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By incorporating these additional metrics, you can gain a more thorough understanding of your sparse retrieval engine's performance and reliability. [Turn 2400] User: hmm, how do we implement these metrics in our existing codebase? [Turn 2401] Assistant: To implement the additional metrics in your existing codebase, you need to integrate the necessary logic for calculating precision, recall, F1 score, and latency. Here is a step-by-step guide to help you incorporate these metrics into your test function: ### Step 1: Import Necessary Libraries Ensure you have the necessary libraries imported, including `numpy` for numerical operations and `sklearn.metrics` for precision, recall, and F1 score calculations. ### Step 2: Modify the Test Function Update your test function to include the new metrics and ensure you have a way to generate ground truth data for comparison. ### Step 3: Generate Ground Truth Data Create a function to generate ground truth data that simulates the relevance of the retrieved documents. ### Step 4: Calculate Metrics Integrate the logic to calculate precision, recall, F1 score, and latency within the test function. ### Example Code