66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
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"""可追溯评测：Chapter/Story 证据闭包模型（strict / partial / fallback）。"""
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from __future__ import annotations
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from typing import Literal
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from pydantic import BaseModel, Field
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LineageTier = Literal["strict", "partial", "fallback"]
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class EvidenceFormatMeta(BaseModel):
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"""Prompt 裁剪与可审计元数据（formatter 产出）。"""
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truncated: bool = False
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dropped_sections: list[str] = Field(default_factory=list)
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included_token_estimate: int = 0
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transcript_chars_included: int = 0
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structured_evidence_chars_included: int = 0
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class ChapterEvidenceBundle(BaseModel):
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"""章节评测证据闭包（Phase A：source_segments 为主链，标记 partial）。"""
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user_id: str
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chapter_id: str
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segment_ids: list[str] = Field(default_factory=list)
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conversation_ids: list[str] = Field(default_factory=list)
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memory_chunk_ids: list[str] = Field(default_factory=list)
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memory_fact_ids: list[str] = Field(default_factory=list)
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timeline_event_ids: list[str] = Field(default_factory=list)
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summary_ids: list[str] = Field(default_factory=list)
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lineage_tier: LineageTier
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notes: list[str] = Field(default_factory=list)
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augmented_with_chapter_context: bool = False
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# DialogueLineage.model_dump() — user/assistant message ids (multi-turn)
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dialogue_lineage: dict | None = None
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class StoryEvidenceBundle(BaseModel):
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"""故事评测证据闭包：StoryEvidenceLink 优先，缺失则回退 chapter source_segments。"""
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user_id: str
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story_id: str
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segment_ids: list[str] = Field(default_factory=list)
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conversation_ids: list[str] = Field(default_factory=list)
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memory_chunk_ids: list[str] = Field(default_factory=list)
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memory_fact_ids: list[str] = Field(default_factory=list)
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timeline_event_ids: list[str] = Field(default_factory=list)
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summary_ids: list[str] = Field(default_factory=list)
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lineage_tier: LineageTier
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notes: list[str] = Field(default_factory=list)
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augmented_with_chapter_context: bool = False
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story_link_evidence_count: int = 0
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fallback_chapter_ids: list[str] = Field(default_factory=list)
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dialogue_lineage: dict | None = None
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class FormattedMemoirEvidence(BaseModel):
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"""供 `EvalJudgeService` 组 prompt 的格式化结果 + 汇总说明。"""
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source_transcript: str = ""
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structured_evidence: str = ""
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format_meta: EvidenceFormatMeta = Field(default_factory=EvidenceFormatMeta)
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evidence_summary: str = ""
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