Evaluations Metric Answer Similarity

## Try It Out!
### 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 [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)


### Need Help?
Join the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!

Happy Hacking!