What is azure-openai-rag-workshop AGENTS.md?

Project instructions for an Azure OpenAI workshop that builds a chat app which searches uploaded documents before generating answers. A monorepo is one repository containing several related services and applications.

Working on the frontend, chat API, document-ingestion service, search system, and Azure deployment in the workshop project.

It gives developers a shared map of the workshop’s architecture, tools, deployment setup, and development rules.

How Azure-Samples/azure-openai-rag-workshop configures Codex and OpenC

Reuse

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.

Copy the file

curl -O https://raw.githubusercontent.com/Azure-Samples/azure-openai-rag-workshop/main/AGENTS.md

Clone the repo

git clone --depth 1 https://github.com/Azure-Samples/azure-openai-rag-workshop

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

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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

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Per session 2,103 This file is loaded in full into every session.

When invoked 2,103 The same file — it is already loaded in full.

Security scan A 1 finding. 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	$0.02103	$0.02103
Opus 5.5	$0.00841	$0.00841
Sonnet 5.5	$0.00421	$0.00421
Haiku 4.5	$0.00210	$0.00210

Measured 2mo ago against content hash 549cd51bf61c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

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.

AGENTS.md · 120 lines

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 /chat to 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 useQdrant parameter.
- 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.
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.

Read the full file on GitHub · 120 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 · 120 lines · 2,103 tokens per session scan A 549cd51bf61c

Subscribe to this mod's changes

azure-openai-rag-workshop AGENTS.md is an instructions file published in the GitHub repository Azure-Samples/azure-openai-rag-workshop (154 stars, last pushed 15d ago), licensed MIT. It adds 2,103 tokens to every session, about $0.0084 per session on Opus 5.5. 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.

Data and AI LangChain RAG and vectors azure chatgpt fastify nodejs openai

azure-openai-rag-workshop AGENTS.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
azure-openai-rag-workshop AGENTS.mdAzure-Samples/azure-openai-rag-workshop	Instructions file	154	15d ago	MIT	A	2,103	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

azure-openai-rag-workshop: Instructions file for Codex