Evaluate AI Agent Correctness with OpenAI and RAGAS

## Try It Out! ### This n8n template demonstrates how to calculate the evaluation metric "Correctness" which in this scenario, measures the compares and classifies the agent's response against a set of ground truths. The scoring approach is adapted from https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_correctness.py ### How it works * This evaluation works best where the agent's response is allowed to be more verbose and conversational. * For our scoring, we classify the agent's response into 3 buckets: True Positive (in answer and ground truth), False Positive (in answer but not ground truth) and False Negative (not in answer but in ground truth). * We also calculate an average similarity score on the agent's response against all ground truths. * The classification and the similarity score is then averaged to give the final score. * A high score indicates the agent is accurate whereas a low score could indicate the agent has incorrect training data or is not providing a comprehensive enough answer. ### Requirements * n8n version 1.94+ * Check out this Google Sheet for a sample data https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing ### Need Help? Join the Discord or ask in the Forum! Happy Hacking!