Evaluation Metrics: Answer Similarity

### This n8n template demonstrates how to calculate the evaluation metric Similarity which in this scenario, measures the consistency of the agent. 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_similarity.py](https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/_answer_similarity.py) ### How it works - This evaluation works best where questions are close-ended or about facts where the answer can have little to no deviation. - For our scoring, we generate embeddings for both the AI's response and ground truth and calculate the cosine similarity between them. - A high score indicates LLM consistency with expected results whereas a low score could signal model hallucination. ### 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)