allm_json_call
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from app.core.logging import get_logger
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from app.features.evaluation.judge_schemas import (
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ConversationJudgeOutput,
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MemoirJudgeOutput,
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TurnJudgeOutput,
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)
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from app.features.evaluation.rubrics.conversation_v1 import (
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COMPARE_CONV_STREAM_HINT,
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CONV_JUDGE_INSTRUCTIONS,
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TURN_JUDGE_INSTRUCTIONS,
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)
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from app.features.evaluation.rubrics.memoir_v1 import MEMOIR_JUDGE_INSTRUCTIONS
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logger = get_logger(__name__)
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TJudgeOutput = TypeVar(
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"TJudgeOutput", TurnJudgeOutput, ConversationJudgeOutput, MemoirJudgeOutput
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)
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_TURN_MAX = 768
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_CONV_MAX = 8192
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_CONV_JUDGE_JSON_MAX = 2048
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_MEMOIR_MAX = 12000
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_MEMOIR_JSON_MAX = 1536
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_COMPARE_STREAM_MAX = 6144
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_MEMOIR_EVIDENCE_MAX = 12000
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@dataclass(slots=True)
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class JudgeCallResult(Generic[TJudgeOutput]):
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output: TJudgeOutput | None
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error: str | None = None
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def _judge_error_message(e: LLMCallError) -> str:
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prefix = {
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"invoke": "模型调用失败",
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"decode": "JSON 解析失败",
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"validation": "结果校验失败",
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}.get(e.kind, "评审失败")
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detail = str(e).strip()
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return f"{prefix}: {detail}" if detail else prefix
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def _build_memoir_judge_prompt(
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*,
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memoir_markdown: str,
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source_transcript: str = "",
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reference_memoir_markdown: str = "",
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evidence_notes: str = "",
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) -> str:
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"""Assemble an evidence-aware memoir judging prompt."""
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source = (source_transcript or "").strip()
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reference = (reference_memoir_markdown or "").strip()
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notes = (evidence_notes or "").strip()
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sections = [
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MEMOIR_JUDGE_INSTRUCTIONS,
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"",
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"【证据与输入顺序】以下区块按优先级给出：评审说明（若有）→ 原始访谈证据 → 参考基线（若有）→ 待评成稿。**真实性相关细项必须以原始访谈证据为准。**",
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"",
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]
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if notes:
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sections.extend(["【评审说明】", notes[:1200], ""])
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if source:
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sections.extend(["【原始访谈/证据】", source[:_MEMOIR_EVIDENCE_MAX], ""])
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else:
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sections.extend(
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[
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"【原始访谈/证据】",
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"无可用原始访谈证据。对于记忆忠实度、事实准确性、事实覆盖率、记忆可追溯性，必须保守打分，不得凭空高分。",
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"",
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]
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)
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if reference:
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sections.extend(["【参考基线/导出成稿】", reference[:_MEMOIR_EVIDENCE_MAX], ""])
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sections.extend(["【当前回忆录正文】", memoir_markdown[:_MEMOIR_MAX]])
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return "\n".join(sections)
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class EvalJudgeService:
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def __init__(self, judge_llm: Any | None) -> None:
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self._llm = judge_llm
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async def judge_turn(
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self,
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*,
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prior_transcript: str,
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user_utterance: str,
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assistant_reply: str,
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turn_index_0: int = 0,
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) -> TurnJudgeOutput | None:
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if not self._llm:
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return None
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t = max(0, int(turn_index_0))
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prompt = f"""{TURN_JUDGE_INSTRUCTIONS}
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【本轮位置】完整对话中当前轮次为 Turn {t + 1}（与下方节选及全量 transcript 的 `[Turn ...]` 编号一致）。evidence_refs.turn_index 请使用该编号。
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【截至上一轮的对话节选】（含 `[Turn k]` 标签）
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{prior_transcript[:_CONV_MAX]}
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【本轮用户】
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{user_utterance[:4000]}
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【本轮 AI】
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{assistant_reply[:4000]}
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"""
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try:
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return await allm_json_call(
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self._llm,
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prompt,
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TurnJudgeOutput,
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max_tokens=_TURN_MAX,
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agent="EvalJudgeService.judge_turn",
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)
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except LLMCallError as e:
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logger.warning("turn judge failed: {}", e)
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return None
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async def judge_conversation_result(
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self, *, full_transcript: str
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) -> JudgeCallResult[ConversationJudgeOutput]:
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if not self._llm:
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return JudgeCallResult(output=None, error="评审模型未配置")
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prompt = f"""{CONV_JUDGE_INSTRUCTIONS}
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【完整对话】（每轮以 `[Turn k]` 开头）
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{full_transcript[:_CONV_MAX]}
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"""
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try:
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out = await allm_json_call(
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self._llm,
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prompt,
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ConversationJudgeOutput,
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max_tokens=_CONV_JUDGE_JSON_MAX,
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agent="EvalJudgeService.judge_conversation",
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)
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return JudgeCallResult(output=out)
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except LLMCallError as e:
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error = _judge_error_message(e)
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logger.warning("conversation judge failed: {}", error)
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return JudgeCallResult(output=None, error=error)
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async def judge_conversation(
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self, *, full_transcript: str
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) -> ConversationJudgeOutput | None:
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result = await self.judge_conversation_result(full_transcript=full_transcript)
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return result.output
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async def stream_conversation_compare(
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self,
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*,
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baseline_transcript: str,
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replay_transcript: str,
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baseline_judge: ConversationJudgeOutput | None,
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replay_judge: ConversationJudgeOutput | None,
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) -> AsyncIterator[str]:
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"""流式输出中文对比与建议（非 JSON）。"""
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if not self._llm:
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yield "[错误] 未配置评审模型 API Key（eval_judge_api_key / zhipu_api_key）"
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return
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b_tr = (baseline_transcript or "").strip()[:_CONV_MAX]
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r_tr = (replay_transcript or "").strip()[:_CONV_MAX]
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b_json = (
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baseline_judge.model_dump_json(ensure_ascii=False)
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if baseline_judge
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else "null"
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)
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r_json = (
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replay_judge.model_dump_json(ensure_ascii=False) if replay_judge else "null"
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)
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if baseline_judge and replay_judge:
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prompt = f"""你是访谈对话评测专家。下面给出两份完整对话 transcript 及各自的整体打分（JSON）。请用中文直接写正文（不要用 JSON、不要用 Markdown 代码块）：
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【A：导出基准对话】（历史快照：用户与当时导出的线上 AI，多轮合并为一篇）
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{b_tr}
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【B：本次回放/新测对话】（用户句与基准对齐，AI 为当前后端重新生成）
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{r_tr}
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【A 的整体评分 JSON】
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{b_json}
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【B 的整体评分 JSON】
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{r_json}
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请依次撰写：
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1) 两段对话在整体体验上的主要差异（情绪承接、信息挖掘、人物建模、访谈结构、提问质量、上下文与重复盘问等）；
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2) B 相对 A 的优点与不足；
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3) 若 B 在关键维度明显弱于 A，给出可操作的改进方向（系统提示、访谈策略、模型或温度等）。
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笔调简洁、偏执行清单。"""
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elif replay_judge:
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prompt = f"""{COMPARE_CONV_STREAM_HINT}
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【回放/新测 transcript】
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{r_tr}
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【整体评分 JSON】
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{r_json}
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"""
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else:
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yield "[错误] 缺少回放对话评分，无法生成建议"
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return
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llm = self._llm
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if hasattr(llm, "bind"):
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llm = llm.bind(max_tokens=_COMPARE_STREAM_MAX)
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try:
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async for chunk in llm.astream(prompt):
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piece = getattr(chunk, "content", None)
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if piece:
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yield piece
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except Exception as e:
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logger.warning("conversation compare stream failed: {}", e)
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yield f"\n\n[流式输出中断：{e}]"
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async def judge_memoir(
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self,
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*,
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memoir_markdown: str,
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source_transcript: str = "",
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reference_memoir_markdown: str = "",
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evidence_notes: str = "",
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) -> MemoirJudgeOutput | None:
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result = await self.judge_memoir_result(
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memoir_markdown=memoir_markdown,
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source_transcript=source_transcript,
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reference_memoir_markdown=reference_memoir_markdown,
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evidence_notes=evidence_notes,
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)
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return result.output
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async def judge_memoir_result(
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self,
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*,
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memoir_markdown: str,
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source_transcript: str = "",
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reference_memoir_markdown: str = "",
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evidence_notes: str = "",
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) -> JudgeCallResult[MemoirJudgeOutput]:
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if not self._llm:
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return JudgeCallResult(output=None, error="评审模型未配置")
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prompt = _build_memoir_judge_prompt(
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memoir_markdown=memoir_markdown,
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source_transcript=source_transcript,
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reference_memoir_markdown=reference_memoir_markdown,
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evidence_notes=evidence_notes,
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)
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try:
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out = await allm_json_call(
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self._llm,
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prompt,
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MemoirJudgeOutput,
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max_tokens=_MEMOIR_JSON_MAX,
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agent="EvalJudgeService.judge_memoir",
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)
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return JudgeCallResult(output=out)
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except LLMCallError as e:
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error = _judge_error_message(e)
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logger.warning("memoir judge failed: {}", error)
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return JudgeCallResult(output=None, error=error)
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