Need higher level RAG metrics

Nobody has claimed this yet.

### Problem & Motivation

There is a huge wave of interest around high accuracy Q&A, such as via Retrieval Augmented Generation (RAG). RAG accuracy is largely driven by how well vector search is able to retrieve the correct context to answer questions via an LLM. When evaluating embedding models, vector search retrieval metrics are helpful but insufficient because they don't reveal how well the retrieved content actually answers the target questions.

### Pitch

I'd love to see an integration with a tool like our new ragulate library (Apache 2 licensed) that would simplify model evaluation on RAG Q&A: https://github.com/epinzur/ragulate/tree/main

### Additional context

I was going to suggest that you integrate with trulens, but then I discovered that we built ragulate to automate much of the process of using trulens, and we'd love feedback on it.

Start by reading the linked ragulate library and its relationship to TruLens to understand the proposed RAG Q&A evaluation integration. Define the supported higher-level retrieval and answer-quality metrics, then verify that the resulting integration evaluates RAG questions beyond vector-search retrieval metrics.

Written by the indexing model from the issue text.