53e0065e3e
配置 SSOT（TOML + .env） 统一错误契约 Auth 与事务边界 Redis / Celery 可靠性:业务 Redis（DB/0）与 Celery broker/backend（DB/1）显式拆分；连接池、sync client 可观测性（OpenTelemetry + LGTM）
273 lines
8.3 KiB
Python
273 lines
8.3 KiB
Python
"""将证据闭包格式化为评审可读文本，并记录截断/丢弃区块（可审计）。"""
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from __future__ import annotations
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from app.features.conversation.models import Segment
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from app.features.evaluation.constants import eval_cfg
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from app.features.evaluation.eval_trace_schemas import (
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ChapterEvidenceBundle,
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EvidenceFormatMeta,
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FormattedMemoirEvidence,
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StoryEvidenceBundle,
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)
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from app.features.memoir.constants import memoir
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from app.features.memory.models import (
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MemoryChunk,
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MemoryFact,
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MemorySummary,
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TimelineEvent,
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)
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def _memoir_evidence_char_cap() -> int:
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"""与 ``Settings.eval_judge_memoir_evidence_max_chars`` 对齐。"""
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return max(1000, int(eval_cfg.judge_memoir_evidence_max_chars))
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def _approx_tokens(chars: int) -> int:
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return max(0, chars // 4)
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def _segment_message_id_header(seg: Segment) -> str:
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um: str | None = None
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am: str | None = None
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lj = getattr(seg, "lineage_json", None)
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if isinstance(lj, dict):
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turns = lj.get("turns")
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if isinstance(turns, list) and turns:
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t0 = turns[0]
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if isinstance(t0, dict):
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um = str(t0.get("user_message_id") or "").strip() or None
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am = str(t0.get("assistant_message_id") or "").strip() or None
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if um is None:
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raw_um = getattr(seg, "user_message_id", None)
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if raw_um:
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um = str(raw_um)
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parts: list[str] = []
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if um:
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parts.append(f"user_msg={um}")
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if am:
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parts.append(f"assistant_msg={am}")
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return " · ".join(parts) if parts else ""
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def build_segment_transcript(
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segments: list[Segment],
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ai_by_segment: dict[str, str],
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) -> str:
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"""按 segment 绑定的局部访谈块（用户句 + AI 回复）。"""
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blocks: list[str] = []
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for i, seg in enumerate(segments, start=1):
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uid = str(seg.id)
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user_txt = (seg.user_input_text or "").strip()
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ai_txt = (ai_by_segment.get(uid) or seg.agent_response or "").strip()
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id_extra = _segment_message_id_header(seg)
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head = f"### Segment {i} · id={uid} · conversation={seg.conversation_id}" + (
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f" · {id_extra}" if id_extra else ""
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)
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body_u = f"用户: {user_txt}" if user_txt else "用户: （空）"
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body_a = f"AI: {ai_txt}" if ai_txt else "AI: （无日志/无 agent_response）"
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blocks.append(f"{head}\n{body_u}\n{body_a}")
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return "\n\n".join(blocks)
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def build_structured_evidence_text(
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*,
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chunks: list[MemoryChunk],
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facts: list[MemoryFact],
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events: list[TimelineEvent],
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summaries: list[MemorySummary],
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max_chars: int | None = None,
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) -> tuple[str, bool, list[str]]:
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"""
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结构化记忆证据块；返回 (text, truncated, dropped_section_tags)。
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"""
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cap = max_chars if max_chars is not None else _memoir_evidence_char_cap()
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parts: list[str] = []
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dropped: list[str] = []
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used = 0
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truncated = False
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def _add_section(title: str, body: str) -> None:
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nonlocal used, truncated
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block = f"{title}\n{body}".strip()
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if not block:
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return
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if used + len(block) + 2 > cap:
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truncated = True
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dropped.append(title.strip("【】").split("·")[0].strip())
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return
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parts.append(block)
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used += len(block) + 2
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if chunks:
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lines = []
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for c in chunks:
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snippet = (c.content or "").strip()
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if len(snippet) > 1200:
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snippet = snippet[:1200] + "…"
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lines.append(f"- chunk `{c.id}`: {snippet}")
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_add_section("【记忆片段 chunks】", "\n".join(lines))
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if facts:
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lines = []
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for f in facts:
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subj = (f.subject or "").strip()
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pred = (f.predicate or "").strip()
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lines.append(
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f"- fact `{f.id}` ({f.fact_type}): {subj} · {pred}".strip(" ·")
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)
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_add_section("【记忆事实 facts】", "\n".join(lines))
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if events:
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lines = []
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for e in events:
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lines.append(
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f"- timeline `{e.id}`: {e.title} ({e.event_year or e.event_date or ''})"
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)
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if e.description:
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desc = (e.description or "").strip()
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if len(desc) > 400:
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desc = desc[:400] + "…"
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lines.append(f" {desc}")
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_add_section("【时间线 timeline】", "\n".join(lines))
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if summaries:
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lines = []
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for s in summaries:
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body = (s.content or "").strip()
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if len(body) > 2000:
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body = body[:2000] + "…"
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lines.append(f"- summary `{s.id}` ({s.summary_type}): {body}")
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_add_section("【摘要 summaries】", "\n".join(lines))
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return "\n\n".join(parts).strip(), truncated, dropped
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def evidence_summary_line(
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*,
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segment_n: int,
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conv_n: int,
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chunk_n: int,
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fact_n: int,
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tl_n: int,
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sum_n: int,
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notes: list[str],
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) -> str:
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bits = [
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f"segments={segment_n}",
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f"conversations={conv_n}",
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f"chunks={chunk_n}",
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f"facts={fact_n}",
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f"timeline={tl_n}",
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f"summaries={sum_n}",
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]
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if notes:
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bits.append("notes=" + "; ".join(notes[:3]))
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return "; ".join(bits)
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def format_chapter_for_judge(
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bundle: ChapterEvidenceBundle,
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*,
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transcript: str,
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chunks: list[MemoryChunk],
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facts: list[MemoryFact],
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events: list[TimelineEvent],
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summaries: list[MemorySummary],
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) -> FormattedMemoirEvidence:
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ev_cap = _memoir_evidence_char_cap()
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dropped: list[str] = []
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truncated = False
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t_in = transcript.strip()
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if len(t_in) > ev_cap:
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truncated = True
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dropped.append("source_transcript_tail")
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t_in = t_in[:ev_cap] + "\n\n…（原始对话证据已截断）"
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struct, s_trunc, s_drop = build_structured_evidence_text(
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chunks=chunks,
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facts=facts,
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events=events,
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summaries=summaries,
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max_chars=ev_cap,
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)
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if s_trunc:
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truncated = True
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dropped.extend(s_drop)
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meta = EvidenceFormatMeta(
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truncated=truncated,
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dropped_sections=sorted(set(dropped)),
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included_token_estimate=_approx_tokens(len(t_in) + len(struct)),
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transcript_chars_included=len(t_in),
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structured_evidence_chars_included=len(struct),
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)
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summary = evidence_summary_line(
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segment_n=len(bundle.segment_ids),
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conv_n=len(bundle.conversation_ids),
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chunk_n=len(bundle.memory_chunk_ids),
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fact_n=len(bundle.memory_fact_ids),
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tl_n=len(bundle.timeline_event_ids),
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sum_n=len(bundle.summary_ids),
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notes=bundle.notes,
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)
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return FormattedMemoirEvidence(
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source_transcript=t_in,
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structured_evidence=struct,
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format_meta=meta,
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evidence_summary=summary,
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)
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def format_story_for_judge(
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bundle: StoryEvidenceBundle,
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*,
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transcript: str,
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chunks: list[MemoryChunk],
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facts: list[MemoryFact],
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events: list[TimelineEvent],
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summaries: list[MemorySummary],
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) -> FormattedMemoirEvidence:
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ev_cap = _memoir_evidence_char_cap()
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dropped: list[str] = []
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truncated = False
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t_in = transcript.strip()
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if len(t_in) > ev_cap:
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truncated = True
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dropped.append("source_transcript_tail")
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t_in = t_in[:ev_cap] + "\n\n…（原始对话证据已截断）"
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struct, s_trunc, s_drop = build_structured_evidence_text(
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chunks=chunks,
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facts=facts,
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events=events,
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summaries=summaries,
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max_chars=ev_cap,
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)
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if s_trunc:
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truncated = True
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dropped.extend(s_drop)
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meta = EvidenceFormatMeta(
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truncated=truncated,
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dropped_sections=sorted(set(dropped)),
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included_token_estimate=_approx_tokens(len(t_in) + len(struct)),
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transcript_chars_included=len(t_in),
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structured_evidence_chars_included=len(struct),
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)
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summary = evidence_summary_line(
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segment_n=len(bundle.segment_ids),
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conv_n=len(bundle.conversation_ids),
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chunk_n=len(bundle.memory_chunk_ids),
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fact_n=len(bundle.memory_fact_ids),
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tl_n=len(bundle.timeline_event_ids),
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sum_n=len(bundle.summary_ids),
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notes=bundle.notes,
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)
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return FormattedMemoirEvidence(
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source_transcript=t_in,
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structured_evidence=struct,
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format_meta=meta,
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evidence_summary=summary,
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)
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