batches

# Generate 14,000 documents documents = [f'doc_{i}' for i in range(14000)] # Split documents into batches batch_size = 1000 batches = [documents[i:i + batch_size] for i in range(0, len(documents), batch_size)] # Add tasks to the system fo…

# Placeholder for actual document processing logic pass class ModularIngestionSystem: def __init__(self): self.tasks = [] def add_task(self, task: IngestionTask): self.tasks.append(task) …

3. **Parallel Processing:** - Uses `ThreadPoolExecutor` to run tasks concurrently. - The `max_workers` parameter controls the number of worker threads. 4. **Batch Processing:** - Documents are split into batches to manage memory a…

1. **Pandas DataFrame**: We use a Pandas DataFrame to simulate the document records. This allows us to leverage vectorized operations and efficient data handling. 2. **Parallel Processing**: The `joblib` library is used to parallelize the p…

- Define a function `process_batch` to process a batch of texts using `nlp.pipe`. 4. **Parallel Processing**: - Define a function `process_texts_in_parallel` to process texts in parallel using `ThreadPoolExecutor`. - Split the tex…

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) …

batch_size = 100 # Adjust batch size as needed batches = [texts[i:i + batch_size] for i in range(0, len(texts), batch_size)] with ThreadPoolExecutor(max_workers=num_workers) as executor: futures = {executor.submit(…

- Use `lru_cache` to cache the results of tokenization to avoid redundant processing. 3. **Batch Processing**: - Define `process_batch` to process a batch of texts using `nlp.pipe`. 4. **Parallel Execution**: - Define `process_te…

return reformulated_queries # Test the function with 500 queries per second queries = [...] # list of 500 queries # Batch processing batch_size = 100 batches = [queries[i:i + batch_size] for i in range(0, len(queries), batch_size)] …