future.result

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

Here's an example implementation using a thread pool and Kafka: ```python import concurrent.futures import threading from kafka import KafkaProducer # Kafka producer setup producer = KafkaProducer(bootstrap_servers='localhost:9092') def…

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: …

- 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…

logging.basicConfig(level=logging.ERROR, format='%(asctime)s - %(levelname)s - %(message)s') # Load the model once model = SentenceTransformer('paraphrase-MiniLM-L6-v2') def vectorize_document(doc, retries=3, delay=1): for attempt in …

logging.error(f"Failed to vectorize document after {retries} retries: {e}") return None def vectorize_pipeline(docs, max_workers=None): vectors = [] with ThreadPoolExecutor(max_workers=max_workers) a…

Ensure that frequently accessed data is cached and accessed quickly. ### 6. Use Efficient Parallel Processing Optimize the number of threads and ensure that tasks are evenly distributed. ### 7. Use Asynchronous Programming Consider using …

futures = {executor.submit(self.rewrite_query, query): query for query in queries} for future in as_completed(futures): rewritten_queries.append(future.result()) return rewritten_queries …