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Ensure that you are using efficient data structures and algorithms to minimize overhead. ### Example Using `concurrent.futures` for Parallel Processing Here's an optimized version of your code using `concurrent.futures` to process user re…

for future in concurrent.futures.as_completed(futures): user_id = futures[future] try: response, response_time = future.result() response_times.append(response_t…

- Monitor the system to ensure it achieves the desired performance. - Use monitoring tools to track resource usage and identify any bottlenecks. ### Enhanced Code with Error Handling and Retry Logic Here is the enhanced code again f…

return [1.0, 2.0, 3.0] def process_documents(documents): vectors = [] with ThreadPoolExecutor(max_workers=10) as executor: futures = [executor.submit(vectorize_document, document) for document in documents] for …

futures = [self.executor.submit(self.query_handler.handle_query, query) for query in queries] results = [future.result() for future in futures] return results # Example usage queries = [ "What is the capital of …

futures = [executor.submit(self.model.batch_reformulate, queries[i:i+batch_size]) for i in range(0, len(queries), batch_size)] results = [] for future in as_completed(futures): results.ext…