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RAG Perf

NVIDIAOfficialCreated by NVIDIA RAG

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • Medium risk

From the author

Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config. Not for accuracy / RAGAS scoring (use rag-eval) or for deploying / repairing services (use rag-blueprint).
— the description by NVIDIA RAG

AutoGineer summary

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

rag-perf is a command-line tool designed to benchmark the performance of deployed NVIDIA RAG Blueprint servers. It executes a two-phase process: a server-side profiling pass to analyze stage-specific timing and bottlenecks, followed by an optional aiperf-based load test to measure throughput, latency, and error rates. The tool is entirely driven by YAML configuration files, allowing users to define concurrency levels, retrieval parameters, and synthetic query generation. It provides comprehensive reporting, including per-point analysis and aggregate sweep comparisons, making it suitable for regression testing and performance tuning of RAG pipelines.

What it does

  • Performs server-side profiling to identify bottlenecks and stage-specific latency.
  • Executes aiperf load tests to measure TTFT, throughput, and error rates.
  • Supports multi-axis sweeps for concurrency, vdb_top_k, and reranker_top_k parameters.
  • Generates unified reports including summary tables and side-by-side comparisons.
  • Validates configurations and provides reproducible output via resolved YAML dumps.
  • Integrates with synthetic query generation for automated load testing.

When to use it

  • When you need to profile the latency and performance of a deployed RAG server.
  • When conducting regression checks on RAG pipeline configurations.
  • When performing concurrency sweeps to determine optimal throughput.
  • When tuning retrieval and reranker parameters for performance optimization.

Advantages

  • Config-driven approach ensures reproducible benchmarking runs.
  • Provides granular stage-by-stage timing breakdowns.
  • Automates complex multi-axis performance sweeps.
  • Includes built-in support for synthetic query generation.
  • Produces clear, actionable reports with bottleneck identification.

Usage examples

“Run a quick performance profile of my RAG server.”
The agent executes the quick_profile.yaml preset, generating a profile report without running a full load test.
“Benchmark my RAG server with a concurrency sweep.”
The agent runs the sweep.yaml configuration, producing a nested directory structure with aggregate results comparing different concurrency and top-k settings.

Install

  1. Download the skill as a ZIP:

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

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

Documentation by NVIDIA RAG

RAG-Perf — config-driven perf benchmark CLI

Purpose

Drive a deployed NVIDIA RAG Blueprint server with a YAML config, run a server-side profiling pass (per-stage timing, citation quality, bottleneck inference) and an optional aiperf load test (TTFT / E2E / token & request throughput / error rate), and write a unified report. The CLI is intentionally minimal: rag-perf -c <config> plus --help / --version. Behaviour is fully config-driven; field variations belong in YAML.

Scope

  • Accuracy / RAGAS scoring of answer quality → use the rag-eval skill.

  • Deploying, repairing, or configuring services (compose, helm, NIM env vars) → use the rag-blueprint skill.

  • Production monitoring / alerting — rag-perf is a one-shot benchmark tool.

  • Runtime requirement: a deployed RAG server reachable on the network.

Prerequisites

  • Repo cloned; run commands from the repo root (config paths in the presets are repo-root-relative).

  • Python 3.11+ and uv on PATH.

  • Install rag-perf into its own uv-managed venv: uv sync --project scripts/rag-perf.

  • For unit tests: install dev extras as well — uv sync --project scripts/rag-perf --extra dev (otherwise pytest-asyncio is missing and async tests error out at collection time).

  • A reachable RAG server (default http://localhost:8081). For the aiperf phase, the bundled nvidia_rag endpoint plugin must be installed — pip install -e ./scripts/rag-perf registers it via the aiperf.plugins entry point.

  • For synthetic queries: an OpenAI-compatible chat-completions endpoint reachable at synthetic.llm_url (default http://localhost:8999/v1/chat/completions).

  • rag-perf itself runs without NVIDIA_API_KEY (unlike rag-eval). The synthetic LLM endpoint may require its own auth — that's the deployment's concern.

Instructions

  1. Pick a preset. The three under scripts/rag-perf/configs/ are:

    • quick_profile.yaml — profile-only, ~30 s. Skips load test. For fast iteration on retrieval / reranker tuning.

    • single_run.yaml — one concurrency level, profiling + aiperf, ~2 min. Regression checks.

    • sweep.yaml — multi-axis sweep. load.concurrency, rag.vdb_top_k, rag.reranker_top_k are all int | list[int]; any of them as a list becomes a sweep axis (Cartesian product).

  2. Edit the preset. Required: replace rag.collection_names: ["<collection_name>"] with a real collection on the deployed ingestor server. Verify the collection exists via GET /v1/collections on the ingestor. The placeholder <collection_name> validates fine but every request will fail at retrieval. Use a copied YAML preset for variants; the CLI surface is intentionally config-only.

  3. Run. From repo root:

    uv run --project scripts/rag-perf rag-perf -c scripts/rag-perf/configs/single_run.yaml

    Same form for the other presets. The CLI accepts only -c / --config (required), --help, --version.

  4. Read stdout. Every invocation prints, in order: a startup banner, a one-line summary, the fully resolved config as YAML (so the run is reproducible from terminal output), per-grid-point progress with the shlex-joined aiperf command in copy-pastable form, a rich per-point summary table (stage breakdown with bars, citation quality, bottleneck, load-test block), and finally a side-by-side comparison table auto-labelled by whichever axis varied. See references/output-and-analysis.md.

  5. Inspect artifacts. Layout depends on run shape — flat for single-point + iterations=1, nested under iter_<i>/<point>/... otherwise. See references/output-and-analysis.md for the full directory tree, file purposes, and how to parse results.json / results.csv / report.md.

  6. Summarise for the user. When reporting back, follow the playbook in references/output-and-analysis.md#summarising-results-to-the-user: pick the canonical result file for the run shape, build a headline table (concurrency × top-k axes × TTFT × throughput × bottleneck × citation quality), compute scaling efficiency on sweeps, always flag zero citations / non-zero error rate / suspect llm_ttft_ms / small-sample p99, and propose a concrete next-experiment YAML.

  7. Tune. Schema is fully documented in docs/performance-benchmarking.md and the deeper-dive references below. Common knobs: turn aiperf.enabled: false for profile-only mode, increase load.iterations for variance estimation, set load.sleep_between_points_s: 60 for overnight Cartesian sweeps.

Examples

Profile-only (quickest signal on retrieval / reranker tuning):

uv run --project scripts/rag-perf rag-perf -c scripts/rag-perf/configs/quick_profile.yaml

Output: rag-perf-results/quick_profile/run_<ts>/{profile_report.md, profile_results.json, profiling/}. The aiperf_rag_on/ directory is omitted. Filenames are profile_* because aiperf.enabled: false.

Single benchmark point with full report:

uv run --project scripts/rag-perf rag-perf -c scripts/rag-perf/configs/single_run.yaml

Output: flat run_<ts>/{report.md, results.json, results.csv, profiling/, aiperf_rag_on/}.

Concurrency sweep:

uv run --project scripts/rag-perf rag-perf -c scripts/rag-perf/configs/sweep.yaml

Output: nested run_<ts>/iter_1/<CR:_VDB-K:_RERANKER-K:_…>/{profiling,aiperf_rag_on}/ per point, plus aggregate report.md / results.json / results.csv at the run root.

Run unit tests:

uv sync --project scripts/rag-perf --extra dev   # one-time, installs pytest-asyncio
uv run --project scripts/rag-perf python -m pytest tests/unit/test_rag_perf/

Limitations

  • The CLI is config-only: author or copy YAML to vary a parameter.

  • load.concurrency / rag.vdb_top_k / rag.reranker_top_k accept int | list[int]; the validator requires unique list values because each value names a unique point dir.

  • input.file and input.synthetic follow an XOR rule — both set fails validation. When neither is set, synthetic auto-fills with defaults so a bare config still validates.

  • File-based input format is inferred from extension only (.jsonl or .csv); other extensions are rejected.

  • Synthetic generation streams each query to disk as it completes (failure-resilient) but fails fast on the first LLM error — partial JSONL is preserved. Re-run after fixing the endpoint.

  • Reasoning models (Nemotron Omni, Qwen-Reasoning) require synthetic.disable_thinking: true (the default). Without it the model exhausts the token budget on chain-of-thought and content returns empty — the generator now raises with a clear message instead of substituting reasoning_content for the answer.

  • aiperf-specific knobs outside the YAML surface (request rate distribution, GPU telemetry config, etc.) require editing AiperfRunner._base_aiperf_cmd in scripts/rag-perf/rag_perf/runner.py.

  • Procedural detail lives under references/ to keep this file concise.

Troubleshooting

Error / signalLikely causeWhat to do
Configuration errors in <yaml>: • input — ... XOR ruleBoth input.file and input.synthetic setPick one. The XOR validator runs at YAML load time.
input.file must end in .jsonl or .csvExtension other than .jsonl / .csvRename or convert.
load.concurrency has duplicate valuese.g. [2, 2, 4]Each concurrency maps to a unique point dir; dedupe.
warmup_requests must be >= 1YAML had warmup_requests: 0aiperf rejects warmup=0; minimum is 1.
LLM returned empty content (reasoning_content was populated — model exhausted its budget on chain-of-thought; raise min_query_tokens or set synthetic.disable_thinking=true).Reasoning model used CoT and ran out of tokensSet synthetic.disable_thinking: true (the default) or raise min_query_tokens.
✗ All N profiling requests failed across M point(s). + exit 1Bad URL, server down, wrong collectionVerify target.url, rag.collection_names (the <collection_name> placeholder will hit this).
Per-iteration ⚠ N profiling requests failed warning, run continuesSome requests timed out / errored mid-runCheck rag-server logs, raise target.timeout_s, drop concurrency.
RuntimeError: Random synthetic query generation failed at query N: ...LLM endpoint rejected a request mid-generationPartial JSONL is at synthetic.jsonl_output_path; fix endpoint and re-run with reduced num_queries, or point input.file at the partial file.
Citation count (mean): 0 and Citation relevance score: N/A for a non-empty deploymentCollection mismatch between rag.collection_names and what's actually ingestedRun curl -s http://<ingestor>:8082/v1/collections to list real collections.
Tests error with ModuleNotFoundError: No module named 'pytest_asyncio'Dev extras missinguv sync --project scripts/rag-perf --extra dev.
CI: ModuleNotFoundError: No module named 'ruamel' from tests/unit/test_rag_perf/rag-perf package missing from CI venvAdd uv pip install -e ./scripts/rag-perf after the top-level install in the unit-tests job.

Gotchas

  • Run from repo root. Preset configs reference scripts/rag-perf/examples/queries.jsonl and scripts/rag-perf/prompts/default_prompts.yaml with repo-root-relative paths. Running from inside scripts/rag-perf/ will fail those file lookups.

  • CLI is config-only. Edit the YAML or copy a preset for URL, concurrency, collection, and similar fields.

  • Always edit rag.collection_names before the first run. The presets ship with ["<collection_name>"] as a deliberate placeholder. Validation passes, retrieval fails silently for every request — manifests as Citation count (mean): 0 everywhere.

  • load.concurrency_list, rag.vdb_top_k_list, rag.reranker_top_k_list are read-only properties that normalise scalar-or-list to a list. Use them when reasoning about the grid; the underlying YAML field is whatever the user wrote.

  • aiperf.enabled: false changes filenames. The top-level outputs become profile_report.md / profile_results.json / profile_results.csv. The aggregate sweep table also suppresses load-test rows and the "Optimal throughput" footer.

  • Resolved-config dump is verbose (50+ lines) — expected. It's what makes terminal output a self-contained reproducer; don't filter it out in scripts.

  • The aiperf shell command is logged before each subprocess. Look for \n $ python -m aiperf profile -m ... --endpoint-type nvidia_rag ... in stdout — copy-paste runnable for reproducing a single point outside rag-perf.

  • --endpoint-type nvidia_rag comes from the bundled plugin at scripts/rag-perf/rag_perf/plugin/nvidia_rag.py. It teaches aiperf about the RAG /v1/generate request shape and parses citations + per-stage metrics out of the SSE stream. If aiperf can't resolve nvidia_rag, rag-perf needs editable installation in the venv — re-run uv sync --project scripts/rag-perf (or uv pip install -e ./scripts/rag-perf).

  • Sweep-mode point-name collision. When two points differ only in concurrency (e.g. [1, 4] × single vdb_top_k), the dir name encodes everything: CR:1_ISL:50_OSL:512_VDB-K:20_RERANKER-K:4_Model:.... Cluster / GPU / experiment_name (output.cluster, output.gpu, output.experiment_name) are appended too — useful for diff-friendly artifact paths across machines.

  • load.iterations > 1 repeats the entire grid. Each repetition writes to its own iter_<i>/. Aggregate CSV row count = n_points × iterations.

Source of truth

PieceLocation
Driverscripts/rag-perf/rag_perf/cli.py (main is the single Click command)
Schemascripts/rag-perf/rag_perf/config.py (RunConfig and sub-models)
Orchestratorscripts/rag-perf/rag_perf/runner.py (BenchmarkRunner.run, RagProfiler, AiperfRunner)
aiperf pluginscripts/rag-perf/rag_perf/plugin/nvidia_rag.py
User-facing docdocs/performance-benchmarking.md
Presetsscripts/rag-perf/configs/{quick_profile,single_run,sweep}.yaml
Sample queriesscripts/rag-perf/examples/queries.jsonl
Synthetic promptsscripts/rag-perf/prompts/default_prompts.yaml
Config schema detailsreferences/config-schema.md
Synthetic-query generationreferences/synthetic-generation.md
Output layout & metric semanticsreferences/output-and-analysis.md

Agent playbook

  1. Sync deps: uv sync --project scripts/rag-perf (one-time per checkout).

  2. Pick & customise a preset: copy scripts/rag-perf/configs/<preset>.yaml if you want a variant; always set rag.collection_names to a real collection.

  3. Run: uv run --project scripts/rag-perf rag-perf -c <config> from repo root.

  4. Read the per-point + aggregate tables on stdout. Bottleneck inference is in the per-point profiling section; comparison across points is the final aggregate table.

  5. Parse artifacts under output.dir/run_<ts>/ — see references/output-and-analysis.md. For multi-point runs, results.csv has one row per (point × iteration).

  6. Summarise for the user using the playbook in references/output-and-analysis.md#summarising-results-to-the-user — headline table, scaling-efficiency math for sweeps, mandatory flags for zero citations / non-zero errors / suspect llm_ttft_ms / low sample size, and a concrete next-experiment YAML.

  7. Tune retrieval / reranker: flip to quick_profile.yaml or aiperf.enabled: false for fast iteration, then return to single_run.yaml / sweep.yaml when characterising under load.

  8. Triage failures: see Troubleshooting above and references/output-and-analysis.md for empty-citation / bottleneck=N/A patterns.

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 network access, Needs an API key, Needs Python

The author says: Repository checkout with uv; Python 3.11+; run from repo root; uv sync --project scripts/rag-perf (perf deps live in scripts/rag-perf/pyproject.toml); reachable RAG server (default http://localhost:8081); for synthetic queries an OpenAI-compatible chat-completions endpoint is required (default http://localhost:8999/v1/chat/completions); aiperf load-test phase uses the bundled nvidia_rag endpoint plugin, registered automatically when rag-perf is installed editable.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA RAG
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/rag-perf · 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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