Resource Cleanup

data = {'message': 'Data retrieved successfully'} return jsonify(data) except TimeoutException as e: return jsonify({'error': str(e)}), 504 finally: # Cancel the alarm signal.alarm(0) if __na…

conn.commit() # Function to get all risk profiles def get_all_risk_profiles(): cursor.execute('SELECT * FROM RiskProfile') return cursor.fetchall() # Insert a new risk profile insert_risk_profile('Service Availability', 'High'…

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If you are deploying this in a production environment, consider using a load balancer to distribute the load across multiple instances. ### 4. Measure and Monitor Performance Use performance monitoring tools to measure and optimize the re…

# Initialize any resources or connections here logging.info("Starting up...") @app.on_event("shutdown") async def shutdown_event(): # Clean up any resources or connections here logging.info("Shutting down...") ``` ### Expl…

curl -X PUT "http://localhost:8000/api/v1/team-tasks/" -H "Content-Type: application/json" -d '{"task_id": -1, "role": "manager"}' ``` 3. **Invalid Input (Empty Role):** ```bash curl -X PUT "http://localhost:8000/api/v1/team-ta…

Now, create a modular document processor that can handle multiple processors. ```python class ModularDocumentProcessor: def __init__(self): self.processors = {} def register_processor(self, file_extension, processor): …

request_timeout_ms=30000 # Maximum time to wait for a request to complete ) try: # Send a message future = producer.send('my_topic', value='Hello, world!') # Block until the message is sent or timeout result = fut…

''', [(entry[0], entry[1], entry[2]) for entry in metadata_entries]) conn.commit() logger.info("Metadata extraction and storage completed.") # Specify the directory path directory_path = '/path/to/documents' # Extract…

} } } }, 'mappings': { 'properties': { 'title': { 'type': 'text', 'similarity': 'my_similarity' …

4. **Proper Exception Handling**: Include proper exception handling and resource cleanup. ### Additional Considerations - **Scroll API**: If you need to fetch large result sets, consider using the Scroll API. - **Bulk Requests**: If you a…

Add error handling to ensure that any issues encountered during log processing are captured and logged. ### Example Optimized Code Here's an optimized version of your code incorporating these suggestions: ```python import logging import …

1. **Asynchronous Logging**: - Use `QueueHandler` and `QueueListener` to offload logging to a separate thread. - This reduces the impact on the main application thread and helps handle high volumes of log entries more efficiently. 2.…

for _, row in batch.iterrows(): query = row['query'] # Process the query result = process_query(query) # Store or use the result print(result) def process_query(query): # Simulate some memory…

} }) # Bulk index some data documents = [ {'_index': index_name, '_source': {'text': 'This is some example text'}}, {'_index': index_name, '_source': {'text': 'Another example text'}}, {'_index': index_name, '_source': {'te…

Here's how you can implement parallel processing using Python's `concurrent.futures` module, which provides a high-level interface for asynchronously executing callables: ### Example Implementation ```python import time from concurrent.fu…

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…