Borrowing it
Nothing to install: this file belongs to Azure-Samples/azure-openai-rag-workshop. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Azure-Samples/azure-openai-rag-workshop/main/AGENTS.mdgit clone --depth 1 https://github.com/Azure-Samples/azure-openai-rag-workshopWrote 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.
[](https://agentmods.dev/instructions/azure-samples/azure-openai-rag-workshop/agents-md)<a href="https://agentmods.dev/instructions/azure-samples/azure-openai-rag-workshop/agents-md"><img src="https://agentmods.dev/badge/instructions/azure-samples/azure-openai-rag-workshop/agents-md.svg" alt="Measured on agentmods" height="20"></a>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 | $0.02103 | $0.02103 |
| Opus 5 | $0.01052 | $0.01052 |
| Sonnet 5 | $0.00421 | $0.00421 |
| Haiku 4.5 | $0.00210 | $0.00210 |
Grade A, and why
azure-openai-rag-workshop AGENTS.md scanned grade A with 1 finding 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 6d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Ingestion not populating data: Confirm `INGESTION_API_URI` and that PDF curl uploads return 2xx. Re-run ingestion script after redeploy. How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure OpenAI RAG Workshop (Node.js)
A monorepo sample + workshop showing how to build a Retrieval‑Augmented Generation (RAG) chat experience using LangChain.js with Azure OpenAI (optionally Qdrant) and expose it through a Fastify backend, an ingestion Fastify service, and a Lit + Vite frontend. Infrastructure is provisioned and deployed to Azure using azd (Azure Developer CLI) with Azure Container Apps, Azure Static Web Apps, Azure AI Search (or Qdrant), and optional OpenAI proxy for training scenarios.
Overview
- Purpose: Educational reference and workshop material for building a production‑minded RAG chat application on Azure.
- Audience: Developers learning Azure OpenAI + vector search patterns (Azure AI Search or Qdrant), workshop trainers, contributors.
- Architecture (core runtime):
- Frontend (Static Web App): Lit web components served via Vite build; proxies
/chatto backend during local dev. - Backend API (Container App): Fastify service orchestrating chat + retrieval via LangChain, Azure OpenAI (or provided OpenAI endpoint), Azure AI Search or Qdrant for vector retrieval.
- Ingestion API (Container App): Fastify service handling PDF uploads and embedding ingestion into chosen vector store.
- (Optional) Qdrant (Container App) or Azure AI Search (managed) selected via
useQdrantparameter. - Trainer Proxy (separate project in
trainer/): Fastify reverse proxy to share a single Azure OpenAI instance with attendees. - Observability: Azure Monitor / Application Insights via Bicep modules.
- Frontend (Static Web App): Lit web components served via Vite build; proxies
- Project layout (selected):
src/frontend: Web UI (Lit, Vite).src/backend: Chat + RAG API (Fastify + LangChain + OpenAI + Search/Qdrant).src/ingestion: Document ingestion API (Fastify) + PDF parsing.infra: Bicep templates (main.bicep, parameters, core modules) for full environment.scripts: Helper scripts (PDF ingestion upload).trainer: Workshop trainer proxy & material references.docs: Workshop and slide assets.- Root
package.json: Defines npm workspaces and shared tooling.
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.
- 6d ago First seen · 120 lines · 2,103 tokens per session scan A 549cd51bf61c
azure-openai-rag-workshop AGENTS.md is an instructions file published in the GitHub repository Azure-Samples/azure-openai-rag-workshop (152 stars, last pushed 7mo ago), licensed MIT. It adds 2,103 tokens to every session, about $0.0105 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.