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 the 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 and you can see the source here https://github.com/explodinggradients/ragas/blob/main/ragas/src/ragas/metrics/answercorrectness.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 Negativ

Help me set up the n8n workflow "Evaluate AI Agent Response Correctness with OpenAI and RAGAS Methodology" (https://n8n.io/workflows/4424). It uses: HTTP Request, Code, AI Agent, Basic LLM Chain, OpenAI Chat Model, Structured Output Parser, Evaluation. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.