str

'ApplyServerSideEncryptionByDefault': { 'SSEAlgorithm': 'AES256' } } ] } try: s3.put_bucket_encryption( Bucket=bucket_name, ServerSideEncryptionConfiguration=encryptio…

Here's a complete example of how you can implement the compliance auditing system: ```python from flask import Flask, request, jsonify app = Flask(__name__) # Define the API endpoint for compliance auditing @app.route('/api/v1/compliance…

# Create a Kafka producer with optimized configurations producer = KafkaProducer( bootstrap_servers='localhost:9092', value_serializer=lambda v: json.dumps(v).encode('utf-8'), # Serialize messages as JSON batch_size=1048576, #…

import logging app = FastAPI() # Configure logging logging.basicConfig(level=logging.INFO) class TeamTask(BaseModel): task_id: int = Field(..., gt=0) role: str = Field(..., min_length=1) @app.exception_handler(RequestValidationE…

- The `ModularDocumentProcessor` class manages a dictionary of processors indexed by file extension. - It registers processors for different file extensions and processes documents based on their extension. - The `process_document`…

[Turn 4200] User: I'm working on the development roadmap, and I need to map 3 pipeline challenges for upcoming sprints, so I'd like to implement a pipeline logic to handle 1,000 concurrent uploads with 99.8% uptime, and I was wondering if y…

### Step 3: Integrate with SentenceTransformers and FAISS Ensure that you log any errors or critical information related to embedding generation and indexing. ```python from sentence_transformers import SentenceTransformer import faiss im…

return token except keycloak.exceptions.KeycloakError as e: # Handle authentication errors log_message('ERROR', f"Authentication error for user {username}", {'error': str(e)}) return None # FastAPI app a…

"client_secret": client_secret } # Create a Keycloak instance kc = keycloak.Keycloak(**keycloak_config) # Define a function to handle authentication async def authenticate(username, password): try: # Authenticate the user …

try: # Get the log message from the request body message = await request.json() log_message = message.get("message") if not log_message: raise HTTPException(status_code=400, detail="Message is…

- Create a route that accepts language and query parameters. - Generate a dynamic cache key based on the language and query parameters. - Use the cache to store and retrieve results. ### Example Code ```python from flask import F…

except OSError as e: logging.error(f"Failed to load SpaCy model: {e}") raise # Define a class to handle language tokenization class LanguageTokenizer: def __init__(self): self.nlp = nlp @lru_cache(maxsize=1000) …

Update your logging code to catch and log the `LogWriteError` specifically. ```python import logging # Configure logging logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') # Example of logging co…

raise ValueError(f"WindowSizeMismatchError: Query length ({len(query)}) exceeds window size ({window_size})") return query[:window_size] # Example usage query = "What is the capital of France?" try: resized_query = res…

return complexity / (len(query) + num_dependencies + 1) def resize_window(query, complexity): # Resize context window based on complexity base_window_size = 512 if complexity > 0.7: window_size = int(base_window_siz…

raise ValueError(f"WindowSizeMismatchError: Query length ({len(query)}) exceeds window size ({window_size})") return query[:window_size] # Example usage query = "What is the capital of France?" try: resized_query = res…

for epoch in range(100): for batch in data_loader: inputs = batch['query'].float().to(device) labels = batch['label'].long().to(device) optimizer.zero_grad() outputs = model(input…

print(f"Tokens: {tokens}") rewritten_query = rewrite_query(tokens) print(f"Rewritten query: {rewritten_query}") return rewritten_query except Exception as e: print(f"Caught exception: {e}") …

```python query = "test" # Check query validity check_query_validity(query) try: rewritten_query = parse_query(query) print(f"Rewritten query: {rewritten_query}") except Exception as e: print(f"Failed to parse query: {query} -…

es = Elasticsearch() # Prepare bulk indexing actions actions = [ { "_index": "my_index", "_source": record } for record in records ] …

except Exception as e: logging.error(f"Error caching query results: {str(e)}") return False def get_cached_query_results(query_id): try: # Create a Redis client redis_client = redis.Redis(host='local…