69a673e6c6
Chat 访谈 - 新增 persona 系统（default / warm_listener / curious_guide）与 background_voice 语气层 - 回复长度由 compute_reply_plan 统一决策（brief / standard / expanded），融合信息密度启发式 - 输入净稿（input_normalize）：编排层可选 rules/llm 归一用户口语后再喂模型与记忆检索 - 记忆证据注入：按用户话检索 memory evidence 并注入 prompt Memoir 回忆录 - 口述归一（oral_normalize）：segment 原文保留，story 管线取派生净稿作叙事输入 - segment 入队批次门闸：累计字数 + 最长等待秒数，减少零碎提交 - fidelity_check / prompts / narrative_agent 微调 - Alembic 0005：清理跨章节 story 外键 Infra - Dockerfile 加入 ffmpeg - pyproject.toml 新增依赖并同步 uv.lock - .env.example / .env.production 补全新配置项 Tests - 新增 test_background_voice、test_chat_input_normalize、test_experience_regressions - 扩展 test_interview_prompts、test_interview_reply_length、test_story_route_oral_invariant Made-with: Cursor
123 lines
3.9 KiB
Python
123 lines
3.9 KiB
Python
"""回忆录：segment_skip_story_ids 与 batch 级短路条件（orchestrator 侧）。"""
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from types import SimpleNamespace
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from unittest.mock import MagicMock
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from app.agents.memoir.classification_agent import (
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ChapterClassifyResult,
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ClassificationAgent,
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)
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from app.agents.memoir.extraction_agent import ExtractionResult
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from app.agents.memoir.orchestrator import MemoirOrchestrator
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from app.agents.state_schema import MemoirStateSchema
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def _empty_state() -> MemoirStateSchema:
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return MemoirStateSchema(
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stage_order=["childhood"],
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current_stage="childhood",
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covered_stages=[],
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slots={},
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)
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def test_prepare_batches_skip_story_id_when_llm_none_and_empty_slots() -> None:
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orch = MemoirOrchestrator()
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orch.extraction_agent.extract = MagicMock(
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return_value=ExtractionResult(detected_stage="career", slots={})
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)
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orch.classification_agent.classify = MagicMock(
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return_value=ChapterClassifyResult(category="summary", llm_said_none=True)
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)
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st = _empty_state()
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def get_state() -> MemoirStateSchema:
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return st
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def update_slot(
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stage: str, slot_name: str, snippet: str, seg_ids: list[str]
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) -> MemoirStateSchema:
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return st
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seg = SimpleNamespace(id="seg-skip-1", user_input_text="聊聊别的吧")
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p = orch.prepare_batches(
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segments=[seg],
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llm=MagicMock(),
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get_or_create_state=get_state,
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update_slot=update_slot,
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)
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assert "seg-skip-1" in p.segment_skip_story_ids
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def test_prepare_batches_fragment_heuristic_not_in_skip_set() -> None:
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"""fragment-only→summary 且 llm_said_none=False，不进入 skip 集合。"""
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orch = MemoirOrchestrator()
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orch.extraction_agent.extract = MagicMock(
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return_value=ExtractionResult(detected_stage="career", slots={})
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)
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orch.classification_agent = ClassificationAgent()
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st = _empty_state()
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def get_state() -> MemoirStateSchema:
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return st
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def update_slot(
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stage: str, slot_name: str, snippet: str, seg_ids: list[str]
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) -> MemoirStateSchema:
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return st
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seg = SimpleNamespace(id="seg-frag-1", user_input_text="1999年出生")
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p = orch.prepare_batches(
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segments=[seg],
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llm=None,
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get_or_create_state=get_state,
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update_slot=update_slot,
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)
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assert "seg-frag-1" not in p.segment_skip_story_ids
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def test_prepare_batches_mixed_batch_only_one_segment_in_skip_set() -> None:
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"""同 category 两段：仅一段满足 skip 条件 → skip 集合仅含该段 id。"""
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orch = MemoirOrchestrator()
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orch.extraction_agent.extract = MagicMock(
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side_effect=[
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ExtractionResult(detected_stage="career", slots={}),
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ExtractionResult(detected_stage="career", slots={"job": "戏剧演员"}),
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]
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)
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orch.classification_agent.classify = MagicMock(
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side_effect=[
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ChapterClassifyResult(category="summary", llm_said_none=True),
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ChapterClassifyResult(category="summary", llm_said_none=False),
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]
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)
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st = _empty_state()
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def get_state() -> MemoirStateSchema:
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return st
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def update_slot(
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stage: str, slot_name: str, snippet: str, seg_ids: list[str]
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) -> MemoirStateSchema:
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return st
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s1 = SimpleNamespace(id="mix-1", user_input_text="聊聊别的吧")
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s2 = SimpleNamespace(id="mix-2", user_input_text="后来当了演员")
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p = orch.prepare_batches(
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segments=[s1, s2],
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llm=MagicMock(),
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get_or_create_state=get_state,
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update_slot=update_slot,
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)
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assert p.segment_skip_story_ids == {"mix-1"}
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assert len(p.category_to_segments.get("summary", [])) == 2
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def test_batch_all_skip_predicate() -> None:
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"""memoir_tasks 短路条件：batch_ids <= skip_ids。"""
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batch_ids = {"a", "b"}
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skip_ids = {"a"}
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assert not (batch_ids <= skip_ids)
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assert {"a"} <= {"a", "b"}
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assert {"a", "b"} <= {"a", "b"}
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