- Add users.language_preference (Alembic 0018, default zh); capture at signup/SMS only; expose on auth and profile APIs - Lite English prompts for chat and memoir; localized stage labels and agent names (Life Echo / 岁月知己) - Tencent TTS: language-aware synthesis, ModelType=1 for 501004, English chunking - WebSocket pipeline: emit all AGENT_RESPONSE segments when TTS cancels; INFO logs for tts_this_turn and TTS decisions; on-demand TTS logging - Expo: device language on auth, i18n tiers/agent name, [SPLIT] streaming UX fixes - Tests for migration, prompts, pipeline, router tts_this_turn, reply segments Co-authored-by: Cursor <cursoragent@cursor.com>
186 lines
5.8 KiB
Python
186 lines
5.8 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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import pytest
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from app.agents.memoir.batch_phase1_prep import BatchPhase1SegmentRow
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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.stage_constants import CHAT_STAGES
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from app.agents.state_schema import DEFAULT_STAGE_ORDER, MemoirStateSchema
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def _empty_state() -> MemoirStateSchema:
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"""与生产默认一致的五阶段 stage_order(计划 §5-C 全量阶段管道覆盖)。"""
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assert list(CHAT_STAGES) == DEFAULT_STAGE_ORDER
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return MemoirStateSchema(
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stage_order=list(CHAT_STAGES),
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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_prepare_batches_batch_llm_path_matches_per_segment_skip_logic(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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monkeypatch.setattr(
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"app.agents.memoir.orchestrator.settings.memoir_phase1_batch_llm_enabled",
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True,
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)
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def fake_batch(
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segments: list,
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state: MemoirStateSchema,
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llm: object,
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*,
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chunk_size: int = 24,
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on_chunk=None,
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language: str = "zh",
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) -> dict:
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return {
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"mix-1": BatchPhase1SegmentRow(
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detected_stage="career",
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slots={},
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chapter_category_raw="none",
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),
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"mix-2": BatchPhase1SegmentRow(
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detected_stage="career",
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slots={"job": "戏剧演员"},
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chapter_category_raw="summary",
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),
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}
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monkeypatch.setattr(
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"app.agents.memoir.orchestrator.run_batch_phase1_prep_chunked",
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fake_batch,
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)
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orch = MemoirOrchestrator()
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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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