Query List

Finally, you can combine the results from both sparse and dense retrievals. One common approach is to use a weighted sum of the scores from both methods. Here's a more complete example: ```python import numpy as np from sklearn.feature_ex…

# Further processing with the expanded query print(f"Processing expanded query: {expanded_query}") async def main(): queries = [ "What are the benefits of using machine learning for natural language processing?", …

vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(documents) query_vector = vectorizer.transform([query]) similarity_scores = (query_vector * tfidf_matrix.T).toarray() return similarity_scores def h…

queries = ["query1", "query2", "query3"] * 10000 # Generate 30,000 queries for query in queries: result = query_handler.execute_query(query) print(f"Result for {query}: {result}") ``` ### Step 4: Monitoring and Sc…

"Explain the theory of relativity and its impl", "What is the weather like today?", "Can you provide a detailed explanation of quantum mechan", "Who is the current president of the United States?", "What are the main com…

pipeline = Pipeline(context_window) queries = ['query1', 'query2', 'query3'] * 1000 # Example queries results = await pipeline.process_queries(queries) print(f'Processed {len(results)} queries.') if __name__ == '__main__':…

- **Optimization**: Use the timing information to identify bottlenecks and optimize the query rewriting logic. ### Example with Profiling You can use `cProfile` to profile the entire process: ```python import cProfile import pstats def …

- Utilized `ThreadPoolExecutor` from `concurrent.futures` to process queries in parallel. This leverages multiple CPU cores to handle the workload more efficiently. 3. **Batch Processing**: - Processed queries in batches by passing a…

def __init__(self): self.model = ReformulationModel() def process_queries(self, queries, batch_size=100, max_workers=10): with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor…