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for i in range(0, len(documents), batch_size): batch = documents[i:i + batch_size] with Pool(processes=os.cpu_count()) as pool: pool.map(ingest_document, batch) def main(): documents = [f"document_{i}" f…

:param weights: Dictionary of weights for each factor :return: Weighted score """ weighted_score = sum(option_scores[factor] * weights[factor] for factor in option_scores) return weighted_score def main(): # Define …

2. **Asynchronous Processing**: Use asynchronous execution to handle multiple queries concurrently. 3. **Batch Processing**: Batch similar queries together to reduce overhead. 4. **Optimize Network Calls**: If the delay is due to network ca…

- Frequently accessed data is stored in high-performance tiers (Tier 1 and Tier 2), ensuring quick access and minimal downtime during recovery. 3. **Offsite Backups:** - Tier 4 (cloud storage) and Tier 5 (physical backup) provide off…

except requests.exceptions.RequestException as err: print(f'Something went wrong: {err}') # Send 10,000 API requests for i in range(10000): send_request(f'https://example.com/api/request/{i}') ``` ->-> 9, [Turn 5751] Assis…

dense_scores = np.random.rand(num_queries, num_documents) # Test queries test_queries = np.random.rand(num_queries, num_documents) predictions = [] for i in range(num_queries): query = test_queries[i] sparse_scores_i = sparse_scor…

if log_entry is None: break try: logger.handle(log_entry) except Exception as e: logger.error(f"Failed to log entry: {e}") q.task_done() # Start the log processing thread …

for i in range(1, len1 + 1): for j in range(1, len2 + 1): if token1[i - 1] == token2[j - 1]: dp[i][j] = dp[i - 1][j - 1] else: dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1]…