Functools import

The first loop will take longer because each query is unique and the function must simulate the delay. The second loop will be much faster because the repeated queries will be served from the cache. ### Example with External Caching (Redis…

1. **Define User Roles**: Define the different user roles and their corresponding rate limits in the `USER_ROLES` dictionary. 2. **Custom Key Function**: Create a custom key function `get_user_role` to identify the user role. This function…

- **Batch Queries:** Group similar queries together and process them in batches to reduce overhead. - **Asynchronous Processing:** Use asynchronous processing to handle multiple queries concurrently. ### 5. Monitoring and Feedback #### Re…

class QueryResult(BaseModel): id: int title: str content: str class QueryResponse(BaseModel): results: List[QueryResult] total_results: int ``` ### Step 3: Initialize Redis Client Initialize the Redis client and confi…

Implement a background task to refresh the cache before the TTL expires to avoid sudden spikes in latency. ### 5. Monitoring and Metrics Integrate monitoring and metrics to track cache performance and identify areas for improvement. ### 6…

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…

[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…