timer_decorator

2. **Concurrency**: Use threading or multiprocessing to handle multiple queries concurrently. 3. **Caching**: Cache frequent queries to avoid redundant processing. 4. **Model Optimization**: If you are using a machine learning model, consid…

redis_client = redis.Redis(host='localhost', port=6379, db=0) @lru_cache(maxsize=1000) def cached_reformulate_query(query): cached_result = redis_client.get(query) if cached_result: return cached_result.decode('utf-8') …

[Turn 10445] Assistant: Great! Let's summarize the steps you'll take to implement batch processing, concurrency, and profiling. We'll also discuss how to handle caching for frequent queries. ### Step 1: Implement Batch Processing and Concu…

5. **Profiling**: We use `cProfile` to profile the `batch_reformulate_queries` function and identify bottlenecks. ### Next Steps 1. **Run the Code**: Execute the code to see the performance improvements and identify any bottlenecks. 2. **…

[Turn 10453] Assistant: Certainly! Using Redis for caching can significantly reduce the latency of your query reformulation by storing frequently accessed queries and their reformulated versions. Here's a detailed example of how to configur…

Implement functions to cache and retrieve reformulated queries. ### Example Implementation Here's a complete example of how to use Redis for caching in your query reformulation pipeline: ```python import redis import time from functools …