v1.0 - Version stable: multi-PC, détection UI-DETR-1, 3 modes exécution

- Frontend v4 accessible sur réseau local (192.168.1.40)
- Ports ouverts: 3002 (frontend), 5001 (backend), 5004 (dashboard)
- Ollama GPU fonctionnel
- Self-healing interactif
- Dashboard confiance

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

tests/unit/test_faiss_manager.py

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+"""
+Tests unitaires pour FAISSManager avec property-based testing.
+
+Property 11: FAISS Index Consistency
+- L'index FAISS doit maintenir la cohérence entre vecteurs et métadonnées
+- Validates: Requirements 4.8, 12.3, 12.6
+"""
+
+import pytest
+import numpy as np
+import tempfile
+import shutil
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent))
+from core.embedding.faiss_manager import FAISSManager
+
+
+class TestFAISSManager:
+    """Tests pour FAISSManager."""
+    
+    def setup_method(self):
+        """Setup avant chaque test."""
+        self.temp_dir = tempfile.mkdtemp()
+        self.index_path = Path(self.temp_dir) / "test_index"
+        self.manager = FAISSManager(dimensions=512)
+    
+    def teardown_method(self):
+        """Cleanup après chaque test."""
+        shutil.rmtree(self.temp_dir, ignore_errors=True)
+    
+    def test_add_single_embedding(self):
+        """Test ajout d'un seul embedding."""
+        vector = np.random.randn(512).astype(np.float32)
+        vector = vector / np.linalg.norm(vector)
+        
+        metadata = {
+            'state_id': 'state_001',
+            'timestamp': '2024-11-22T10:00:00'
+        }
+        
+        idx = self.manager.add_embedding('emb_001', vector, metadata)
+        
+        assert idx == 0
+        assert self.manager.index.ntotal == 1
+    
+    def test_property_index_consistency(self):
+        """Property 11: Cohérence entre index et métadonnées."""
+        n_vectors = 20
+        
+        for i in range(n_vectors):
+            vector = np.random.randn(512).astype(np.float32)
+            vector = vector / np.linalg.norm(vector)
+            metadata = {'state_id': f'state_{i:03d}', 'index': i}
+            self.manager.add_embedding(f'emb_{i:03d}', vector, metadata)
+        
+        assert self.manager.index.ntotal == n_vectors
+        assert len(self.manager.metadata_store) == n_vectors
+    
+    def test_search_similar(self):
+        """Test recherche de similarité."""
+        vectors = []
+        for i in range(5):
+            vector = np.random.randn(512).astype(np.float32)
+            vector = vector / np.linalg.norm(vector)
+            vectors.append(vector)
+            self.manager.add_embedding(f'emb_{i}', vector, {'state_id': f'state_{i}'})
+        
+        query = vectors[0]
+        results = self.manager.search_similar(query, k=3)
+        
+        assert len(results) == 3
+        assert results[0].similarity > 0.99
+    
+    def test_save_and_load(self):
+        """Test sauvegarde et chargement."""
+        n_vectors = 10
+        original_vectors = []
+        
+        for i in range(n_vectors):
+            vector = np.random.randn(512).astype(np.float32)
+            vector = vector / np.linalg.norm(vector)
+            original_vectors.append(vector)
+            self.manager.add_embedding(f'emb_{i}', vector, {'state_id': f'state_{i}'})
+        
+        index_path = self.index_path / "index.faiss"
+        metadata_path = self.index_path / "metadata.pkl"
+        self.manager.save(index_path, metadata_path)
+        
+        new_manager = FAISSManager.load(index_path, metadata_path)
+        
+        assert new_manager.index.ntotal == n_vectors
+        assert len(new_manager.metadata_store) == n_vectors
+
+
+if __name__ == '__main__':
+    pytest.main([__file__, '-v'])