@cache

By following these strategies and using the provided code example, you can effectively integrate vector search with approximate nearest neighbors to achieve better search results and performance. If you have any specific questions or need f…

5. **Connection Pooling**: Use connection pooling to manage database connections more efficiently. 6. **Compression**: Compress data before sending it over the network to reduce transfer time. ### Example Code with Caching Your provided c…

def fetch_data(lang): # Simulate fetching data time.sleep(1) return {"result": f"Query result for {lang}"} return jsonify(fetch_data(language)) # Example usage if __name__ == '__main__': app.run(deb…

```python import aiohttp import asyncio async def fetch(session, url): async with session.get(url) as response: return await response.json() async def main(): async with aiohttp.ClientSession() as session: tasks = …

1. **Install Required Libraries**: Ensure you have `joblib` installed. You can install it using pip if you haven't already: ```bash pip install joblib ``` 2. **Define Cache Location**: Choose a location to store the cache fi…

Monitor the cache hit ratio and adjust the cache timeouts and invalidation logic as needed. ### Example Implementation Here's how you can implement caching using Flask and `flask_caching` with Redis: #### 1. Install Dependencies First, …

return jsonify({"message": "Training documents retrieved successfully"}) # Cache the results for 1 minute @cache.cached(timeout=60) def get_cached_training_docs(): return get_training_docs() if __name__ == '__main__': app.run(…