Shutdown executor comment

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') # Define the API endpoint endpoint = 'https://api.example.com/endpoint' # Define the request payload payload = {'key': 'value'} # Initialize a co…

# Define a function to generate answers def generate_answer(question): # Tokenize the question inputs = tokenizer(question, return_tensors="pt") # Generate the answer outputs = model.generate(**inputs) # Decode the ans…

### Example Code with Debugging Steps Let's walk through the code and add some debugging steps to identify the issue. #### 1. Verify Weaviate Server Status Ensure the Weaviate server is running and accessible. ```python import weaviate …

# Remote logging server REMOTE_LOGGING_URL = 'https://your-remote-logging-server.com/api/log' def send_remote_log(message): try: response = requests.post(REMOTE_LOGGING_URL, json={'message': message}) response.raise_for…

logging.warning('Logs are not stored securely') # Check 3: Ensure access controls are in place if not logs['access_controls']: logging.warning('Access controls are not in place') # Check 4: Ensure audit trails …

def process_segment_with_llm(segment): # Placeholder function to simulate LLM processing return f"Processed {segment}" # Example usage if __name__ == "__main__": max_tokens = 100 # Example max token limit overlap = 20 # E…

[Turn 8160] User: I'm trying to implement a dynamic context window resizing algorithm based on query complexity, but I'm not sure how to handle edge cases, can you provide an example of how to handle queries with high complexity and low com…

class ResizingModule(nn.Module): def __init__(self): super(ResizingModule, self).__init__() self.fc1 = nn.Linear(512, 128) self.fc2 = nn.Linear(128, 128) def forward(self, x): x = torch.relu(self.fc1…

def calculate_term_frequencies(documents): # Flatten the list of documents into a single list of terms all_terms = [term for document in documents for term in document] # Use Counter to count the frequency of each term …

# Rerank the results reranked_results = rerank(results) # Log the success logger.info("Results reranked successfully") return reranked_results except RerankScoreError as e: # Log …

logging.basicConfig(level=logging.ERROR, format='%(asctime)s - %(levelname)s - %(message)s') # Define key rotation function def rotate_key(operation): try: # Simulate key rotation logic time.sleep(0.001) # Simulate a s…

improved_percentage = (improved_steps / steps) * 100 # Initialize a dictionary to store the metrics metrics = { 'Improved Steps': improved_steps, 'Improved Percentage': improved_percentage } # A…

logging.error(f'Error in PostProcessor for text "{text}": {e}') return text # Define the evaluation function def evaluate_reformulation(stages, inputs, outputs): # Apply the reformulation stages to the inputs …