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RAG Eval

NVIDIAOfficialCreated by NVIDIA RAG

  • Skill
  • Review and audit
  • Software development
  • Apache-2.0
  • Medium risk

From the author

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.
— the description by NVIDIA RAG

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to evaluating RAG (Retrieval-Augmented Generation) pipeline quality using the NVIDIA RAG Blueprint framework. It utilizes a specific filesystem layout consisting of a corpus directory and a train.json file to run automated evaluations via the evaluate_rag.py script. The process leverages RAGAS metrics to assess accuracy, context relevance, and groundedness, allowing developers to tune retrieval and generation parameters for optimal performance.

What it does

  • Automates RAG quality evaluation using RAGAS metrics.
  • Supports tuning of retrieval parameters like top_k and vdb_top_k.
  • Enables configuration of generation settings such as temperature and max-tokens.
  • Provides structured JSON output for headline metrics and per-query forensics.
  • Includes troubleshooting guidance for common ingestion and evaluation errors.
  • Facilitates comparative analysis of different pipeline configurations.

When to use it

  • When you need to measure the quality of your RAG pipeline's responses.
  • When comparing different retrieval or generation settings for your RAG system.
  • When you have prepared a dataset following the required corpus and train.json structure.
  • When you need to perform forensic analysis on specific query failures.

Advantages

  • Standardized evaluation workflow within the NVIDIA RAG Blueprint ecosystem.
  • Clear separation between quality evaluation and performance benchmarking.
  • Detailed per-query data for deep-dive analysis of retrieval and generation.
  • Flexible configuration for testing various RAG pipeline parameters.

Usage examples

“Run a full RAG evaluation on my dataset located at /data/my_rag_test.”
The agent will execute the evaluation script using the provided dataset path, host, and port, generating RAGAS metrics and summary reports in the results directory.
“How can I compare the impact of different top_k settings on my RAG quality?”
The agent will guide you to adjust the --top_k and --vdb_top_k flags in the evaluation command to perform comparative runs and analyze the resulting RAGAS scores.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/rag-eval/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip rag-eval.zip -d .agents/skills

Documentation by NVIDIA RAG

On-disk RAG evaluation (corpus/ + train.json)

Purpose

Guide agents through NVIDIA RAG Blueprint filesystem benchmarks: preparing corpus/ and train.json, running scripts/eval/evaluate_rag.py, tuning retrieval and generation flags for quality comparisons, interpreting RAGAS JSON outputs, and triaging failures (HTTP/stream errors, empty contexts, collection mismatch, judge API).

For latency, throughput, and load testing, use the rag-perf skill (scripts/rag-perf, docs/performance-benchmarking.md) — not this skill.

When not to use

Do not use this skill for: deploying or repairing services (use rag-blueprint); evaluating APIs without the corpus/ + train.json layout; general ML experimentation unrelated to this evaluator; production monitoring/alerting; or latency/throughput benchmarking (use rag-perf).

Prerequisites

  • Repo cloned; run commands from repo root (imports and paths assume this).

  • Python 3.11+ and uv; eval deps: uv sync --project scripts/eval.

  • Reachable RAG server and ingestor (defaults often localhost:8081 / 8082).

  • NVIDIA_API_KEY for RAGAS (see credential hygiene); optional RAG_EVAL_JUDGE_MODEL.

  • Dataset roots passed to --dataset-paths each contain corpus/ and train.json.

Instructions

  1. Prepare data — Ensure each dataset directory matches the layout and train.json rules in references/dataset-and-conversion.md. When sources arrive as public links (sites or dataset pages), materialize documents under corpus/—prefer PDF for multimodal content so images stay embedded; convert CSV/JSONL/etc. using the patterns there.

  2. Run eval — uv run --project scripts/eval python scripts/eval/evaluate_rag.py with --dataset-paths, --host, and --port. See references/benchmark-execution.md for command examples, outputs, and errors. Use references/evaluate-rag-cli.md for flag-level detail.

  3. Tune quality — Adjust --top_k / --vdb_top_k, reranker and query-rewriting toggles, and generation overrides (--temperature, --top-p, --max-tokens) as documented in references/benchmark-execution.md when comparing retrieval/generation configs for RAGAS scores.

  4. Analyze results — Use references/result-analysis.md for scripts; scan rag_*_evaluation_summary.json for headline RAGAS metrics.

  5. Triage errors — Use the error signal table and the Troubleshooting section below.

Examples

Set API key without putting secrets in shell history (preferred patterns): load from a gitignored env file or secrets manager; avoid committing .env; rotate keys if exposed. Details: references/benchmark-execution.md#credential-hygiene-nvidia_api_key.

Minimal eval (key already in environment):

uv sync --project scripts/eval
uv run --project scripts/eval python scripts/eval/evaluate_rag.py \
  --dataset-paths /path/to/my_dataset \
  --host localhost \
  --port 8081

Pretty-print summary JSON:

python3 -m json.tool results/my_dataset/rag_my_dataset_evaluation_summary.json

More examples (skip ingestion, quality sweeps): references/benchmark-execution.md.

Limitations

  • Evaluator behavior is fixed to the filesystem contract and evaluate_rag.py; it does not substitute for custom offline judges or non-RAG benchmarks.

  • Vector DB / embedding choices follow deployed ingestor and RAG env — not overridden by this CLI alone.

  • Scores depend on retrieval quality, judge model availability, and NVIDIA_API_KEY; empty contexts yield partial RAGAS metrics (see references).

  • Large procedural detail lives under references/ to keep routing concise; read those files when the user needs step-by-step conversion, full flags, or error tables.

Troubleshooting

Error / signalLikely causeWhat to do
Immediate exit mentioning NVIDIA_API_KEYMissing or invalid keySet key via secure channel; see credential hygiene in references/benchmark-execution.md.
train.json must be a JSON arrayWrong JSON shapeTop-level array of objects; validate per references/dataset-and-conversion.md.
Fewer rows in evaluation_data.json than train.jsonPer-query failuresCheck stderr: network or stream JSON errors; see error table in benchmark-execution.
Empty generated_contexts everywhereRetrieval gapVerify collection, ingestion, top_k / vdb_top_k, and ingestor_server_url without /v1 suffix.
Ingestor 404 on uploadBad ingestor base URLPass http://host:port only — code appends /v1/.

Full signal table: references/benchmark-execution.md#common-error-cases-and-signals.

Gotchas

  • Run from repo root: paths and imports in scripts/eval/evaluate_rag.py assume this; a wrong directory silently breaks imports.

  • --ingestor_server_url: pass http://host:port without /v1—the code appends /v1/ automatically. Including /v1 causes 404s on ingestor calls.

  • Vector DB / embedding settings: not set by this CLI; configure via the deployed ingestor and RAG server env vars (e.g. APP_VECTORSTORE_URL, embedding model).

  • --model / --llm_endpoint: forwarded verbatim only when explicitly set; omit to keep the server's configured LLM.

  • Stale collections: a previous run's ingested data persists unless you use --force_ingestion. Use --collection with a unique name when comparing quality across isolated runs.

  • Empty context metrics: if all generated_contexts are empty, RAGAS scores only nv_accuracy and leaves the other two metrics blank—this is not a silent success.

Source of truth

PieceLocation
Driverscripts/eval/evaluate_rag.py (CORPUS_DIRECTORY = corpus, EVAL_DATA = train.json)
Human README (always in-repo)scripts/eval/README.md
Full CLI (flags, defaults)scripts/eval/evaluate_rag.py --help; references/evaluate-rag-cli.md
Dataset / conversionreferences/dataset-and-conversion.md
Runs, outputs, errorsreferences/benchmark-execution.md
Result analysis scriptsreferences/result-analysis.md
Latency / throughputrag-perf skill, docs/performance-benchmarking.md

Agent playbook

  1. Run eval — uv sync --project scripts/eval then uv run --project scripts/eval python scripts/eval/evaluate_rag.py with required --dataset-paths, --host, and --port (and env NVIDIA_API_KEY). Argument --ingestor_server_url is optional (defaults to http://localhost:8082); pass it only when overriding the ingestor endpoint.

  2. Quality tuning — See references/benchmark-execution.md: --top_k/--vdb_top_k, reranker and query-rewriting toggles, --temperature, --top-p, --max-tokens.

  3. Data conversion — Follow references/dataset-and-conversion.md.

  4. Analyze results — references/result-analysis.md; quick scan: python3 -m json.tool results/<dataset>/rag_<dataset>_evaluation_summary.json.

  5. Error triage — references/benchmark-execution.md#common-error-cases-and-signals.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs an API key, Needs Python

The author says: 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).

Security

What our scanner noticed· Medium risk

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Attribution and licence

Created by
NVIDIA RAG
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/rag-eval · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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