refactor(eval+memoir)：精简内部评测路由与服务，composite/对话摘要与 judge 能力补强
- 访谈：新增 interview_state_hints，联动 orchestrator 与提示词 - 回忆录：story_pipeline_sync/state/memory/post_commit 与 Celery 任务调整 - 基建：开发用 celery broker、compose/development 脚本、依赖注入 - eval-web：移除数据集/实验/版本等页面与流式轮询，突出 Playground - 文档与单测同步
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Kevin
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api/tests/test_memoir_pipeline_optimization.py
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api/tests/test_memoir_pipeline_optimization.py
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"""Validation tests for memoir pipeline optimization (Phase A/B/C).
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Tests:
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- Phase1 batch path is now the default
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- Memory enrichment is dispatched asynchronously
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- Unified narrative unit executor produces correct results
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- Post-commit fan-out includes quality pass
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- Quality pass task handles title polishing
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"""
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from __future__ import annotations
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from unittest.mock import MagicMock, patch
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import pytest
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from app.agents.memoir.extraction_agent import ExtractionResult
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from app.agents.memoir.classification_agent import ChapterClassifyResult
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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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# ---------------------------------------------------------------------------
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# Phase1 batch path defaults
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# ---------------------------------------------------------------------------
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def test_phase1_batch_enabled_by_default() -> None:
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"""memoir_phase1_batch_llm_enabled should default to True after optimization."""
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from app.core.config import Settings
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s = Settings()
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assert s.memoir_phase1_batch_llm_enabled is True
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def test_quality_pass_enabled_by_default() -> None:
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from app.core.config import Settings
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s = Settings()
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assert s.memoir_quality_pass_enabled is True
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# ---------------------------------------------------------------------------
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# Phase1 orchestrator selects batch path when available
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# ---------------------------------------------------------------------------
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def test_orchestrator_tries_batch_first(monkeypatch: pytest.MonkeyPatch) -> None:
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"""When batch LLM is enabled and LLM is available, batch path should be attempted."""
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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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orch = MemoirOrchestrator()
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batch_called = {"flag": False}
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def fake_batch(*args, **kwargs):
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batch_called["flag"] = True
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return MagicMock(
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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={},
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),
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category_to_segments={},
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segment_skip_story_ids=set(),
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segment_chapter_category={},
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)
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orch._prepare_batches_via_batch_llm = fake_batch
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class _Seg:
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def __init__(self, sid: str) -> None:
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self.id = sid
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self.user_input_text = "test"
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st = 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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orch.prepare_batches(
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segments=[_Seg("s1")],
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llm=MagicMock(),
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llm_fast=MagicMock(),
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get_or_create_state=lambda: st,
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update_slot=lambda *a: st,
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)
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assert batch_called["flag"] is True
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def test_orchestrator_fallback_to_sequential(monkeypatch: pytest.MonkeyPatch) -> None:
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"""If batch path raises, should fall back to sequential extraction."""
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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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orch = MemoirOrchestrator()
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def fail_batch(*args, **kwargs):
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raise RuntimeError("batch LLM unavailable")
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orch._prepare_batches_via_batch_llm = fail_batch
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orch.extraction_agent.extract = MagicMock(
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return_value=ExtractionResult(detected_stage="childhood", slots={"toy": "ball"})
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)
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orch.classification_agent.classify = MagicMock(
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return_value=ChapterClassifyResult(category="childhood", llm_said_none=False)
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)
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st = 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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class _Seg:
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def __init__(self, sid: str, text: str) -> None:
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self.id = sid
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self.user_input_text = text
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result = orch.prepare_batches(
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segments=[_Seg("s1", "我小时候玩球")],
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llm=MagicMock(),
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llm_fast=MagicMock(),
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get_or_create_state=lambda: st,
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update_slot=lambda *a: st,
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)
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assert "s1" in result.segment_chapter_category
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# ---------------------------------------------------------------------------
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# Memory enrichment decoupled from ingest
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# ---------------------------------------------------------------------------
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def test_ingest_transcript_sync_no_longer_calls_enrichment_inline() -> None:
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"""After decoupling, ingest_transcript_sync should NOT import enrichment inline."""
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import inspect
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from app.features.memory.service import ingest_transcript_sync
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source = inspect.getsource(ingest_transcript_sync)
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assert "enrich_memory_after_ingest_sync" not in source
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assert "enrich_memory_source" in source
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# ---------------------------------------------------------------------------
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# Post-commit unified fan-out
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# ---------------------------------------------------------------------------
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def test_post_commit_result_includes_quality_pass() -> None:
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"""PostCommitResult should have quality_pass_scheduled field."""
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from app.features.story.post_commit import PostCommitResult
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r = PostCommitResult()
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assert hasattr(r, "quality_pass_scheduled")
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assert r.quality_pass_scheduled is False
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def test_post_commit_signature_accepts_quality_pass() -> None:
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"""enqueue_story_post_commit_effects should accept need_quality_pass kwarg."""
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import inspect
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from app.features.story.post_commit import enqueue_story_post_commit_effects
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sig = inspect.signature(enqueue_story_post_commit_effects)
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assert "need_quality_pass" in sig.parameters
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assert "memoir_correlation_id" in sig.parameters
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# ---------------------------------------------------------------------------
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# resolve_append_target
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# ---------------------------------------------------------------------------
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def test_resolve_append_target_forced_new_on_overflow() -> None:
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"""When canonical exceeds limit, should force new story."""
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from app.features.memoir.story_pipeline_sync import _resolve_append_target
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session = MagicMock()
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big_story = MagicMock()
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big_story.user_id = "u1"
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big_story.id = "story-1"
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big_story.canonical_markdown = "x" * 200_000
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session.get.return_value = big_story
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with patch(
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"app.features.memoir.story_pipeline_sync.count_story_versions_sync",
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return_value=1,
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):
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tid, existing, dsrc = _resolve_append_target(
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session,
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route_decision="append_story",
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route_target_story_id="story-1",
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user_id="u1",
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chapter_category="childhood",
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oral_norm="short text",
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candidate_stories=[],
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story_meta={},
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decision_source="test",
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memoir_correlation_id=None,
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)
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assert tid is None
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assert dsrc == "forced_new_due_to_append_limit"
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# ---------------------------------------------------------------------------
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# _run_post_pipeline_commit helper
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# ---------------------------------------------------------------------------
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def test_run_post_pipeline_commit_calls_post_commit() -> None:
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"""Shared helper should call enqueue_story_post_commit_effects."""
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from app.tasks.memoir_tasks import _run_post_pipeline_commit
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with patch(
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"app.features.story.post_commit.enqueue_story_post_commit_effects"
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) as mock_pc, patch(
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"app.features.memoir.memoir_images.settings.MemoirImageSettings"
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) as mock_img:
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mock_pc.return_value = MagicMock(
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enqueued_story_image_count=0,
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enqueued_chapter_recompose_count=0,
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compaction_scheduled=False,
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quality_pass_scheduled=True,
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errors=[],
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)
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mock_img.from_env.return_value = MagicMock(enabled=False)
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_run_post_pipeline_commit(
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user_id="u1",
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story_dispatch_ids={"s1"},
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recompose_chapter_ids={"c1"},
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cover_chapter_ids=set(),
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trigger_source="test",
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need_compaction=False,
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need_quality_pass=True,
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memoir_correlation_id="cid-1",
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)
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mock_pc.assert_called_once()
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call_kwargs = mock_pc.call_args
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assert call_kwargs.kwargs["need_quality_pass"] is True
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assert call_kwargs.kwargs["memoir_correlation_id"] == "cid-1"
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