1 hour

- If the result is cached, return the cached value and measure the latency. 4. **Perform Authentication**: - If the result is not cached, perform the actual authentication. - After authentication, cache the result in Redis with an…

- Use `asyncio` and `await` to handle asynchronous requests efficiently. - Ensure that `kc.token_async` is used for asynchronous token retrieval. 2. **Caching**: - Use `aiocache` with Redis to cache tokens. - Check the cache fi…

1. **Use Expiry Times**: Ensure that each cached item has a reasonable expiry time to prevent stale data. 2. **Cache Invalidation**: Implement a mechanism to invalidate the cache when the underlying data changes. 3. **Versioning**: Use vers…

hit_rate = (self.metrics['hits'] / self.metrics['total_requests']) * 100 if self.metrics['total_requests'] > 0 else 0 miss_rate = (self.metrics['misses'] / self.metrics['total_requests']) * 100 if self.metrics['total_request…

self.cache_layer.set(query, result, ttl=3600) # Set TTL to 1 hour return result def _execute_actual_query(self, query): # Placeholder for actual query execution logic return f"Result for {query}" ``` #…

redis-server /path/to/redis.conf ``` ### Step 2: Implement Caching in Your Application Use the `redis-py` library to interact with Redis from your Python application. Here is an example of how to set up caching for log summaries: `…

5. **Time-Based Estimation for Detailed Tasks**: - For Task 1, estimate the time required for each activity: - Activity 1.1: 2 hours - Activity 1.2: 1 hour - Total: 3 hours 6. **Regular Review**: - Daily stand-ups to d…

future = executor.submit(evaluate_test, test_data) futures.append(future) # Wait for all futures to complete for future in concurrent.futures.as_completed(futures): try: …

- Use `json.dumps` to serialize the `synonym_results` dictionary into a JSON string. This is necessary because Redis stores data as strings. 2. **Set the Cache**: - Use `redis_client.set` to store the serialized data in Redis under t…

for future in as_completed(futures): results.extend(future.result()) return results # Example usage: queries = ["What is the capital of France?", "Who is the president of the United States?", ...] reformulated_q…

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

- `batch_reformulate` method processes multiple queries in a single batch. - This reduces the overhead of tokenization and leverages parallel processing. 4. **Parallel Execution with `ThreadPoolExecutor`**: - `ThreadPoolExecutor` …

print(f"Error retrieving cached tokens: {str(e)}") return None # Example usage tokens = [{"id": 1, "text": "This is an example token."}] # Cache the tokens cache_tokens(tokens, ttl=3600) # Retrieve the cached tokens cache…