```

from pydantic import BaseModel app = FastAPI() class QueryRequest(BaseModel): query: str class QueryResponse(BaseModel): results: list @app.post("/retrieve", response_model=QueryResponse) def retrieve(query_request: QueryRequest…

#### 3. Define Training Arguments ```python # Define training arguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=2, # Smaller batch size for CPU per_device_…

# Output: ['Task 1', 'Task 45', 'Task 2', 'Task 4', ..., 'Task 50'] print(matrix.get_tasks_for_position("Engineer 2")) # Output: ['Task 1', 'Task 2', 'Task 4', ..., 'Task 50'] print(matrix.get_tasks_for_position("Engineer 3")) # Output: […

es = Elasticsearch() def create_pipeline(index_name): # Create a new pipeline pipeline = { 'description': 'My pipeline', 'processors': [ {'set': {'field': '_index', 'value': index_name}}, {'r…

How can I make this function more effective at detecting GDPR compliance issues and providing actionable recommendations for remediation, maybe by using a more advanced regex pattern or integrating with a compliance auditing tool? ->-> 10,2…

client = redis.Redis(host='node3', port=6379, db=0) # Cache the data client.set(data, True) ``` Can you help me optimize my load balancing strategy and ensure that the cache load is distributed evenly across the nodes? ->-> …

5. **Data Retention Policies**: Define and enforce data retention policies. 6. **Secure Storage**: Use secure storage mechanisms like encrypted Redis or other secure caching solutions. ### Example Implementation Here's an improved version…

Hash the identifier to generate a consistent seed. This ensures that the same identifier always produces the same seed, regardless of the environment. ### 3. **Initialize the Random Number Generator** Use the generated seed to initialize t…

- If the loss oscillates or diverges, you might need to decrease the learning rate (e.g., \(0.0005\) or \(0.0001\)). 3. **Use Learning Rate Schedules**: - Implement learning rate schedules such as step decay, exponential decay, or co…

Here's an enhanced version of your code with improved error handling and logging: ```python import traceback class DocFormatError(Exception): pass def save_documentation(doc_id, user_id, document_data): try: # Simulate sa…

2. **Tokenization**: Tokenization can also be a bottleneck. Ensure you are using efficient tokenization settings. 3. **Batch Processing**: If possible, process queries in batches to reduce overhead. ### Example Optimization If the `model.…

5. **Batch Processing**: Ensure that batch processing is used to minimize overhead. 6. **Data Structures**: Use efficient data structures to store and manipulate data. 7. **Monitoring and Profiling**: Regularly monitor and profile the code …

# Initialize a thread pool executor = ThreadPoolExecutor(max_workers=10) def tokenize_data(data): # Simulate tokenization logic return [f"token_{item}" for item in data] class TokenizeMulti(Resource): def __init__(self): …