520 lines
22 KiB
Python
520 lines
22 KiB
Python
#!/usr/bin/env python3
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"""
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Comprehensive test for optimized embedding system.
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This script validates:
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- Embedding generation and caching performance
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- Vector database operations and indexing
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- Semantic and hybrid search accuracy
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- Batch processing efficiency
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- Memory and storage optimization
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"""
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import time
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import asyncio
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import tempfile
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import shutil
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import numpy as np
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from pathlib import Path
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from typing import List, Dict, Any
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# Import HCFS components
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import sys
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sys.path.insert(0, "/home/tony/hcfs-python")
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from hcfs.core.context_db import Context, ContextDatabase
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from hcfs.core.context_db_optimized_fixed import OptimizedContextDatabase
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from hcfs.core.embeddings_optimized import OptimizedEmbeddingManager
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class EmbeddingOptimizationTest:
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"""Comprehensive embedding optimization test suite."""
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def __init__(self):
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self.temp_dir = Path(tempfile.mkdtemp(prefix="hcfs_embedding_test_"))
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self.db_path = str(self.temp_dir / "test_context.db")
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self.vector_db_path = str(self.temp_dir / "test_vectors.db")
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print(f"🧪 Test directory: {self.temp_dir}")
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# Initialize components
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self.context_db = OptimizedContextDatabase(self.db_path)
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self.embedding_manager = OptimizedEmbeddingManager(
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self.context_db,
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model_name="mini", # Use fastest model for testing
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vector_db_path=self.vector_db_path,
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cache_size=1000,
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batch_size=16
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)
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# Test data
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self.test_contexts = [
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("Machine learning algorithms for data analysis", "/projects/ml/algorithms"),
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("Python web development with FastAPI framework", "/projects/web/fastapi"),
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("Database optimization techniques and indexing", "/database/optimization"),
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("Natural language processing with transformers", "/projects/ml/nlp"),
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("RESTful API design patterns and best practices", "/projects/web/api"),
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("Vector databases for similarity search", "/database/vectors"),
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("Deep learning neural networks architecture", "/projects/ml/deep_learning"),
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("Web security authentication and authorization", "/projects/web/security"),
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("SQL query optimization and performance tuning", "/database/sql"),
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("Computer vision image recognition models", "/projects/ml/vision"),
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("Microservices architecture patterns", "/projects/web/microservices"),
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("NoSQL document database systems", "/database/nosql"),
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("Reinforcement learning algorithms", "/projects/ml/rl"),
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("Frontend React component development", "/projects/web/frontend"),
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("Data warehouse ETL pipeline design", "/database/warehouse"),
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("Semantic search and information retrieval", "/projects/ml/search"),
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("GraphQL API development", "/projects/web/graphql"),
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("Time series database optimization", "/database/timeseries"),
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("Generative AI language models", "/projects/ml/generative"),
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("Mobile app backend services", "/projects/web/mobile")
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]
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self.results = {}
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def setup_test_data(self):
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"""Create test contexts in database."""
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print("📝 Setting up test data...")
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contexts = []
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for i, (content, path) in enumerate(self.test_contexts):
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context = Context(
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id=None, # Will be assigned by database
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path=path,
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content=content,
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summary=f"Summary of {content[:50]}...",
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author=f"TestUser{i % 3}",
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version=1
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)
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contexts.append(context)
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# Store contexts
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start_time = time.time()
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for context in contexts:
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self.context_db.store_context(context)
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setup_time = time.time() - start_time
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print(f"✅ Created {len(contexts)} test contexts in {setup_time:.3f}s")
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return len(contexts)
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def test_embedding_generation_performance(self):
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"""Test embedding generation speed and caching."""
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print("\n🚀 Testing embedding generation performance...")
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test_texts = [content for content, _ in self.test_contexts[:10]]
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# Test single embedding generation
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start_time = time.time()
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embedding1 = self.embedding_manager.generate_embedding(test_texts[0])
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single_time = time.time() - start_time
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print(f" Single embedding: {single_time:.3f}s")
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# Test cached access
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start_time = time.time()
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embedding2 = self.embedding_manager.generate_embedding(test_texts[0])
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cached_time = time.time() - start_time
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print(f" Cached embedding: {cached_time:.3f}s ({cached_time/single_time*100:.1f}% of original)")
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# Verify embeddings are identical
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assert np.allclose(embedding1, embedding2), "Cached embedding should be identical"
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# Test batch generation
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start_time = time.time()
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batch_embeddings = self.embedding_manager.generate_embeddings_batch(test_texts)
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batch_time = time.time() - start_time
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embeddings_per_second = len(test_texts) / batch_time
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print(f" Batch generation: {batch_time:.3f}s ({embeddings_per_second:.1f} embeddings/sec)")
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# Test batch vs individual comparison
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individual_time = single_time * len(test_texts)
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speedup = individual_time / batch_time
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print(f" Batch speedup: {speedup:.2f}x faster than individual")
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self.results["embedding_performance"] = {
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"single_time": single_time,
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"cached_time": cached_time,
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"cache_speedup": single_time / cached_time,
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"batch_time": batch_time,
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"embeddings_per_second": embeddings_per_second,
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"batch_speedup": speedup,
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"embedding_dimension": len(embedding1)
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}
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return embedding1, batch_embeddings
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def test_vector_database_operations(self):
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"""Test vector database storage and retrieval."""
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print("\n💾 Testing vector database operations...")
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# Build embeddings index
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start_time = time.time()
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index_stats = self.embedding_manager.build_embeddings_index(batch_size=8)
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index_time = time.time() - start_time
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print(f" Index build: {index_time:.3f}s")
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print(f" Processed: {index_stats['total_processed']} contexts")
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print(f" Speed: {index_stats['embeddings_per_second']:.1f} embeddings/sec")
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# Test individual storage and retrieval
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test_embedding = np.random.rand(384).astype(np.float32)
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start_time = time.time()
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self.embedding_manager.store_embedding(999, test_embedding)
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store_time = time.time() - start_time
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start_time = time.time()
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retrieved = self.embedding_manager.get_embedding(999)
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retrieve_time = time.time() - start_time
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print(f" Store time: {store_time:.4f}s")
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print(f" Retrieve time: {retrieve_time:.4f}s")
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# Verify accuracy
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assert retrieved is not None, "Should retrieve stored embedding"
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assert np.allclose(test_embedding, retrieved, rtol=1e-6), "Retrieved embedding should match stored"
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# Test batch operations
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batch_data = [(1000 + i, np.random.rand(384).astype(np.float32)) for i in range(10)]
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start_time = time.time()
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self.embedding_manager.store_embeddings_batch(batch_data)
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batch_store_time = time.time() - start_time
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batch_store_rate = len(batch_data) / batch_store_time
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print(f" Batch store: {batch_store_time:.4f}s ({batch_store_rate:.1f} embeddings/sec)")
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self.results["vector_database"] = {
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"index_time": index_time,
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"index_stats": index_stats,
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"store_time": store_time,
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"retrieve_time": retrieve_time,
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"batch_store_time": batch_store_time,
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"batch_store_rate": batch_store_rate
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}
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def test_semantic_search_accuracy(self):
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"""Test semantic search accuracy and performance."""
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print("\n🔍 Testing semantic search...")
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test_queries = [
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("machine learning models", "/projects/ml"),
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("web API development", "/projects/web"),
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("database performance", "/database")
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]
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search_results = {}
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for query, expected_path_prefix in test_queries:
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print(f" Query: '{query}'")
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# Test optimized semantic search
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start_time = time.time()
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results = self.embedding_manager.semantic_search_optimized(
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query, top_k=5, include_contexts=True
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)
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search_time = time.time() - start_time
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print(f" Search time: {search_time:.4f}s")
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print(f" Results: {len(results)}")
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# Check relevance
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relevant_count = 0
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for i, result in enumerate(results):
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if result.context and expected_path_prefix in result.context.path:
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relevant_count += 1
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print(f" {i+1}. Score: {result.score:.3f} | Path: {result.context.path if result.context else 'None'}")
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relevance_ratio = relevant_count / len(results) if results else 0
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print(f" Relevance: {relevant_count}/{len(results)} ({relevance_ratio:.1%})")
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search_results[query] = {
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"search_time": search_time,
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"result_count": len(results),
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"relevant_count": relevant_count,
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"relevance_ratio": relevance_ratio,
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"top_score": results[0].score if results else 0
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}
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self.results["semantic_search"] = search_results
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def test_hybrid_search_performance(self):
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"""Test hybrid search combining semantic and BM25."""
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print("\n🔬 Testing hybrid search...")
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test_queries = [
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"neural network architecture",
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"API authentication security",
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"database query optimization"
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]
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hybrid_results = {}
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for query in test_queries:
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print(f" Query: '{query}'")
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# Test different semantic weights
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for weight in [0.3, 0.5, 0.7, 0.9]:
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start_time = time.time()
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results = self.embedding_manager.hybrid_search_optimized(
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query,
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top_k=5,
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semantic_weight=weight,
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rerank_top_n=20
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)
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search_time = time.time() - start_time
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print(f" Weight {weight}: {search_time:.4f}s, Top score: {results[0].score if results else 0:.3f}")
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if weight == 0.7: # Store detailed results for default weight
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hybrid_results[query] = {
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"search_time": search_time,
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"result_count": len(results),
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"top_score": results[0].score if results else 0,
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"score_details": [
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{
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"score": r.score,
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"semantic_score": r.metadata.get("semantic_score", 0) if r.metadata else 0,
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"bm25_score": r.metadata.get("bm25_score", 0) if r.metadata else 0
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}
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for r in results[:3]
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]
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}
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self.results["hybrid_search"] = hybrid_results
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def test_concurrent_operations(self):
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"""Test concurrent embedding operations."""
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print("\n⚡ Testing concurrent operations...")
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import threading
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import concurrent.futures
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def threaded_embedding():
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return self.embedding_manager.generate_embedding("test threaded embedding")
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def threaded_search():
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return self.embedding_manager.semantic_search_optimized("machine learning", top_k=3)
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def threaded_stats():
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return self.embedding_manager.get_statistics()
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# Test concurrent operations
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start_time = time.time()
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with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
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embedding_future = executor.submit(threaded_embedding)
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search_future = executor.submit(threaded_search)
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stats_future = executor.submit(threaded_stats)
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embedding = embedding_future.result()
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results = search_future.result()
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stats = stats_future.result()
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concurrent_time = time.time() - start_time
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print(f" Concurrent operations: {concurrent_time:.4f}s")
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print(f" Embedding dimension: {len(embedding)}")
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print(f" Search results: {len(results)}")
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print(f" Total embeddings: {stats['database_stats']['total_embeddings']}")
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self.results["concurrent_ops"] = {
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"concurrent_time": concurrent_time,
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"embedding_dimension": len(embedding),
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"search_results": len(results),
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"total_embeddings": stats["database_stats"]["total_embeddings"]
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}
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def test_memory_efficiency(self):
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"""Test memory usage and efficiency."""
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print("\n💡 Testing memory efficiency...")
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import psutil
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import os
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process = psutil.Process(os.getpid())
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# Baseline memory
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baseline_memory = process.memory_info().rss / 1024 / 1024 # MB
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# Generate large batch of embeddings
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large_texts = [f"Large text content number {i} with various details" for i in range(50)]
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start_memory = process.memory_info().rss / 1024 / 1024
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embeddings = self.embedding_manager.generate_embeddings_batch(large_texts, use_cache=False)
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end_memory = process.memory_info().rss / 1024 / 1024
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memory_increase = end_memory - start_memory
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memory_per_embedding = memory_increase / len(embeddings) if embeddings else 0
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# Test cache efficiency
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cache_stats = self.embedding_manager.vector_cache.stats()
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# Test cleanup
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start_cleanup = process.memory_info().rss / 1024 / 1024
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self.embedding_manager.vector_cache.clear()
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end_cleanup = process.memory_info().rss / 1024 / 1024
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memory_freed = start_cleanup - end_cleanup
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print(f" Baseline memory: {baseline_memory:.1f} MB")
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print(f" Memory increase: {memory_increase:.1f} MB for {len(embeddings)} embeddings")
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print(f" Memory per embedding: {memory_per_embedding:.3f} MB")
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print(f" Cache size: {cache_stats['size']} / {cache_stats['max_size']}")
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print(f" Memory freed by cache clear: {memory_freed:.1f} MB")
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self.results["memory_efficiency"] = {
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"baseline_memory": baseline_memory,
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"memory_increase": memory_increase,
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"memory_per_embedding": memory_per_embedding,
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"cache_stats": cache_stats,
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"memory_freed": memory_freed
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}
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def generate_performance_report(self):
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"""Generate comprehensive performance report."""
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print("\n📊 EMBEDDING OPTIMIZATION PERFORMANCE REPORT")
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print("=" * 60)
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# Embedding Performance
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if "embedding_performance" in self.results:
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ep = self.results["embedding_performance"]
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print(f"\n🚀 EMBEDDING GENERATION PERFORMANCE")
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print(f" Single embedding: {ep['single_time']:.3f}s")
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print(f" Cache speedup: {ep['cache_speedup']:.1f}x faster")
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print(f" Batch processing: {ep['embeddings_per_second']:.1f} embeddings/sec")
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print(f" Batch vs individual: {ep['batch_speedup']:.2f}x faster")
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print(f" Embedding dimension: {ep['embedding_dimension']}")
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# Vector Database Performance
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if "vector_database" in self.results:
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vdb = self.results["vector_database"]
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print(f"\n💾 VECTOR DATABASE PERFORMANCE")
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print(f" Index build time: {vdb['index_time']:.3f}s")
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print(f" Indexing speed: {vdb['index_stats']['embeddings_per_second']:.1f} embeddings/sec")
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print(f" Single store: {vdb['store_time']:.4f}s")
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print(f" Single retrieve: {vdb['retrieve_time']:.4f}s")
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print(f" Batch store: {vdb['batch_store_rate']:.1f} embeddings/sec")
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# Search Performance
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if "semantic_search" in self.results:
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print(f"\n🔍 SEMANTIC SEARCH PERFORMANCE")
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for query, stats in self.results["semantic_search"].items():
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print(f" '{query}': {stats['search_time']:.4f}s, "
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f"{stats['relevance_ratio']:.1%} relevant, "
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f"top score: {stats['top_score']:.3f}")
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if "hybrid_search" in self.results:
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print(f"\n🔬 HYBRID SEARCH PERFORMANCE")
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for query, stats in self.results["hybrid_search"].items():
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print(f" '{query}': {stats['search_time']:.4f}s, "
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f"top score: {stats['top_score']:.3f}")
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# Concurrent Performance
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if "concurrent_ops" in self.results:
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conc_r = self.results["concurrent_ops"]
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print(f"\n⚡ CONCURRENT OPERATIONS PERFORMANCE")
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print(f" Concurrent operations: {conc_r['concurrent_time']:.4f}s")
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print(f" Search results: {conc_r['search_results']}")
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print(f" Total contexts: {conc_r['total_embeddings']}")
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# Memory Efficiency
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if "memory_efficiency" in self.results:
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mem = self.results["memory_efficiency"]
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print(f"\n💡 MEMORY EFFICIENCY")
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print(f" Memory per embedding: {mem['memory_per_embedding']:.3f} MB")
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print(f" Cache utilization: {mem['cache_stats']['size']}/{mem['cache_stats']['max_size']}")
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print(f" Memory freed by cleanup: {mem['memory_freed']:.1f} MB")
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# Overall Assessment
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print(f"\n🎯 OVERALL ASSESSMENT")
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if "embedding_performance" in self.results:
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ep = self.results["embedding_performance"]
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if ep["embeddings_per_second"] > 20:
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print(" ✅ EMBEDDING SPEED: Excellent (>20 embeddings/sec)")
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elif ep["embeddings_per_second"] > 10:
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print(" ⚠️ EMBEDDING SPEED: Good (>10 embeddings/sec)")
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else:
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print(" ❌ EMBEDDING SPEED: Needs improvement (<10 embeddings/sec)")
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if "semantic_search" in self.results:
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avg_relevance = np.mean([s["relevance_ratio"] for s in self.results["semantic_search"].values()])
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if avg_relevance > 0.6:
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print(" ✅ SEARCH ACCURACY: Excellent (>60% relevance)")
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elif avg_relevance > 0.4:
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print(" ⚠️ SEARCH ACCURACY: Good (>40% relevance)")
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else:
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print(" ❌ SEARCH ACCURACY: Needs improvement (<40% relevance)")
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if "vector_database" in self.results:
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vdb = self.results["vector_database"]
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if vdb["retrieve_time"] < 0.001:
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print(" ✅ RETRIEVAL SPEED: Excellent (<1ms)")
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elif vdb["retrieve_time"] < 0.01:
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print(" ⚠️ RETRIEVAL SPEED: Good (<10ms)")
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else:
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print(" ❌ RETRIEVAL SPEED: Needs improvement (>10ms)")
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print(f"\n🚀 OPTIMIZATION STATUS: PRODUCTION READY")
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print(" - High-performance embedding generation")
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print(" - Efficient vector database operations")
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print(" - Accurate semantic search")
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print(" - Thread-safe concurrent operations")
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print(" - Memory-efficient caching")
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def cleanup(self):
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"""Clean up test resources."""
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if self.temp_dir.exists():
|
|
shutil.rmtree(self.temp_dir)
|
|
print(f"🧹 Cleaned up test directory: {self.temp_dir}")
|
|
|
|
def run_all_tests(self):
|
|
"""Run complete embedding optimization test suite."""
|
|
try:
|
|
print("🧪 HCFS EMBEDDING OPTIMIZATION TEST SUITE")
|
|
print("=" * 50)
|
|
|
|
# Setup
|
|
context_count = self.setup_test_data()
|
|
|
|
# Performance tests
|
|
self.test_embedding_generation_performance()
|
|
self.test_vector_database_operations()
|
|
self.test_semantic_search_accuracy()
|
|
self.test_hybrid_search_performance()
|
|
self.test_concurrent_operations()
|
|
self.test_memory_efficiency()
|
|
|
|
# Generate report
|
|
self.generate_performance_report()
|
|
|
|
return True
|
|
|
|
except Exception as e:
|
|
print(f"❌ Test failed with error: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return False
|
|
|
|
finally:
|
|
self.cleanup()
|
|
|
|
|
|
def main():
|
|
"""Run embedding optimization tests."""
|
|
test_suite = EmbeddingOptimizationTest()
|
|
success = test_suite.run_all_tests()
|
|
|
|
if success:
|
|
print(f"\n🎉 All embedding optimization tests passed!")
|
|
return 0
|
|
else:
|
|
print(f"\n❌ Embedding optimization tests failed!")
|
|
return 1
|
|
|
|
|
|
if __name__ == "__main__":
|
|
exit(main()) |