244 lines
8.9 KiB
Python
244 lines
8.9 KiB
Python
from __future__ import annotations
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import asyncio
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import json
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from types import SimpleNamespace
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import httpx
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import pytest
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from app.gateway import llmwiki_index_scheduler
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from deerflow.config.llmwiki_config import LocalWikiEmbeddingConfig, LocalWikiIndexConfig
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from deerflow.integrations.weknora.local_index.embedding import StrictWikiEmbeddingClient
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from deerflow.integrations.weknora.local_index.sync import WikiSyncService
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from deerflow.integrations.weknora.local_index.vector_cache import WikiVectorCache
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from deerflow.persistence.llmwiki_index.memory import MemoryLlmWikiIndexStore
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class FakeEmbedding:
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dimensions = 2
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fingerprint = "fp"
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def __init__(self) -> None:
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self.calls = 0
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async def embed_texts(self, texts: list[str]) -> list[list[float]]:
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self.calls += 1
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return [[1.0, 0.0] for _ in texts]
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class FailingEmbedding(FakeEmbedding):
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async def embed_texts(self, texts: list[str]) -> list[list[float]]:
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raise RuntimeError("embedding unavailable")
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class NewFingerprintEmbedding(FakeEmbedding):
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fingerprint = "fp-new"
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class FakeWeKnora:
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def __init__(self) -> None:
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self.pages = {
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"one": {
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"id": "p1",
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"slug": "one",
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"title": "One",
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"summary": "A long enough summary for indexing and retrieval.",
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"content": "# Intro\nA long enough Wiki paragraph for indexing and semantic retrieval.",
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"status": "published",
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"version": 1,
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},
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}
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async def get_knowledge_base(self, knowledge_base_id: str):
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return {"id": knowledge_base_id, "indexing_strategy": {"wiki_enabled": True}}
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async def list_wiki_pages(self, knowledge_base_id: str, *, page: int, page_size: int):
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return {
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"pages": [{"slug": item["slug"], "title": item["title"]} for item in self.pages.values()],
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"total": len(self.pages),
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"total_pages": 1,
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}
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async def get_wiki_page(self, knowledge_base_id: str, slug: str):
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return dict(self.pages[slug])
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@pytest.mark.asyncio
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async def test_configured_dimensions_are_validated_but_not_sent_to_bge() -> None:
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requests: list[dict] = []
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def handler(request: httpx.Request) -> httpx.Response:
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payload = json.loads(request.content)
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requests.append(payload)
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if "dimensions" in payload:
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return httpx.Response(400, json={"detail": "dimensions is not supported"})
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return httpx.Response(
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200,
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json={"data": [{"index": 0, "embedding": [1.0, 0.0, 0.0]}]},
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)
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config = LocalWikiEmbeddingConfig(
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model="bge-embedding-m3",
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base_url="http://embedding.test/v1",
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dimensions=3,
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max_retries=0,
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)
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async with httpx.AsyncClient(transport=httpx.MockTransport(handler)) as http_client:
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client = StrictWikiEmbeddingClient(config, http_client=http_client)
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vector = await client.embed_query("Wiki page")
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assert vector == [1.0, 0.0, 0.0]
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assert requests == [{"model": "bge-embedding-m3", "input": ["Wiki page"]}]
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@pytest.mark.asyncio
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async def test_embedding_error_includes_upstream_reason(caplog) -> None:
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def handler(request: httpx.Request) -> httpx.Response:
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return httpx.Response(400, json={"detail": "unsupported request parameter: dimensions"})
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config = LocalWikiEmbeddingConfig(
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model="bge-embedding-m3",
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base_url="http://embedding.test/v1",
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max_retries=0,
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)
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async with httpx.AsyncClient(transport=httpx.MockTransport(handler)) as http_client:
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client = StrictWikiEmbeddingClient(config, http_client=http_client)
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with pytest.raises(RuntimeError, match="unsupported request parameter: dimensions"):
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await client.embed_query("Wiki page")
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assert "unsupported request parameter: dimensions" in caplog.text
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@pytest.mark.asyncio
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async def test_omitted_dimensions_are_discovered_from_first_response() -> None:
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def handler(request: httpx.Request) -> httpx.Response:
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return httpx.Response(
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200,
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json={"data": [{"index": 0, "embedding": [0.0, 1.0]}]},
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)
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config = LocalWikiEmbeddingConfig(
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model="bge-embedding-m3",
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base_url="http://embedding.test/v1",
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dimensions=None,
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max_retries=0,
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)
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async with httpx.AsyncClient(transport=httpx.MockTransport(handler)) as http_client:
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client = StrictWikiEmbeddingClient(config, http_client=http_client)
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await client.embed_query("dimension probe")
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assert client.dimensions == 2
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@pytest.mark.asyncio
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async def test_sync_is_incremental_and_full_scan_deletes_missing_pages() -> None:
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config = LocalWikiIndexConfig(chunk_min_chars=10, min_similarity=0)
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store = MemoryLlmWikiIndexStore()
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embedding = FakeEmbedding()
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client = FakeWeKnora()
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service = WikiSyncService(config, store, embedding, WikiVectorCache(store), client)
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mapping = {"id": "kb", "weknora_id": "remote", "name": "Wiki"}
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first = await service.sync_mapping(mapping)
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assert first["changed_pages"] == 1
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assert embedding.calls == 1
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statuses = await store.list_page_index_status("kb")
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assert statuses[0]["title"] == "One"
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assert statuses[0]["vector_count"] > 0
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assert store.states["kb"]["remote_page_count"] == 1
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second = await service.sync_mapping(mapping)
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assert second["changed_pages"] == 0
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assert embedding.calls == 1
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client.pages.clear()
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deleted = await service.sync_mapping(mapping)
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assert deleted["deleted_pages"] == 1
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assert next(iter(store.pages.values()))["is_remote_deleted"] is True
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@pytest.mark.asyncio
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async def test_page_failure_keeps_previous_generation_and_marks_partial() -> None:
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config = LocalWikiIndexConfig(chunk_min_chars=10, min_similarity=0)
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store = MemoryLlmWikiIndexStore()
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client = FakeWeKnora()
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mapping = {"id": "kb", "weknora_id": "remote", "name": "Wiki"}
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await WikiSyncService(config, store, FakeEmbedding(), WikiVectorCache(store), client).sync_mapping(mapping)
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page = next(iter(store.pages.values()))
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old_hash = page["content_hash"]
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old_vectors = list(store.vectors[page["id"]])
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client.pages["one"]["content"] += " Changed."
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result = await WikiSyncService(config, store, FailingEmbedding(), WikiVectorCache(store), client).sync_mapping(mapping)
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assert result["status"] == "partial"
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assert store.states["kb"]["state"] == "partial"
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assert store.states["kb"]["failed_page_count"] == 1
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assert store.pages[page["id"]]["content_hash"] == old_hash
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assert store.pages[page["id"]]["index_status"] == "ready"
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assert store.vectors[page["id"]] == old_vectors
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@pytest.mark.asyncio
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async def test_fingerprint_change_automatically_rebuilds_entire_mapping() -> None:
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config = LocalWikiIndexConfig(chunk_min_chars=10, min_similarity=0)
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store = MemoryLlmWikiIndexStore()
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client = FakeWeKnora()
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mapping = {"id": "kb", "weknora_id": "remote", "name": "Wiki"}
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await WikiSyncService(config, store, FakeEmbedding(), WikiVectorCache(store), client).sync_mapping(mapping)
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embedding = NewFingerprintEmbedding()
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result = await WikiSyncService(config, store, embedding, WikiVectorCache(store), client).sync_mapping(mapping)
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assert result["status"] == "completed"
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assert result["changed_pages"] == 1
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assert embedding.calls == 1
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assert store.states["kb"]["state"] == "idle"
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assert store.states["kb"]["embedding_fingerprint"] == "fp-new"
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assert {vector["embedding_fingerprint"] for vectors in store.vectors.values() for vector in vectors} == {"fp-new"}
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@pytest.mark.asyncio
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async def test_auto_sync_scheduler_scans_immediately_then_waits(monkeypatch) -> None:
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calls: list[str] = []
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async def fake_sleep(seconds: float) -> None:
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calls.append(f"sleep:{seconds}")
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raise asyncio.CancelledError
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async def fake_sync_once(app) -> None:
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calls.append("sync")
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if calls.count("sync") > 1:
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raise asyncio.CancelledError
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monkeypatch.setattr(llmwiki_index_scheduler.asyncio, "sleep", fake_sleep)
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monkeypatch.setattr(llmwiki_index_scheduler, "_sync_once", fake_sync_once)
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app = SimpleNamespace(
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state=SimpleNamespace(
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config=SimpleNamespace(
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llmwiki=SimpleNamespace(
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local_wiki_index=SimpleNamespace(sync_interval_seconds=120),
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)
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)
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)
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)
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with pytest.raises(asyncio.CancelledError):
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await llmwiki_index_scheduler.local_wiki_index_scheduler_loop(app)
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assert calls == ["sync", "sleep:120"]
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def test_local_wiki_index_defaults_to_automatic_sync() -> None:
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config = LocalWikiIndexConfig()
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assert config.auto_sync is True
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assert config.sync_interval_seconds == 30
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def test_local_wiki_index_accepts_legacy_sync_flag() -> None:
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config = LocalWikiIndexConfig.model_validate({"sync_enabled": True})
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assert config.auto_sync is True
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assert config.sync_enabled is True
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