Evaluate AI Agent Response Correctness with OpenAI and RAGAS Methodology

### This n8n template demonstrates how to calculate the evaluation metric Correctness which in this scenario, measures, compares, and classifies the agent's response against a set of ground truths. The scoring approach is adapted from the open-source evaluations project [RAGAS](https://docs.ragas.io/) and you can see the source here [https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_correctness.py](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 in 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 are 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](https://docs.google.com/spreadsheets/d/1YOnu2JJjlxd787AuYcg-wKbkjyjyZFgASYVV0jsij5Y/edit?usp=sharing)