futures

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…

# Example: Add costs based on query parameters cost += query['param1'] * 100 cost += query['param2'] * 50 return cost def process_query(monitor, query): monitor.monitor_cost(query) def main(): monitor = CostMonitor…

with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor: futures = [executor.submit(process_query, monitor, query) for query in queries] concurrent.futures.wait(futures) print(f"Total Costs: {monitor.get_costs()}") `…

2. **Submit Tasks**: Submits tasks to the executor and stores the futures. 3. **Collect Results**: Collects results as they become available using `as_completed`. ### Performance Considerations: - **Thread Pool Size**: Adjust the `max_work…

- Use multi-threading or asynchronous programming to improve CPU utilization. 2. **Optimize Memory Usage:** - Use memory profiling tools to identify memory leaks and inefficiencies. - Implement caching mechanisms to reduce memory …

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 …

# Placeholder for actual document processing logic pass class ModularIngestionSystem: def __init__(self): self.tasks = [] def add_task(self, task: IngestionTask): self.tasks.append(task) …

- `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…

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: …

[Turn 4725] Assistant: Certainly! To scale your vectorization process to handle 3,500 documents per hour with under 200ms processing time, you can leverage parallel processing. One effective way to achieve this is by using Python's `concurr…

- Test the pipeline to ensure it handles errors and retries correctly. - Verify that the system can handle 3,500 documents per hour with under 200ms processing time. 3. **Monitor Performance**: - Monitor the system to ensure it ac…

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…

Here is the code again for your reference: ```python import numpy as np from sentence_transformers import SentenceTransformer from concurrent.futures import ThreadPoolExecutor, as_completed # Load the model once model = SentenceTransforme…

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…

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…

# Configure logging logging.basicConfig(level=logging.INFO, 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): …

logging.warning(f"Attempt {attempt + 1}/{retries}: Error vectorizing document: {e}. Retrying in {delay} seconds...") time.sleep(delay) else: logging.error(f"Failed to vectorize doc…

- Define a function `process_batch` to process a batch of texts using `nlp.pipe`. 4. **Parallel Processing**: - Define a function `process_texts_in_parallel` to process texts in parallel using `ThreadPoolExecutor`. - Split the tex…

4. **Batch Processing**: - Define `process_batch` to process a batch of texts using `nlp.pipe`. 5. **Parallel Execution**: - Define `process_texts_in_parallel` to process texts in parallel using `ThreadPoolExecutor`. - Split the t…

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(…

- Use `lru_cache` to cache the results of tokenization to avoid redundant processing. 3. **Batch Processing**: - Define `process_batch` to process a batch of texts using `nlp.pipe`. 4. **Parallel Execution**: - Define `process_te…

print(f"Processed {len(test_texts)} queries in {end_time - start_time:.2f} seconds") # Get the current memory snapshot snapshot = tracemalloc.take_snapshot() # Print the top 10 memory blocks top_stats = snapshot.statistics('lineno') for s…

self.complexity_calculator = ComplexityCalculator() self.window_resizer = WindowResizer() self.query_handler = QueryHandler(self.complexity_calculator, self.window_resizer) self.executor = ThreadPoolExecutor(…

super(FeedbackModel, self).__init__() self.fc1 = nn.Linear(128, 128) self.fc2 = nn.Linear(128, 128) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.fc2(x) return x def process…

def worker(data_loader): local_model = MyModel() local_optimizer = optim.Adam(local_model.parameters(), lr=0.001) update_model(local_model, local_optimizer, data_loader) return local_model.state_dict(), local_optimizer.state…

data = data.to(device) optimizer.zero_grad() outputs = model(data) loss = nn.MSELoss()(outputs, data) loss.backward() optimizer.step() # Generate synthetic data num_queries = 3500 batch_size …

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() …

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Initialize the model model = ScoringModel() pipeline = EvaluationPipeline(model, device='cuda' if torch.cuda.is_available() else …

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) ```…

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 …

return rewritten_queries def consume_queries(channel, queue_name): def callback(ch, method, properties, body): query = body.decode('utf-8') rewriter = QueryRewriter() rewritten_query = rewriter.rewrite_q…

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…

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…

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…

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 = [] for i in range(0, len(queries), batch_size): batch…

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…

return reformulated_queries # Test the function with 500 queries per second queries = [...] # list of 500 queries # Batch processing batch_size = 100 batches = [queries[i:i + batch_size] for i in range(0, len(queries), batch_size)] …