Evaluation metric example: RAG document relevance

AI evaluation in n8n This is a template for n8n's evaluation feature. Evaluation is a technique for getting confidence that your AI workflow performs reliably, by running a test dataset containing different inputs through the workflow. By calculating a metric (score) for each input, you can see where the workflow is performing well and where it isn't. How it works This template shows how to calculate a workflow evaluation metric: retrieved document relevance (i.e. whether the information retrieved from a vector store is relevant to the question). The workflow takes a question and checks whether the information retrieved to answer it is relevant. To run this workflow, you need to insert docum

Help me set up the n8n workflow "Evaluation metric example: RAG document relevance" (https://n8n.io/workflows/4273). It uses: Google Sheets, AI Agent, Embeddings OpenAI, OpenAI Chat Model, Recursive Character Text Splitter, Simple Vector Store, Default Data Loader, OpenAI, Evaluation. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.