Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality).
Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.This is the skill's description: the text your AI reads to decide when the skill applies.
nvidia-rag-eval/SKILL.md
---
name: rag-eval
version: "2.6.0"
description: >-
Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod
monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.
license: Apache-2.0
compatibility: Repository checkout with uv; Python 3.11+; run from repo root; uv sync --project scripts/eval (eval deps live in scripts/eval/pyproject.toml); network to RAG, ingestor, and vdb endpoints; NVIDIA_API_KEY for RAGAS; optional RAG_EVAL_JUDGE_MODEL (default mistralai/mixtral-8x22b-instruct-v0.1).
metadata:
author: NVIDIA RAG <foundational-rag-dev@exchange.nvidia.com>
github-url: "https://github.com/NVIDIA-AI-Blueprints/rag"
endpoint-openapi-schemas:
- docs/api_reference/openapi_schema_rag_server.json
- docs/api_reference/openapi_schema_ingestor_server.json
argument-hint: RAGAS eval | evaluate_rag | train.json | corpus | results json | error triage | uv run --project scripts/eval | enable_reranker | query_rewriting | temperature | skip_ingestion
tags:
- nvidia
- blueprint
- rag
- evaluation
- ragas
- benchmarking
- nvidia-rag-blueprint
languages:
- python