Module Dependency

logging.info(f'Value {value} is within acceptable range.') # Example usage check_thresholds(80) check_thresholds(95) # Additional functionality to handle cases where logging plan is not shared def send_notification(value): if …

locals { role_arn = module.shared_resources.role_arn } ``` #### Retrieval Module (`modules/retrieval/main.tf`) ```hcl variable "role_arn" { type = string default = "" } resource "aws_instance" "retrieval" { ami = "am…

Action = "sts:AssumeRole" } ] }) # Add new permissions or modify existing ones policy = jsonencode({ Version = "2012-10-17" Statement = [ { Effect = "Allow" Action = [ "s3:Get…

# Validate input dimensions if sparse_scores.shape != dense_scores.shape: raise ValueError("Mismatched dimensions between sparse and dense scores") # Normalize scores to ensure they are on the same scale…

- Implement pre-fetching to anticipate and prepare for future queries. 5. **Load Balancing:** - Distribute the load between sparse and dense query processors to ensure balanced resource utilization. - Use load balancers to manage …

logger = logging.getLogger(__name__) @app.post("/search", response_model=SearchResponse) async def search(query: SearchQuery): try: sparse_results = call_sparse_retrieval(query) except HTTPException as e: logger.err…

# Simulate cache lookups start_time = time.time() latencies = [] for _ in range(14000): start_query_time = time.time() result = search_query("example") end_query_time = time.time() latencies.append(end_query_time - start_que…

- Process inputs in batches to leverage the parallelism offered by GPUs. - Use DataLoader for efficient batch processing. 3. **Optimize Model Execution**: - Ensure that the model is optimized for inference, such as using `torch.ji…

closest_word = find_closest_match(word, dictionary) if closest_word: corrected_words.append(closest_word) else: corrected_words.append(word) # Fallback to original word …