Files
life-echo/api/tests/test_interview_reply_length.py
Kevin 69a673e6c6 feat(api): 访谈人格/回复长度策略、口述归一、背景语气与输入净稿全链路
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
2026-03-31 23:55:26 +08:00

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