Chris CoutinhoandClaude Opus 4.8
df03d33fd4
Round-4 claude-review findings (no blockers): - 🟡 Untested server-layer metering hook (raised across rounds): extracted the nc_semantic_search embeddings_queries recording into a module-level record_search_usage() helper (mirroring record_indexing_usage) and added tests/unit/server/test_semantic_metering.py — value = query token count, flag-off no-op, None token → 0, doc_types metadata bounding, best-effort failure swallowed. - 🟡 Dedup-hit skipped metering invisibly: the existing dedup info log now states "no embedding/usage recorded" so a "fewer embeddings_queries rows than expected" audit lands on the dedup path directly. Deferred 🟢 nits (stated on the PR): search 0-token rows are recorded deliberately (the query embedding ran; zero is a sum no-op) — documented in the helper; embed_tokens closure locality and the OpenAI embed() dual path are unchanged (correct as-is / separate refactor). Deck #67. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
"""Unit tests for the search-path usage-metering helper (Deck #67).
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``record_search_usage`` records the billable ``embeddings_queries`` event for a
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semantic search. These pin the value mapping (query token count), the flag-off
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no-op, the doc_types metadata bounding, and the best-effort failure path —
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covering the server-tool metering wiring without standing up the full
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``nc_semantic_search`` tool.
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"""
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from unittest.mock import AsyncMock, MagicMock
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import pytest
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from nextcloud_mcp_server.server import semantic
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@pytest.fixture
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def store_spy(monkeypatch):
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"""Patch UsageEventStore.shared() to return a spy store."""
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store = MagicMock()
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store.record_usage_event = AsyncMock()
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monkeypatch.setattr(
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semantic.UsageEventStore, "shared", AsyncMock(return_value=store)
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)
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return store
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@pytest.mark.unit
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async def test_records_query_token_count(store_spy):
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"""The event value is the query embedding's token count."""
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await semantic.record_search_usage(
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enabled=True,
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user_id="alice",
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fusion="rrf",
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doc_types=["note", "file"],
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token_count=42,
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)
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store_spy.record_usage_event.assert_awaited_once()
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kwargs = store_spy.record_usage_event.await_args.kwargs
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assert kwargs["metric"] == "embeddings_queries"
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assert kwargs["value"] == 42
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assert kwargs["enabled"] is True
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assert kwargs["metadata"]["user_id"] == "alice"
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assert kwargs["metadata"]["fusion"] == "rrf"
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assert kwargs["metadata"]["doc_types"] == ["note", "file"]
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@pytest.mark.unit
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async def test_disabled_is_noop(store_spy):
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"""Flag off → no store access, no event."""
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await semantic.record_search_usage(
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enabled=False,
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user_id="alice",
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fusion="rrf",
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doc_types=None,
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token_count=10,
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)
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store_spy.record_usage_event.assert_not_awaited()
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@pytest.mark.unit
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async def test_none_token_count_records_zero(store_spy):
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"""A missing token count (pre-embed error) records value 0, not None."""
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await semantic.record_search_usage(
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enabled=True,
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user_id="alice",
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fusion="dbsf",
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doc_types=None,
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token_count=None,
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)
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kwargs = store_spy.record_usage_event.await_args.kwargs
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assert kwargs["value"] == 0
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# None and [] both normalize to null for consistent IS NULL counting.
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assert kwargs["metadata"]["doc_types"] is None
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@pytest.mark.unit
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async def test_doc_types_metadata_is_bounded(store_spy):
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"""A large doc_types list is truncated to the metadata cap."""
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many = [f"type-{i}" for i in range(40)]
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await semantic.record_search_usage(
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enabled=True,
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user_id="alice",
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fusion="rrf",
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doc_types=many,
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token_count=5,
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)
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recorded = store_spy.record_usage_event.await_args.kwargs["metadata"]["doc_types"]
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assert recorded == many[: semantic._USAGE_METADATA_MAX_DOC_TYPES]
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@pytest.mark.unit
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async def test_store_failure_is_swallowed(monkeypatch):
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"""A store-construction failure is logged, never raised into the search."""
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monkeypatch.setattr(
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semantic.UsageEventStore,
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"shared",
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AsyncMock(side_effect=RuntimeError("boom")),
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)
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# Must not raise.
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await semantic.record_search_usage(
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enabled=True,
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user_id="alice",
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fusion="rrf",
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doc_types=None,
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token_count=7,
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)
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