QueryProcessor

Let's assume you have two main modules: `SparseQueryModule` and `DenseQueryModule`. Here's how you can structure them: #### 1. SparseQueryModule - **Responsibilities:** - Handle sparse vector queries. - Use techniques like BM25 or TF-…

async def process_query(self, query: str) -> List[str]: pass class SparseQueryProcessor(QueryProcessor): async def process_query(self, query: str) -> List[str]: await asyncio.sleep(0.1) # Simulate processing time …

print(f"Sparse results: {sparse_results}") print(f"Dense results: {dense_results}") ``` ### Additional Considerations 1. **Concurrency and Parallelism:** - Use threading or multiprocessing to handle multiple queries concurrently. - …

- Implement pre-fetching to anticipate and prepare for future queries. 5. **Load Balancing:** - Distribute the load between sparse and dense query processors to ensure balanced resource utilization. - Use load balancers to manage …

- Distribute the load between sparse and dense query processors to ensure balanced resource utilization. - Use load balancers to manage the distribution of queries. ### Example Implementation Here's an example implementation in Pyth…

expanded_query.append(term) return ' '.join(expanded_query) def simulate_synonym_expansion(self, term): # Simulate the probability of correct synonym expansion return np.random.rand() < self.thre…

When you initialize the `QueryProcessor` with the optimal threshold, it will use this value to process queries and expand synonyms accordingly. ### Conclusion By integrating the optimal threshold into your query processing pipeline, you c…

[Turn 10569] Assistant: Certainly! Let's review your current implementation and suggest improvements to ensure it meets your performance requirements. Your goal is to process 2,500 queries per minute (approximately 41.67 queries per second)…