What is rag AGENTS.md?

Repository-specific instructions for working on NVIDIA RAG Blueprint, a reference project that lets applications search documents and generate answers from them. They cover its Python backend, React interface, deployment files, tests, and optional evaluation tools.

Use it when modifying the NVIDIA RAG Blueprint, running its unit or integration tests, checking code style, or working on its Docker, Helm, frontend, backend, or evaluation files.

They give a coding agent the project layout and commands it needs to make changes consistently. This reduces guesswork about testing, linting, deployment, and document-search components.

How NVIDIA-AI-Blueprints/rag configures Codex and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to NVIDIA-AI-Blueprints/rag. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/NVIDIA-AI-Blueprints/rag/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/NVIDIA-AI-Blueprints/rag

Made for: Codex, OpenCode.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/nvidia-ai-blueprints/rag/agents-md/github.svg)](https://agentmods.dev/instructions/nvidia-ai-blueprints/rag/agents-md)

Your own site

<a href="https://agentmods.dev/instructions/nvidia-ai-blueprints/rag/agents-md"><img src="https://agentmods.dev/badge/instructions/nvidia-ai-blueprints/rag/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/nvidia-ai-blueprints/rag/agents-md"><img src="https://agentmods.dev/badge/instructions/nvidia-ai-blueprints/rag/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 951 This file is loaded in full into every session.

When invoked 951 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00951	$0.00951
Opus 5.5	$0.00380	$0.00380
Sonnet 5.5	$0.00190	$0.00190
Haiku 4.5	$0.00095	$0.00095

Measured 2mo ago against content hash d0eed583831b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

rag AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 89 lines

How it starts

The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.

NVIDIA RAG Blueprint

Reference implementation for a Retrieval Augmented Generation pipeline. Python 3.11+ backend (FastAPI + LangChain), React/TypeScript frontend, deployable via Docker Compose or Helm.

Project structure

src/nvidia_rag/
├── rag_server/        # RAG query/response server (FastAPI)
├── ingestor_server/   # Document ingestion server (FastAPI)
└── utils/             # Shared utilities
frontend/              # React + TypeScript UI (pnpm)
deploy/
├── compose/           # Docker Compose files and env configs
└── helm/              # Helm charts (standard + MIG-slicing)
docs/                  # User-facing documentation (Sphinx, RST/MD)
tests/
├── unit/              # No network calls allowed
└── integration/       # Network calls permitted
notebooks/             # Jupyter notebooks for evaluation and examples

Development commands

Backend (Python)

uv sync                              # Install all deps
# Optional: RAGAS benchmark CLI (see scripts/eval/README.md)
# uv sync --project scripts/eval
uv run pytest tests/unit/            # Unit tests
uv run pytest tests/integration/     # Integration tests
ruff check --fix src/                # Lint + autofix
ruff format src/                     # Format
pre-commit run --all-files           # Run all pre-commit hooks

Frontend (TypeScript)

cd frontend
pnpm install
pnpm run dev                         # Dev server
pnpm run lint                        # ESLint
pnpm exec tsc --noEmit               # Type check
pnpm run test:run                    # Tests

Code conventions

Python: Ruff for linting and formatting (line-length 88, double quotes, space indent). Config in pyproject.toml.
Type hints: Required on all function signatures.
Imports: Sorted by isort via Ruff. No in-function imports.
Tests: Mirror source tree (src/nvidia_rag/rag_server/server.py → tests/unit/rag_server/test_server.py).
Frontend: ESLint + TypeScript strict mode. Function components with hooks.
Env files: deploy/compose/nvdev.env (NVIDIA-hosted NIMs) and deploy/compose/.env (self-hosted). These are the source of truth for Docker deployments — shell-only exports are lost on restart.

Read the full file on GitHub · 89 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 89 lines · 951 tokens per session scan A d0eed583831b

Subscribe to this mod's changes

rag AGENTS.md is an instructions file published in the GitHub repository NVIDIA-AI-Blueprints/rag (819 stars, last pushed 3d ago), licensed Apache-2.0. It adds 951 tokens to every session, about $0.0038 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Backend and APIs Data and AI RAG and vectors Writing and docs blueprint nim retrieval-augmented-generation

rag AGENTS.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
rag AGENTS.mdNVIDIA-AI-Blueprints/rag	Instructions file	819	3d ago	Apache-2.0	A	951	Codex, OpenCode
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	9,719	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	today	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	yesterday	MIT	A	4,680	Codex, OpenCode
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	5,516	GitHub Copilot
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	yesterday	MIT	A	3,093	GitHub Copilot
deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness	Instructions file	274k	2d ago	MIT	A	4,313	Codex, OpenCode

rag: Instructions file for Codex