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
204 lines
6.3 KiB
Python
204 lines
6.3 KiB
Python
"""访谈回复长度策略:分桶与 InterviewAgent 的 max_tokens / 截断联动。"""
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from types import SimpleNamespace
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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from app.agents.chat.interview_reply_length import (
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ReplyLengthMode,
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bump_reply_length_strategy_for_background_voice,
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compute_reply_length_strategy,
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compute_reply_plan,
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)
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from app.agents.state_schema import MemoirStateSchema
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def _fake_settings(**overrides: object) -> SimpleNamespace:
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base = {
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"chat_interview_max_tokens": 380,
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"chat_interview_max_segments": 2,
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"chat_interview_max_chars_per_segment": 260,
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"chat_interview_brief_max_tokens": 260,
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"chat_interview_brief_max_chars_per_segment": 200,
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"chat_interview_expanded_max_tokens": 520,
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"chat_interview_expanded_max_chars_per_segment": 380,
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}
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base.update(overrides)
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return SimpleNamespace(**base)
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def test_strategy_brief_when_very_short() -> None:
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s = compute_reply_length_strategy(
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5,
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likely_new_detail=False,
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likely_chit_chat=False,
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settings=_fake_settings(),
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)
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assert s.mode == ReplyLengthMode.brief
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assert s.max_tokens == 260
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assert s.max_chars_per_segment == 200
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def test_strategy_standard_mid_length() -> None:
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s = compute_reply_length_strategy(
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50,
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likely_new_detail=True,
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likely_chit_chat=False,
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settings=_fake_settings(),
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)
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assert s.mode == ReplyLengthMode.standard
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assert s.max_tokens == 380
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assert s.max_chars_per_segment == 260
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def test_strategy_long_chit_stays_standard() -> None:
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s = compute_reply_length_strategy(
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120,
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likely_new_detail=False,
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likely_chit_chat=True,
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settings=_fake_settings(),
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)
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assert s.mode == ReplyLengthMode.standard
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assert s.max_tokens == 380
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def test_strategy_long_with_new_detail_expanded() -> None:
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s = compute_reply_length_strategy(
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120,
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likely_new_detail=True,
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likely_chit_chat=False,
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settings=_fake_settings(),
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)
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assert s.mode == ReplyLengthMode.expanded
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assert s.max_tokens == 520
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assert s.max_chars_per_segment == 380
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def test_strategy_boundary_len_20_brief_len_21_standard() -> None:
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a = compute_reply_length_strategy(
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20, likely_new_detail=False, likely_chit_chat=False, settings=_fake_settings()
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)
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b = compute_reply_length_strategy(
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21, likely_new_detail=False, likely_chit_chat=False, settings=_fake_settings()
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)
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assert a.mode == ReplyLengthMode.brief
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assert b.mode == ReplyLengthMode.standard
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def test_bump_standard_only_for_cadre_military() -> None:
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s0 = compute_reply_length_strategy(
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50,
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likely_new_detail=False,
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likely_chit_chat=False,
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settings=_fake_settings(),
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)
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bumped = bump_reply_length_strategy_for_background_voice(
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s0,
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background_voice="cadre",
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settings=_fake_settings(
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chat_interview_cadre_military_standard_extra_tokens=40,
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chat_interview_cadre_military_standard_extra_chars=40,
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),
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)
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assert bumped.max_tokens == s0.max_tokens + 40
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assert bumped.max_chars_per_segment == s0.max_chars_per_segment + 40
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brief = compute_reply_length_strategy(
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5,
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likely_new_detail=False,
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likely_chit_chat=False,
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settings=_fake_settings(
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chat_interview_cadre_military_standard_extra_tokens=40,
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chat_interview_cadre_military_standard_extra_chars=40,
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),
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)
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same = bump_reply_length_strategy_for_background_voice(
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brief,
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background_voice="military",
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settings=_fake_settings(
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chat_interview_cadre_military_standard_extra_tokens=40,
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chat_interview_cadre_military_standard_extra_chars=40,
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),
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)
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assert same.max_tokens == brief.max_tokens
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def test_plan_short_information_rich_is_standard_not_brief() -> None:
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"""短句但含高密度锚点(如「那年」「我爸」)→ standard,避免误压成 brief。"""
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p = compute_reply_plan(
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"那年我爸突然病了",
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background_voice=None,
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settings=_fake_settings(),
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)
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assert p.mode == ReplyLengthMode.standard
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assert p.information_rich is True
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def test_plan_long_chit_stays_standard_not_expanded() -> None:
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"""长段明显闲聊 → standard,不因字数进入 expanded。"""
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msg = "今天天气真好哈哈" * 11
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assert len(msg) >= 80
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p = compute_reply_plan(
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msg,
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background_voice=None,
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settings=_fake_settings(),
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)
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assert p.mode == ReplyLengthMode.standard
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assert p.likely_chit_chat is True
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def test_strategy_boundary_len_79_standard_len_80_long_branch() -> None:
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a = compute_reply_length_strategy(
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79, likely_new_detail=False, likely_chit_chat=False, settings=_fake_settings()
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)
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b = compute_reply_length_strategy(
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80,
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likely_new_detail=False,
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likely_chit_chat=False,
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settings=_fake_settings(),
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)
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assert a.mode == ReplyLengthMode.standard
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assert b.mode == ReplyLengthMode.standard
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@pytest.mark.asyncio
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async def test_interview_agent_passes_strategy_to_bind_and_truncate() -> None:
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"""同一套 strategy 用于 llm.bind(max_tokens=) 与 truncate_chat_segments。"""
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from app.agents.chat import interview_agent as ia
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mock_llm = MagicMock()
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mock_bound = MagicMock()
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mock_bound.ainvoke = AsyncMock(
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return_value=MagicMock(content="你好,后来呢?[SPLIT]还有吗?")
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)
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mock_llm.bind = MagicMock(return_value=mock_bound)
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agent = ia.InterviewAgent()
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agent.llm = mock_llm
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state = 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={"childhood": {}},
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)
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with patch(
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"app.agents.chat.interview_agent.get_history_messages",
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new=AsyncMock(return_value=[]),
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):
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turn = await agent.generate_response_with_state(
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conversation_id="c1",
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user_message="x" * 100 + "第一次认识他",
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memoir_state=state,
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)
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mock_llm.bind.assert_called_once()
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call_kw = mock_llm.bind.call_args[1]
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assert call_kw["max_tokens"] == 520
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assert len(turn.messages) >= 1
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for seg in turn.messages:
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assert len(seg) <= 380
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