Performance Expectation

By applying these optimizations, your RAG system should be able to handle 8,000 queries hourly more efficiently. [Turn 1182] User: I'm working on refining my choices for the RAG system, aiming to refine 20% of them based on feedback from 5…

- We define several example combinations of instance types and their counts. - We calculate the total cost for each combination and print the results. ### Output Running the script will give you the following output: ```plaintext C…

- A NumPy array `vectors` is created with the specified initial capacity and vector size. 2. **Adding Vectors**: - The `add_vector` method checks if the current number of vectors has reached the capacity. If so, it resizes the array …

file_handler.setFormatter(formatter) console_handler.setFormatter(formatter) # Add handlers to logger logger.addHandler(file_handler) logger.addHandler(console_handler) # Log some transactions for i in range(1000000): logger.info(f'Tr…

response = requests.post(REMOTE_LOGGING_URL, json={'message': message}) response.raise_for_status() except requests.exceptions.RequestException as e: logger.error(f'Failed to send remote log: {e}') # Log some tr…

if __name__ == '__main__': unittest.main() ``` ### Explanation 1. **Test Valid Input:** - `test_valid_input`: Tests with valid input where the dimensions of `sparse_scores` and `dense_scores` match. - Verifies that the function …

k = 1 # Number of nearest neighbors to retrieve distances, indices = index.search(query_vector.reshape(1, -1), k) print("Distances:", distances) print("Indices:", indices) ``` ### Explanation 1. **Dimensionality**: - Ensure the dimen…