dialog-reddit-tools

""" Test suite for Phase 2a vector database enhancements. Tests all four enhancements: - 2a.1: Distance scores in response - 2a.2: Match tier classification - 2a.3: Confidence threshold filtering - 2a.4: Confidence statistics and tier distribution """ import pytest import sys import os from unittest.mock import Mock, patch, AsyncMock from fastmcp import Context # Add project root to Python path sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..')) from src.tools.discover import ( discover_subreddits, classify_match_tier, calculate_confidence_stats, calculate_tier_distribution ) class TestPhase2aEnhancements: """Tests for all Phase 2a enhancements.""" @pytest.fixture def mock_collection(self): """Create a mock ChromaDB collection with realistic data.""" collection = Mock() # Mock query response with realistic distances collection.query.return_value = { 'ids': [['MachineLearning', 'deeplearning', 'artificial', 'learnmachinelearning', 'datascience']], 'distances': [[0.15, 0.28, 0.55, 0.72, 0.95]], # Varied distances for tier testing 'documents': [['ML subreddit', 'Deep learning', 'AI', 'Learning ML', 'Data science']], 'metadatas': [[ { 'name': 'MachineLearning', 'subscribers': 825000, 'nsfw': False, 'url': 'https://reddit.com/r/MachineLearning' }, { 'name': 'deeplearning', 'subscribers': 285000, 'nsfw': False, 'url': 'https://reddit.com/r/deeplearning' }, { 'name': 'artificial', 'subscribers': 125000, 'nsfw': False, 'url': 'https://reddit.com/r/artificial' }, { 'name': 'learnmachinelearning', 'subscribers': 45000, 'nsfw': False, 'url': 'https://reddit.com/r/learnmachinelearning' }, { 'name': 'datascience', 'subscribers': 650000, 'nsfw': False, 'url': 'https://reddit.com/r/datascience' } ]] } return collection @pytest.mark.asyncio async def test_2a1_distance_scores_exposed(self, mock_collection): """Enhancement 2a.1: Distance scores in response.""" with patch('src.tools.discover.get_chroma_client'), \ patch('src.tools.discover.get_collection', return_value=mock_collection): response = await discover_subreddits("machine learning", limit=5) # Verify response structure assert 'subreddits' in response assert len(response['subreddits']) > 0 # Check each subreddit has distance field for subreddit in response['subreddits']: assert 'distance' in subreddit, "Distance field missing" assert isinstance(subreddit['distance'], float), "Distance should be float" assert 0.0 <= subreddit['distance'] <= 2.0, "Distance out of valid range" @pytest.mark.asyncio async def test_2a2_match_tier_labels(self, mock_collection): """Enhancement 2a.2: Match tier classification.""" with patch('src.tools.discover.get_chroma_client'), \ patch('src.tools.discover.get_collection', return_value=mock_collection): response = await discover_subreddits("machine learning", limit=5) valid_tiers = {'exact', 'semantic', 'adjacent', 'peripheral'} # Check each subreddit has match_tier field for subreddit in response['subreddits']: assert 'match_tier' in subreddit, "Match tier field missing" assert subreddit['match_tier'] in valid_tiers, f"Invalid tier: {subreddit['match_tier']}" @pytest.mark.asyncio async def test_2a2_tier_distance_alignment(self, mock_collection): """Verify tiers match distance thresholds.""" with patch('src.tools.discover.get_chroma_client'), \ patch('src.tools.discover.get_collection', return_value=mock_collection): response = await discover_subreddits("machine learning", limit=5) for subreddit in response['subreddits']: distance = subreddit['distance'] tier = subreddit['match_tier'] # Verify tier matches distance thresholds if tier == 'exact': assert distance < 0.2, f"Exact tier but distance {distance} >= 0.2" elif tier == 'semantic': assert 0.2 <= distance < 0.35, f"Semantic tier but distance {distance} not in [0.2, 0.35)" elif tier == 'adjacent': assert 0.35 <= distance < 0.65, f"Adjacent tier but distance {distance} not in [0.35, 0.65)" else: # peripheral assert distance >= 0.65, f"Peripheral tier but distance {distance} < 0.65" @pytest.mark.asyncio async def test_2a3_min_confidence_parameter(self, mock_collection): """Enhancement 2a.3: Confidence threshold filtering.""" with patch('src.tools.discover.get_chroma_client'), \ patch('src.tools.discover.get_collection', return_value=mock_collection): # Get all results (no filter) all_results = await discover_subreddits("machine learning", limit=10) all_count = len(all_results['subreddits']) # Get filtered results filtered = await discover_subreddits("machine learning", limit=10, min_confidence=0.7) filtered_count = len(filtered['subreddits']) # Verify filtering works assert filtered_count <= all_count, "Filtered count should be <= all count" # Verify all returned results meet threshold for subreddit in filtered['subreddits']: assert subreddit['confidence'] >= 0.7, \ f"Subreddit {subreddit['name']} has confidence {subreddit['confidence']} < 0.7" @pytest.mark.asyncio async def test_2a3_confidence_threshold_default(self, mock_collection): """Default min_confidence should be 0.0 (no filtering).""" with patch('src.tools.discover.get_chroma_client'), \ patch('src.tools.discover.get_collection', return_value=mock_collection): results1 = await discover_subreddits("machine learning", limit=5) results2 = await discover_subreddits("machine learning", limit=5, min_confidence=0.0) # Should return same number of results assert len(results1['subreddits']) == len(results2['subreddits']) @pytest.mark.asyncio async def test_2a4_confidence_statistics(self, mock_collection): """Enhancement 2a.4: Confidence distribution statistics.""" with patch('src.tools.discover.get_chroma_client'), \ patch('src.tools.discover.get_collection', return_value=mock_collection): response = await discover_subreddits("machine learning", limit=5) assert 'summary' in response assert 'confidence_stats' in response['summary'] stats = response['summary']['confidence_stats'] # Verify all stats present assert 'mean' in stats assert 'median' in stats assert 'min' in stats assert 'max' in stats assert 'std_dev' in stats # Verify logical relationships assert stats['min'] <= stats['median'] <= stats['max'], \ "Stats order should be min <= median <= max" assert 0.0 <= stats['min'] <= 1.0, "Min out of range" assert 0.0 <= stats['max'] <= 1.0, "Max out of range" @pytest.mark.asyncio async def test_2a4_tier_distribution(self, mock_collection): """Tier distribution counts should match returned count.""" with patch('src.tools.discover.get_chroma_client'), \ patch('src.tools.discover.get_collection', return_value=mock_collection): response = await discover_subreddits("machine learning", limit=5) assert 'summary' in response assert 'tier_distribution' in response['summary'] tiers = response['summary']['tier_distribution'] # Verify all tiers present assert all(tier in tiers for tier in ['exact', 'semantic', 'adjacent', 'peripheral']) # Verify counts sum correctly total_tiers = sum(tiers.values()) returned = response['summary']['returned'] assert total_tiers == returned, \ f"Tier distribution sum {total_tiers} != returned count {returned}" @pytest.mark.asyncio async def test_backward_compatibility(self, mock_collection): """All changes must be backward compatible.""" with patch('src.tools.discover.get_chroma_client'), \ patch('src.tools.discover.get_collection', return_value=mock_collection): response = await discover_subreddits("machine learning") # Old fields should still exist assert 'subreddits' in response assert 'summary' in response for subreddit in response['subreddits']: # Original fields assert 'name' in subreddit assert 'confidence' in subreddit assert 'url' in subreddit assert 'subscribers' in subreddit # New fields (Phase 2a) assert 'distance' in subreddit assert 'match_tier' in subreddit # Summary original fields assert 'total_found' in response['summary'] assert 'returned' in response['summary'] assert 'has_more' in response['summary'] # Summary new fields (Phase 2a) assert 'confidence_stats' in response['summary'] assert 'tier_distribution' in response['summary'] @pytest.mark.asyncio async def test_all_enhancements_together(self, mock_collection): """Test all Phase 2a features in one response.""" with patch('src.tools.discover.get_chroma_client'), \ patch('src.tools.discover.get_collection', return_value=mock_collection): response = await discover_subreddits( "machine learning", limit=5, min_confidence=0.5 ) # Verify all features present for subreddit in response['subreddits']: # 2a.1 & 2a.2 assert 'distance' in subreddit assert 'match_tier' in subreddit # 2a.3 applied assert subreddit['confidence'] >= 0.5 # 2a.4 assert 'confidence_stats' in response['summary'] assert 'tier_distribution' in response['summary'] class TestHelperFunctions: """Test helper functions directly.""" def test_classify_match_tier_exact(self): """Test exact tier classification (distance < 0.2).""" assert classify_match_tier(0.1) == "exact" assert classify_match_tier(0.19) == "exact" def test_classify_match_tier_semantic(self): """Test semantic tier classification (0.2 <= distance < 0.35).""" assert classify_match_tier(0.2) == "semantic" assert classify_match_tier(0.3) == "semantic" def test_classify_match_tier_adjacent(self): """Test adjacent tier classification (0.35 <= distance < 0.65).""" assert classify_match_tier(0.35) == "adjacent" assert classify_match_tier(0.5) == "adjacent" def test_classify_match_tier_peripheral(self): """Test peripheral tier classification (distance >= 0.65).""" assert classify_match_tier(0.65) == "peripheral" assert classify_match_tier(1.0) == "peripheral" def test_calculate_confidence_stats_normal(self): """Test confidence statistics with normal data.""" scores = [0.9, 0.8, 0.7, 0.6, 0.5] stats = calculate_confidence_stats(scores) assert stats['mean'] == 0.7 assert stats['median'] == 0.7 assert stats['min'] == 0.5 assert stats['max'] == 0.9 assert stats['std_dev'] > 0 # Should have some variance def test_calculate_confidence_stats_empty(self): """Test confidence statistics with empty list.""" stats = calculate_confidence_stats([]) assert stats['mean'] == 0.0 assert stats['median'] == 0.0 assert stats['min'] == 0.0 assert stats['max'] == 0.0 assert stats['std_dev'] == 0.0 def test_calculate_confidence_stats_single(self): """Test confidence statistics with single value.""" scores = [0.75] stats = calculate_confidence_stats(scores) assert stats['mean'] == 0.75 assert stats['median'] == 0.75 assert stats['min'] == 0.75 assert stats['max'] == 0.75 assert stats['std_dev'] == 0.0 # No variance with single value def test_calculate_tier_distribution(self): """Test tier distribution calculation.""" results = [ {'match_tier': 'exact'}, {'match_tier': 'exact'}, {'match_tier': 'semantic'}, {'match_tier': 'adjacent'}, {'match_tier': 'peripheral'} ] dist = calculate_tier_distribution(results) assert dist['exact'] == 2 assert dist['semantic'] == 1 assert dist['adjacent'] == 1 assert dist['peripheral'] == 1 def test_calculate_tier_distribution_empty(self): """Test tier distribution with empty results.""" dist = calculate_tier_distribution([]) assert dist['exact'] == 0 assert dist['semantic'] == 0 assert dist['adjacent'] == 0 assert dist['peripheral'] == 0 class TestEdgeCases: """Test edge cases and error handling.""" @pytest.mark.asyncio async def test_min_confidence_filters_all(self): """Test min_confidence that filters all results.""" collection = Mock() collection.query.return_value = { 'ids': [['test']], 'distances': [[0.9]], # Low confidence result 'documents': [['test']], 'metadatas': [[{ 'name': 'test', 'subscribers': 1000, 'nsfw': False, 'url': 'https://reddit.com/r/test' }]] } with patch('src.tools.discover.get_chroma_client'), \ patch('src.tools.discover.get_collection', return_value=collection): # Set min_confidence very high response = await discover_subreddits("test", limit=10, min_confidence=0.99) # Should return empty or very few results assert len(response['subreddits']) == 0 @pytest.mark.asyncio async def test_batch_mode_with_min_confidence(self): """Test batch mode respects min_confidence parameter.""" collection = Mock() collection.query.return_value = { 'ids': [['test1', 'test2']], 'distances': [[0.5, 0.9]], 'documents': [['test1', 'test2']], 'metadatas': [[ {'name': 'test1', 'subscribers': 1000, 'nsfw': False, 'url': 'https://reddit.com/r/test1'}, {'name': 'test2', 'subscribers': 2000, 'nsfw': False, 'url': 'https://reddit.com/r/test2'} ]] } with patch('src.tools.discover.get_chroma_client'), \ patch('src.tools.discover.get_collection', return_value=collection): response = await discover_subreddits( queries=["query1", "query2"], limit=10, min_confidence=0.6 ) # Verify batch mode assert 'batch_mode' in response assert response['batch_mode'] is True # Each query should respect min_confidence for query, result in response['results'].items(): for subreddit in result['subreddits']: assert subreddit['confidence'] >= 0.6