Query string generation

2. **Asynchronous Processing**: Use asynchronous execution to handle multiple queries concurrently. 3. **Batch Processing**: Batch similar queries together to reduce overhead. 4. **Optimize Network Calls**: If the delay is due to network ca…

- Train the index using the combined embeddings. - Add the embeddings to the index. 4. **Querying**: - Generate a query embedding using the same multilingual model. - Perform the search using the FAISS index. ### Additional Co…

print(f"Total latency for 10,000 queries: {total_latency:.2f} seconds") print(f"Average latency per query: {average_latency * 1000:.2f} ms") # Measure individual latencies individual_latencies = [] for query in queries: latency = measu…

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…

correct_count = 0 for query, expected in zip(test_queries, expected_outcomes): # Calculate complexity complexity = calculate_complexity(query) # Apply threshold and resize window resized_quer…

2. **Define the Reformulation Logic**: Encode the input query and generate the reformulated query. 3. **Batch Processing and Threading**: Handle multiple queries efficiently using batch processing and threading. 4. **Caching with Redis**: S…

### Step 4: Refine Reformulation Logic Refine the reformulation logic to handle edge cases and improve overall accuracy. Here's an example of how you might structure the reformulation logic: ```python from transformers import AutoModelFor…