ReformulationModel

[Turn 10415] Assistant: Great! Let's break down the steps to optimize your query reformulation pipeline. We'll start by using a smaller model like `t5-small`, implement batch processing, and use `ThreadPoolExecutor` for concurrency. Finally…

2. **Concurrency**: Use threading or multiprocessing to handle multiple queries concurrently. 3. **Caching**: Use Redis to cache frequent queries and their reformulated versions to reduce the load on the model. 4. **Efficient Tokenization**…

3. **Redis Configuration**: Ensure Redis is properly configured and accessible from your application. ### Next Steps 1. **Implement Batch Processing**: Modify the `reformulate` and `batch_reformulate` methods to handle batches. 2. **Use `…

outputs = self.model.generate(**inputs) reformulated_query = self.tokenizer.decode(outputs[0], skip_special_tokens=True) self.redis_client.set(query, reformulated_query, ex=3600) # Cache for 1 hour return re…

def reformulate(self, query): cached_result = self.redis_client.get(query) if cached_result: return cached_result.decode('utf-8') inputs = self.tokenizer(f"reformulate: {query}", return_tensors="pt")…