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from sentence_transformers import SentenceTransformer from concurrent.futures import ThreadPoolExecutor, as_completed # Load the model once model = SentenceTransformer('paraphrase-MiniLM-L6-v2') def vectorize_document(doc): return mod…

- Monitor the system to ensure it achieves the desired performance. - Use monitoring tools to track resource usage and identify any bottlenecks. ### Enhanced Code with Error Handling and Retry Logic Here is the enhanced code again f…

logging.error(f"Failed to vectorize document after {retries} retries: {e}") return None def vectorize_pipeline(docs, max_workers=None): vectors = [] with ThreadPoolExecutor(max_workers=max_workers) a…

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…

"text": { "type": "text" } } } } es.indices.create(index='my_index', body=settings) # Index some documents using bulk indexing docs = [ {'_index': 'my_index', '_id': 1, 'text': 'This …

3. **Parallel Processing**: Use parallel processing to handle multiple batches concurrently. 4. **Reducing Overhead**: Minimize unnecessary operations and ensure that spaCy is used optimally. ### Step-by-Step Optimization 1. **Profiling**…

By following this approach, you can integrate spaCy for tokenization and handle high-throughput query rewriting with the required performance and uptime. [Turn 9876] User: I've been using spaCy 3.7.2 for tokenization, and I'm impressed by …