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futures = {executor.submit(vectorize_document, doc): doc for doc in docs} for future in as_completed(futures): try: vectors.append(future.result()) except Exception as e: …

from sentence_transformers import SentenceTransformer from concurrent.futures import ThreadPoolExecutor, as_completed # Load the model once model = SentenceTransformer('paraphrase-MiniLM-L6-v2') def vectorize_document(doc): return mod…

for future in as_completed(futures): try: vectors.append(future.result()) except Exception as e: print(f"Error processing document: {e}") return vectors # Example usage do…

- Monitor the system to ensure it achieves the desired performance. - Use monitoring tools to track resource usage and identify any bottlenecks. ### Example Usage Ensure you replace the placeholder documents with your actual data: …

with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = {executor.submit(vectorize_document, doc): doc for doc in docs} for future in as_completed(futures): try: vectors.append…

Document(id=2, metadata={'key': 'wrong_value'}, retrieval_time=datetime.now() + timedelta(milliseconds=150), expected_metadata={'key': 'value'}), # Add more documents as needed ] # Log the metadata mismatches and delays for doc in …