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>
127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
"""Unit tests for BM25 hybrid search algorithm."""
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from unittest.mock import AsyncMock, MagicMock
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import pytest
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from qdrant_client import models
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from nextcloud_mcp_server.search.bm25_hybrid import BM25HybridSearchAlgorithm
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@pytest.mark.unit
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def test_bm25_hybrid_initialization_default():
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"""Test BM25HybridSearchAlgorithm initializes with default RRF fusion."""
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algo = BM25HybridSearchAlgorithm()
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assert algo.score_threshold == 0.0
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assert algo.fusion == models.Fusion.RRF
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assert algo.fusion_name == "rrf"
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assert algo.name == "bm25_hybrid"
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@pytest.mark.unit
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def test_bm25_hybrid_initialization_with_rrf():
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"""Test BM25HybridSearchAlgorithm initializes with explicit RRF fusion."""
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algo = BM25HybridSearchAlgorithm(score_threshold=0.5, fusion="rrf")
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assert algo.score_threshold == 0.5
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assert algo.fusion == models.Fusion.RRF
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assert algo.fusion_name == "rrf"
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@pytest.mark.unit
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def test_bm25_hybrid_initialization_with_dbsf():
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"""Test BM25HybridSearchAlgorithm initializes with DBSF fusion."""
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algo = BM25HybridSearchAlgorithm(score_threshold=0.7, fusion="dbsf")
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assert algo.score_threshold == 0.7
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assert algo.fusion == models.Fusion.DBSF
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assert algo.fusion_name == "dbsf"
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@pytest.mark.unit
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def test_bm25_hybrid_invalid_fusion_raises_error():
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"""Test BM25HybridSearchAlgorithm raises ValueError for invalid fusion."""
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with pytest.raises(ValueError) as exc_info:
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BM25HybridSearchAlgorithm(fusion="invalid")
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assert "Invalid fusion algorithm 'invalid'" in str(exc_info.value)
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assert "Must be 'rrf' or 'dbsf'" in str(exc_info.value)
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@pytest.mark.unit
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def test_bm25_hybrid_requires_vector_db():
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"""Test BM25HybridSearchAlgorithm reports it requires vector database."""
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algo = BM25HybridSearchAlgorithm()
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assert algo.requires_vector_db is True
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@pytest.fixture
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def patched_search(monkeypatch):
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"""Stub the embedding / BM25 / Qdrant deps of search() and return the
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embed_with_usage mock so tests can assert how often the query was embedded."""
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embed = AsyncMock(return_value=([0.1, 0.2, 0.3], 7))
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svc = MagicMock()
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svc.embed_with_usage = embed
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monkeypatch.setattr(
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"nextcloud_mcp_server.search.bm25_hybrid.get_embedding_service", lambda: svc
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)
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bm25 = MagicMock()
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bm25.encode_async = AsyncMock(return_value={"indices": [1], "values": [0.5]})
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monkeypatch.setattr(
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"nextcloud_mcp_server.search.bm25_hybrid.get_bm25_service",
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AsyncMock(return_value=bm25),
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)
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qdrant = MagicMock()
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empty = MagicMock()
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empty.points = []
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qdrant.query_points = AsyncMock(return_value=empty)
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monkeypatch.setattr(
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"nextcloud_mcp_server.search.bm25_hybrid.get_qdrant_client",
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AsyncMock(return_value=qdrant),
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)
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settings = MagicMock()
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settings.get_collection_name.return_value = "test_collection"
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settings.get_embedding_provider_family.return_value = "mistral"
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monkeypatch.setattr(
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"nextcloud_mcp_server.search.bm25_hybrid.get_settings", lambda: settings
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)
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monkeypatch.setattr(
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"nextcloud_mcp_server.search.bm25_hybrid.build_base_filter_conditions",
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lambda **kwargs: [],
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)
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return embed
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@pytest.mark.unit
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async def test_query_embedded_and_metered_once_across_doc_types(patched_search):
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"""nc_semantic_search calls search() once per doc_type on one instance with
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the same query; the dense embedding (and its billed token count) must be
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computed exactly once, not once per type."""
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embed = patched_search
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algo = BM25HybridSearchAlgorithm()
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for dtype in ("note", "file", "deck_card"):
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await algo.search(query="hello", user_id="alice", doc_type=dtype)
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assert embed.await_count == 1 # embedded once, not 3×
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assert (
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algo.query_token_count == 7
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) # single query's token count, not summed/overwritten
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assert algo.query_embedding == [0.1, 0.2, 0.3]
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@pytest.mark.unit
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async def test_different_query_invalidates_cache(patched_search):
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"""A different query string re-embeds (and re-meters)."""
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embed = patched_search
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algo = BM25HybridSearchAlgorithm()
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await algo.search(query="hello", user_id="alice")
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await algo.search(query="world", user_id="alice")
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assert embed.await_count == 2
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