Chris CoutinhoandClaude Opus 4.8
973f80e7b9
Billing product model finalized (Deck #281): bill pages externally, record tokens internally. Rename the data-plane metric literals to match the now- canonical contract (Deck #284) — the control plane's METRIC_EVENT_NAMES is already renamed, so the old names would be unmapped and never sync to Stripe. Rename (values unchanged): - embeddings_queries → tokens_embedded (value = real token count, already emitted by this PR; the unit upstream providers bill on). - pages_chunks → pages_embedded (value kept as len(chunk_texts) interim; TODO(#282): real normalized "pages indexed" count — real pages for paginated types, chars/tokens-per-page constant otherwise — is deferred to the instrumentation card, this only lands the name/contract). - All literals, log strings, docstrings, comments, the migration comment, and tests renamed; grep confirms zero old strings remain. Observability (new): export embedding token cost to Prometheus as astrolabe_embedding_tokens_total{provider,operation} (operation = index|query) so the billed cost unit is visible in Grafana, not just the per-tenant billing DB. Dedicated counter (doesn't inflate the existing chunk/request metrics) and always-on (independent of USAGE_METERING_ENABLED, so OSS/self-host gets it). Wired on both the indexing batch embed and the search query embed (query inside the per-request cache-miss branch, so reused embeddings aren't double-counted). Note: the rename orphans any pre-existing embeddings_queries/pages_chunks rows in tenant app DBs (CP no longer maps them) — acceptable; pipeline is inert with throwaway dev/sandbox data. Deck #284 (folded into PR #875). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
125 lines
3.8 KiB
Python
125 lines
3.8 KiB
Python
"""Unit tests for the search-path usage-metering helper (Deck #67).
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``record_search_usage`` records the billable ``tokens_embedded`` 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"] == "tokens_embedded"
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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_empty_doc_types_normalizes_to_null(store_spy):
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"""An empty doc_types list normalizes to None, same as a None input, so a
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metadata->'doc_types' IS NULL query counts the all-types case consistently."""
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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=[],
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token_count=5,
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)
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kwargs = store_spy.record_usage_event.await_args.kwargs
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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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