Nextcloud MCP Server

"""Unit tests for SearchResult validation.""" import pytest from nextcloud_mcp_server.search.algorithms import SearchResult @pytest.mark.unit def test_search_result_rrf_score_in_range(): """Test SearchResult accepts RRF scores in [0.0, 1.0] range.""" result = SearchResult( id=1, doc_type="note", title="Test Note", excerpt="Test excerpt", score=0.85, ) assert result.score == 0.85 @pytest.mark.unit def test_search_result_rrf_score_at_lower_bound(): """Test SearchResult accepts RRF score at lower bound (0.0).""" result = SearchResult( id=1, doc_type="note", title="Test Note", excerpt="Test excerpt", score=0.0, ) assert result.score == 0.0 @pytest.mark.unit def test_search_result_rrf_score_at_upper_bound(): """Test SearchResult accepts RRF score at upper bound (1.0).""" result = SearchResult( id=1, doc_type="note", title="Test Note", excerpt="Test excerpt", score=1.0, ) assert result.score == 1.0 @pytest.mark.unit def test_search_result_dbsf_score_above_one(): """Test SearchResult accepts DBSF scores > 1.0. DBSF (Distribution-Based Score Fusion) sums normalized scores from multiple systems (dense semantic + sparse BM25), so scores can exceed 1.0 when both systems strongly agree a document is relevant. """ # Typical DBSF score when both systems agree result = SearchResult( id=1, doc_type="note", title="Highly Relevant Note", excerpt="Contains keywords and is semantically similar", score=1.55, ) assert result.score == 1.55 @pytest.mark.unit def test_search_result_dbsf_score_edge_case(): """Test SearchResult accepts DBSF maximum theoretical score (2.0). Maximum DBSF score with 2 systems: 1.0 (dense) + 1.0 (sparse) = 2.0 """ result = SearchResult( id=1, doc_type="note", title="Perfect Match", excerpt="Perfect semantic and keyword match", score=2.0, ) assert result.score == 2.0 @pytest.mark.unit def test_search_result_negative_score_raises_error(): """Test SearchResult rejects negative scores.""" with pytest.raises(ValueError) as exc_info: SearchResult( id=1, doc_type="note", title="Test Note", excerpt="Test excerpt", score=-0.1, ) assert "Score must be non-negative" in str(exc_info.value) assert "got -0.1" in str(exc_info.value) @pytest.mark.unit def test_search_result_with_metadata(): """Test SearchResult with optional metadata field.""" result = SearchResult( id=1, doc_type="note", title="Test Note", excerpt="Test excerpt", score=1.25, metadata={"fusion_method": "dbsf", "dense_score": 0.8, "sparse_score": 0.45}, ) assert result.score == 1.25 assert result.metadata["fusion_method"] == "dbsf" assert result.metadata["dense_score"] == 0.8 assert result.metadata["sparse_score"] == 0.45 @pytest.mark.unit def test_search_result_with_chunk_offsets(): """Test SearchResult with chunk offset information.""" result = SearchResult( id=1, doc_type="note", title="Test Note", excerpt="matching chunk text", score=0.9, chunk_start_offset=100, chunk_end_offset=500, ) assert result.chunk_start_offset == 100 assert result.chunk_end_offset == 500