Example Usage

1. **Nested Loops**: The nested loops iterate over each document and each term within the document, which can be inefficient for large datasets. 2. **Dictionary Operations**: Dictionary lookups and insertions can be costly, especially if th…

from redis.connection import ConnectionPool from functools import lru_cache # Configure Redis client with connection pooling pool = ConnectionPool(host="localhost", port=6379, db=0, max_connections=100) redis_client = redis.Redis(connectio…

password=password, host=host, database=database, connect_timeout=10 # Timeout in seconds ) return cnx except mysql.connector.Error as err: logging.error(f"Error co…

def authenticate_user(self, user): # Implement MFA authentication pass def log_action(self, user, action): # Log the action and retain for at least 90 days pass def enforce_policy(self, user, ac…

vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(documents) query_vector = vectorizer.transform([query]) similarity_scores = (query_vector * tfidf_matrix.T).toarray() return similarity_scores def h…

level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler("debug_training.log"), logging.StreamHandler() ] ) # Define a custom dataset class for our queries class…

Returns: tuple: Tuple containing distances and indices of the nearest neighbors. """ return self.index.search(query_embedding, k) # Example usage if __name__ == "__main__": # Create instances of the modu…

1. **Configure Structured Logging**: - Use `structlog` to configure structured logging with JSON rendering. - Set up the logger to handle debug-level messages. 2. **Asynchronous Logging**: - Use `QueueHandler` and `QueueListener` …

avg_val_loss = total_val_loss / len(val_loader) print(f"Validation Loss: {avg_val_loss:.4f}") return model ``` ### Example Usage Here's how you can use the above components to integrate your reranking logi…

import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from concurrent.futures import ThreadPoolExecutor, as_completed import redis class ReformulationModel: def __init__(self): self.model = AutoModelForSeq2…

with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = {executor.submit(vectorize_document, doc): doc for doc in docs} for future in as_completed(futures): try: vectors.append…

tokenizer_service = TokenizerService('bert-base-uncased', 512) input_text = 'This is a sample input text that needs to be segmented and processed.' chunks = tokenizer_service.segment(input_text) print(chunks) ``` #### Model Inference Servi…

4. **Caching**: Use caching to reduce the load on the underlying data store. ### Optimized Implementation Here's an improved version of your `SynonymLookupModule`: 1. **Use `defaultdict` for Multiple Synonyms**: This allows storing multi…