309a051038
数据库与模型：新增多版迁移（章节证据快照、对话血缘、记忆事实/时间线 lineage 等），把「成稿 ↔ 对话/记忆」的溯源信息落到表结构里。 业务链路：会话与 WS、回忆录/故事流水线、记忆写入与 enrichment 等跟着接上线索与快照；新增章节证据快照与评测侧 EvalTraceService 等模块，方便组评审用的证据包。 内部评测：自动化 run 与手工 memoir 评审共用可追溯证据；rubric/ judge 相关脚本与文档有配套调整。 app-eval-web：Memoir/实验详情里能展开看证据摘要与 evidence_trace（含对话轮次 id）；Vite 代理与 development.sh 注入的 API 端口与当前默认内部评测端口一致，避免改端口后页面连错服务。 工程杂项：GitHub Actions / 仓库说明有更新；各适配器与支付/配额/plan 等多处为小改动或跟随主改动的收尾；新增/扩充了?
332 lines
12 KiB
Python
332 lines
12 KiB
Python
"""智谱 GLM-5 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.config import settings
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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_JUDGE_JSON_MAX = 2048
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_CONV_HEADER = "【完整对话】（每轮以 `[Turn k]` 开头）\n\n"
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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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def _eval_judge_prompt_char_pool() -> int:
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"""整段请求的字符预算（由评审模型 context window 推导，保守）。"""
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toks = (
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settings.eval_judge_context_window_tokens
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- settings.eval_judge_completion_reserve_tokens
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- settings.eval_judge_prompt_budget_safety_tokens
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)
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toks = max(1, toks)
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return max(1, int(toks / settings.eval_judge_approx_tokens_per_char))
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def eval_judge_conversation_transcript_max_chars() -> int:
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"""整段对话评审：【完整对话】transcript 最大字符数。"""
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if settings.eval_judge_max_transcript_chars > 0:
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return settings.eval_judge_max_transcript_chars
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overhead = len(CONV_JUDGE_INSTRUCTIONS) + len(_CONV_HEADER) + 32
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return max(1, _eval_judge_prompt_char_pool() - overhead)
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def eval_judge_turn_prior_transcript_max_chars() -> int:
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"""逐轮评审：截至上一轮的 transcript 节选上限（含与用户/助手正文头的固定开销）。"""
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if settings.eval_judge_max_transcript_chars > 0:
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return settings.eval_judge_max_transcript_chars
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static = len(TURN_JUDGE_INSTRUCTIONS) + 8800
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return max(1, _eval_judge_prompt_char_pool() - static)
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def eval_judge_compare_transcript_each_max_chars() -> int:
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"""A/B 两段 transcript 同 prompt 时，每条 transcript 的上限（均分字符预算）。"""
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if settings.eval_judge_max_compare_transcript_chars_each > 0:
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return settings.eval_judge_max_compare_transcript_chars_each
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pool = _eval_judge_prompt_char_pool() - settings.eval_judge_compare_prompt_overhead_chars
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return max(1, pool // 2)
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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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structured_evidence: 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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struct = (structured_evidence 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："
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"评审说明（若有）→ 原始访谈/对话证据（segment 绑定）→ 结构化记忆证据（chunk/fact/timeline/summary）"
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"→ 参考基线（若有）→ 待评成稿。**真实性、覆盖率、可追溯性以「artifact 绑定证据闭包」为准**；"
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"若存在 `lineage_tier=fallback` 或证据不足，须保守打分并写 `insufficient_evidence`。",
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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/对话证据】", 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/对话证据】",
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"无可用局部对话证据。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 struct:
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sections.extend(
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["【结构化记忆证据】", struct[:_MEMOIR_EVIDENCE_MAX], ""]
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)
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else:
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sections.extend(
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[
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"【结构化记忆证据】",
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"（本 artifact 未绑定或未解析到 chunk/fact/timeline/summary 证据。）",
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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[: eval_judge_turn_prior_transcript_max_chars()]}
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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[: eval_judge_conversation_transcript_max_chars()]}
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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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cap_each = eval_judge_compare_transcript_each_max_chars()
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cap_single = eval_judge_conversation_transcript_max_chars()
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b_tr = (baseline_transcript or "").strip()[:cap_each]
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r_tr = (replay_transcript or "").strip()[:cap_each]
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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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r_one = (replay_transcript or "").strip()[:cap_single]
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prompt = f"""{COMPARE_CONV_STREAM_HINT}
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【回放/新测 transcript】
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{r_one}
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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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structured_evidence: 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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structured_evidence=structured_evidence,
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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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structured_evidence: 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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structured_evidence=structured_evidence,
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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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