warning level

response = make_request(prompt) if response: return response if attempt < retries: logging.warning(f"Attempt {attempt + 1} failed. Retrying in {delay} seconds...") …

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: …

3. **Logging**: - Implement detailed logging to track the progress and errors during metadata extraction. 4. **Configuration**: - Customize Tika's behavior by configuring it through its API or using command-line arguments. ### Examp…

- Implement a retry mechanism within the `vectorize_document` function. - Retry up to a specified number of times (`retries`) with a delay between attempts (`delay`). 4. **Detailed Error Reporting**: - Log detailed error informati…

- 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…

2. **Profile the Code**: Use profiling tools to identify bottlenecks. 3. **Monitor Resource Usage**: Track CPU, memory, and I/O usage to understand resource consumption. 4. **Log Detailed Metrics**: Capture detailed metrics for analysis. 5.…

- Monitor the system to ensure it achieves the desired performance. - Use monitoring tools to track resource usage and identify any bottlenecks. ### Example Code Here is the code again for your reference: ```python import logging i…

logger.warning(f"Unexpected error on attempt {attempt}: {e}") if attempt == retries: logger.error("Max retries reached. Message consumption failed.") break # Example usage consume_messag…

app = FastAPI() # Simulated database mock_database = { "valid_token": True, "invalid_token": False } # Asynchronous token validation function with caching @lru_cache(maxsize=128) async def validate_token(token: str) -> bool: #…

# Calculate score mismatches mismatches = np.abs(sparse_scores - dense_scores) # Find indices where mismatches exceed the threshold mismatch_indices = np.where(mismatches > threshold)[0] # Log detailed informat…

- Test the logging changes incrementally to ensure they do not break existing functionality. - Verify that the logs are being generated correctly and contain the necessary information. 6. **Integrate with Centralized Logging**: - …

# Check 6: Data protection by design if not has_data_protection_by_design(data): logging.warning('Data protection by design is not implemented') # Check 7: Data protection by default if not has_data_protection_b…

7. **Encryption of Logs**: - Ensure that all logs are encrypted both at rest and in transit. - Use strong encryption algorithms like AES-256. 8. **Secure Transmission**: - Use TLS/SSL for secure transmission of logs. - Ensure t…

Document(id=2, metadata={'key': 'wrong_value'}, retrieval_time=datetime.now() + timedelta(milliseconds=150), expected_metadata={'key': 'value'}), # Add more documents as needed ] # Log the metadata mismatches and delays for doc in …

corrected_query = ' '.join(words) # log the result logging.info(f'Successfully corrected query: {query} -> {corrected_query}') self.success_count += 1 except Exception as …