Evaluate RAG Response Accuracy with OpenAI: Document Groundedness Metric

This n8n template demonstrates how to calculate the evaluation metric "RAG document groundedness" which in this scenario, measures the ability to provide or reference information included only in retrieved vector store documents. The scoring approach is adapted from https://cloud.google.com/vertex-ai/generative-ai/docs/models/metrics-templatespointwisegroundedness How it works This evaluation works best for an agent that requires document retrieval from a vector store or similar source. For our scoring, we need to collect the agent's response and the documents retrieved and use an LLM to assess if the former is based off the latter. A key factor is to look out information in the response whi

Help me set up the n8n workflow "Evaluate RAG Response Accuracy with OpenAI: Document Groundedness Metric" (https://n8n.io/workflows/4426). It uses: HTTP Request, AI Agent, Basic LLM Chain, Embeddings OpenAI, OpenAI Chat Model, Structured Output Parser, Recursive Character Text Splitter, Simple Vector Store, Default Data Loader, Evaluation. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.