a27b74cf22
- 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>
99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
"""
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Tests unitaires pour FAISSManager avec property-based testing.
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Property 11: FAISS Index Consistency
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- L'index FAISS doit maintenir la cohérence entre vecteurs et métadonnées
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- Validates: Requirements 4.8, 12.3, 12.6
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"""
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import pytest
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import numpy as np
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import tempfile
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import shutil
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from core.embedding.faiss_manager import FAISSManager
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class TestFAISSManager:
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"""Tests pour FAISSManager."""
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def setup_method(self):
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"""Setup avant chaque test."""
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self.temp_dir = tempfile.mkdtemp()
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self.index_path = Path(self.temp_dir) / "test_index"
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self.manager = FAISSManager(dimensions=512)
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def teardown_method(self):
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"""Cleanup après chaque test."""
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shutil.rmtree(self.temp_dir, ignore_errors=True)
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def test_add_single_embedding(self):
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"""Test ajout d'un seul embedding."""
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vector = np.random.randn(512).astype(np.float32)
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vector = vector / np.linalg.norm(vector)
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metadata = {
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'state_id': 'state_001',
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'timestamp': '2024-11-22T10:00:00'
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}
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idx = self.manager.add_embedding('emb_001', vector, metadata)
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assert idx == 0
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assert self.manager.index.ntotal == 1
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def test_property_index_consistency(self):
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"""Property 11: Cohérence entre index et métadonnées."""
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n_vectors = 20
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for i in range(n_vectors):
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vector = np.random.randn(512).astype(np.float32)
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vector = vector / np.linalg.norm(vector)
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metadata = {'state_id': f'state_{i:03d}', 'index': i}
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self.manager.add_embedding(f'emb_{i:03d}', vector, metadata)
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assert self.manager.index.ntotal == n_vectors
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assert len(self.manager.metadata_store) == n_vectors
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def test_search_similar(self):
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"""Test recherche de similarité."""
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vectors = []
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for i in range(5):
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vector = np.random.randn(512).astype(np.float32)
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vector = vector / np.linalg.norm(vector)
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vectors.append(vector)
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self.manager.add_embedding(f'emb_{i}', vector, {'state_id': f'state_{i}'})
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query = vectors[0]
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results = self.manager.search_similar(query, k=3)
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assert len(results) == 3
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assert results[0].similarity > 0.99
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def test_save_and_load(self):
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"""Test sauvegarde et chargement."""
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n_vectors = 10
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original_vectors = []
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for i in range(n_vectors):
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vector = np.random.randn(512).astype(np.float32)
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vector = vector / np.linalg.norm(vector)
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original_vectors.append(vector)
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self.manager.add_embedding(f'emb_{i}', vector, {'state_id': f'state_{i}'})
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index_path = self.index_path / "index.faiss"
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metadata_path = self.index_path / "metadata.pkl"
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self.manager.save(index_path, metadata_path)
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new_manager = FAISSManager.load(index_path, metadata_path)
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assert new_manager.index.ntotal == n_vectors
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assert len(new_manager.metadata_store) == n_vectors
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if __name__ == '__main__':
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pytest.main([__file__, '-v'])
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