GPT-RAG Data Ingestion is a service that processes documents such as PDFs, images, spreadsheets, transcripts, and SharePoint files so they can be searched through Azure AI Search. It prepares data with format-specific chunking and text or image embeddings for multimodal retrieval in agent-based applications.
Borrowing it
Nothing to install: this file belongs to Azure/gpt-rag-ingestion. 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/gpt-rag-ingestion/main/AGENTS.mdgit clone --depth 1 https://github.com/Azure/gpt-rag-ingestionWrote 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/gpt-rag-ingestion/agents-md)<a href="https://agentmods.dev/instructions/azure/gpt-rag-ingestion/agents-md"><img src="https://agentmods.dev/badge/instructions/azure/gpt-rag-ingestion/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 | $0.01304 | $0.01304 |
| Opus 5 | $0.00652 | $0.00652 |
| Sonnet 5 | $0.00261 | $0.00261 |
| Haiku 4.5 | $0.00130 | $0.00130 |
Grade A, and why
gpt-rag-ingestion 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GPT-RAG ingestion engineering-agent contract
This is the stable repository-wide contract for GitHub Copilot engineering
agents. Detailed procedures belong in .github/skills/; path-specific rules
belong in .github/instructions/; branching and release policy remains in
.github/copilot-instructions.md.
Priority
Follow, in order:
- Security, privacy, authorization, and platform instructions.
- Task requirements and acceptance criteria.
- Executable configuration and versioned contracts in this repository.
.github/copilot-instructions.md, this contract, and applicable scoped instructions.- Local conventions in the affected code.
Do not guess behavior that could affect indexed data, document authorization, shared contracts, production jobs, or releases. Record uncertainty and obtain a human decision.
What this repository is
gpt-rag-ingestion is the Python 3.12 data plane that turns source documents
into chunks and embeddings and writes them to Azure AI Search. It supports
Blob Storage, SharePoint, NL2SQL, multimodal content, scheduled ingestion and
purge jobs, versioned audit events, a FastAPI operator API, and a React
operator dashboard.
The repository is one runtime component of Azure/GPT-RAG. Shared deployment, configuration, contracts, and release pins must remain compatible with the umbrella repository and other consumers.
The files under .github/agents/ define Copilot engineering roles used to
develop and operate this repository. They are not runtime ingestion agents.
The modules under jobs/ and the APScheduler registrations in main.py are
runtime workers executed by the ingestion service; never describe or modify
them as Copilot agents.
Repository boundaries
chunking/: document orchestration and format-specific chunkers selected throughchunking/chunker_factory.py.jobs/: long-running index, purge, and source synchronization workers.tools/: Azure and source-system adapters, credentials, and clients.api/: thin FastAPI operator endpoints.frontend/: React/Vite operator dashboard.telemetry/: OpenTelemetry and versioned ingestion audit behavior.contracts/: shared schema bytes and integrity pins.scripts/,azure.yaml,Dockerfile, andinfra/: build, deployment, and Azure runtime surfaces.tests/: focused Python behavior and contract tests.
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.
- 8d ago First seen · 135 lines · 1,304 tokens per session scan A c5c2114ddbe7
gpt-rag-ingestion AGENTS.md is an instructions file published in the GitHub repository Azure/gpt-rag-ingestion (189 stars, last pushed yesterday), licensed MIT. It adds 1,304 tokens to every session, about $0.0065 per session on Opus 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.
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.