Evaluate AI Agent Response Relevance using OpenAI and Cosine Similarity

This n8n template demonstrates how to calculate the evaluation metric "Relevance" which in this scenario, measures the relevance of the agent's response to the user's question. 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/answerrelevance.py How it works This evaluation works best for Q&A agents. For our scoring, we analyse the agent's response and ask another AI to generate a question from it. This generated question is then compared to the original question using cosine similarity. A high score indicates relevance and the agent's successful ability

Help me set up the n8n workflow "Evaluate AI Agent Response Relevance using OpenAI and Cosine Similarity" (https://n8n.io/workflows/4425). 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.