QueryRewriter

1. **Sleep Simulation**: The `time.sleep(0.01)` simulates a 10ms delay per query. To handle 1,500 queries per minute, you need to process each query in less than 4ms (since 60,000ms / 1,500 queries = 40ms/query). 2. **Sequential Processing…

3. **Performance Measurement**: Added timing to measure the total processing time for 1,500 queries. ### Further Optimization 1. **Batch Processing**: If the query rewriting logic can be batched, consider processing queries in batches to …

By using parallel processing and optimizing the query rewriting logic, you can achieve the required throughput of 1,500 queries per minute. The `ThreadPoolExecutor` helps in efficiently managing multiple threads, and batching can further re…

4. **Batch Processing**: Process queries in batches to manage the workload efficiently. ### Example Code Here's a complete example that integrates spaCy for tokenization and handles the parallel processing of queries: ```python import ti…

return rewritten_queries # Example usage: rewriter = QueryRewriter() queries = ["query1", "query2", "query3"] * 1000 # 3000 queries rewritten_queries = rewriter.handle_queries(queries) print(rewritten_queries) ``` ->-> 1,5 [Turn …

pass rewriter = QueryRewriter() query = "example query" rewritten_query = rewriter.rewrite_query(query) print(rewritten_query) ``` I'm looking for ways to improve this implementation, maybe someone can review my code and suggest so…

- Create unit tests to validate the parsing logic and ensure it can handle a wide range of input scenarios. 6. **Performance Optimization**: - Optimize the parsing logic to improve performance, especially for high-throughput scenario…

4. **Evaluate and Iterate**: - Continuously evaluate the accuracy of the rewritten queries. - Use feedback to refine and expand the rules. 5. **Logging and Monitoring**: - Implement logging to track the performance and identify is…

- Regularly evaluate the accuracy of the rewritten queries and use the results to improve the rules. By implementing these improvements, you can enhance the accuracy and efficiency of your query rewriting algorithm. [Turn 9902] User: I'…

def rewrite_query(self, query): try: expanded_query = self.expand_query(query) logging.info(f"Original Query: {query} | Expanded Query: {expanded_query}") return expanded_query except …

def apply_contextual_expansion(self, query): for context, expansion in self.contextual_expansions.items(): query = re.sub(r'\b' + re.escape(context) + r'\b', expansion, query) return query def process_qu…

By following these steps, you can effectively handle special characters and improve the robustness of your query rewriting pipeline. [Turn 9906] User: I'm looking for ways to optimize my query rewriting pipeline to handle a larger volume o…

(r'\bOR\b', 'ORELSE') ] self.contextual_expansions = { 'table': 'TABLE_NAME', 'condition': 'CONDITION_VALUE' } def rewrite_query(self, query): try: expande…