future

1. **Asynchronous Execution**: The `runAsync` method of `CompletableFuture` runs the given task asynchronously. Each service call is wrapped in a lambda function and executed asynchronously. 2. **Waiting for Completion**: The `allOf` metho…

[2025-04-04 05:23] lisamegawatts: I had a polisci professor that worked on this, he used to say theory is fine but no match for data https://correlatesofwar.org/ [2025-04-04 05:23] lisamegawatts: Trying to catalog and predict all factors th…

[2025-04-04 05:23] lisamegawatts: I had a polisci professor that worked on this, he used to say theory is fine but no match for data https://correlatesofwar.org/ [2025-04-04 05:23] lisamegawatts: Trying to catalog and predict all factors th…

REQUEST_TIME = Histogram('request_processing_seconds', 'Time spent processing request') def handle_request(user_id): with REQUEST_TIME.time(): # Simulate some processing time time.sleep(random.uniform(0.0…

```python import logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') class IngestionTask: def __init__(self, task_name: str, documents: List[str]): self.task_name = task_name …

- Implement `PDFProcessor` and `DOCXProcessor` classes that inherit from `DocumentProcessor`. - Each processor handles a specific document format and performs the required processing. 3. **Modular Document Processor:** - `ModularD…

- `JSONProcessor` reads JSON files and returns the data as a dictionary or list. 2. **Register New Processors:** - Register the new processors for CSV and JSON file extensions. 3. **Process Document:** - The `process_document` me…

latency_reduction = 120 # ms return latency_reduction def optimize_scalability(self): # Initialize optimization metrics total_latency_reduction = 0 total_threads_used = 0 # Use a Thread…

with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_threads) as executor: # Submit tasks to the executor futures = [executor.submit(self.process_document, document) for document in range(self.docu…

return None def update_metadata(metadata, file_path): if metadata: # Update metadata in the database # Placeholder for actual database update logic print(f"Updating metadata for {file_path}") else: …

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…

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…

try: future = producer.send(topic, value=data) record_metadata = future.get(timeout=10) # Wait for the message to be sent print(f"Message sent to topic {record_metadata.topic}, partition {record_…

from kafka import KafkaProducer from kafka.errors import KafkaError, KafkaTimeoutError, KafkaConnectionError, KafkaAuthenticationError import logging # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__…

batch_size = 100 # Adjust batch size as needed batches = [texts[i:i + batch_size] for i in range(0, len(texts), batch_size)] with ThreadPoolExecutor(max_workers=num_workers) as executor: futures = {executor.submit(…

futures = {executor.submit(process_query, query): query for query in queries} for future in concurrent.futures.as_completed(futures): try: result = future.result() results.append(r…

with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor: future = executor.submit(train_model, X, y) result = future.result() end_time = time.time() latency = end_time - start_time print(f'…

encrypted_data = cipher.encrypt(data) return encrypted_data def decrypt_data(encrypted_data, key): cipher = Fernet(key) decrypted_data = cipher.decrypt(encrypted_data) return decrypted_data def load_data(): # Place…

optimizer = optim.Adam(model.parameters(), lr=0.001) # Define the update logic def update_model(model, optimizer, data_loader): model.train() for data, _ in data_loader: data = data.to(device) optimizer.zero_grad() …

future = executor.submit(evaluate_test, test_data) futures.append(future) # Wait for all futures to complete for future in concurrent.futures.as_completed(futures): try: …

futures.append(executor.submit(pipeline.evaluate, batch)) # Collect results results = [future.result() for future in futures] # Flatten the results scores = np.concatenate(results) print(scores) ```…

with ThreadPoolExecutor(max_workers=num_workers) as executor: futures = {executor.submit(process_user, user_id, password, salt): user_id for user_id, password, salt in users} results = {} for future in as_completed(futures)…

self.access_count += 1 # Handle high access volume if self.access_count > 25000: print("High access volume detected") else: print("Normal access volume") retu…

rewritten_queries.extend(future.result()) return rewritten_queries def _process_batch(self, batch: List[str]) -> List[str]: rewritten_batch = [] for query in batch: rewritten_query =…

for future in as_completed(futures): results.extend(future.result()) return results # Example usage: queries = ["What is the capital of France?", "Who is the president of the United States?", ...] reformulated_q…

model = ReformulationModel() def process_queries(queries, batch_size=100, max_workers=10): with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor.submit(model.batch_reformulate, queries[i:i+batch_size…

for future in as_completed(futures): results.extend(future.result()) return results # Example usage: queries = ["What is the capital of France?", "Who is the president of the United States?", ...] reformulated_q…

def __init__(self): self.model = ReformulationModel() def process_queries(self, queries, batch_size=100, max_workers=10): with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor…

results = [] for future in as_completed(futures): results.extend(future.result()) return results class ReformulationService: def __init__(self): self.pipeline = ReformulationP…

with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor.submit(process_query_with_caching, query) for query in queries] results = [] for future in as_completed(futures): resu…

futures = [executor.submit(reformulate_query, query) for query in queries] for future in as_completed(futures): results.append(future.result()) return results ``` #### 5. Batch Processing Process queries in…

reformulated_query = query end_time = time.time() return reformulated_query, end_time - start_time # Define a function to process queries in batches def process_queries_in_batches(queries, batch_size=100): results = [] …

futures = [executor.submit(reformulate_query, query) for query in queries] for future in as_completed(futures): results.append(future.result()) return results # Define a function to tokenize queries def toke…

futures = [executor.submit(model.process, segment) for segment in batch] for future in as_completed(futures): processed_segments.append(future.result()) # Combine the processed segments m…

1. **Batch Processing**: Instead of processing each segment individually, process them in batches to reduce overhead. 2. **Parallel Processing**: Use parallel processing to handle multiple segments simultaneously. 3. **Efficient Memory Mana…

[Session date: 8:29 pm on 13 June, 2023] Jon: Hey Gina, thanks for being there for me and believing in me. It means a lot. Gina: Sure, Jon! I'm always around for you. How've you been since we caught up? Jon: I'm prepping for my dance studi…