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.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/azure/gpt-rag-ingestion/api-frontendgit 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/api-frontend)<a href="https://agentmods.dev/instructions/azure/gpt-rag-ingestion/api-frontend"><img src="https://agentmods.dev/badge/instructions/azure/gpt-rag-ingestion/api-frontend.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 | $0.00208 | $0.00208 |
| Opus 5 | $0.00104 | $0.00104 |
| Sonnet 5 | $0.00042 | $0.00042 |
| Haiku 4.5 | $0.00021 | $0.00021 |
Grade A, and why
gpt-rag-ingestion api-frontend.instructions.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 5d 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.
What it actually says
Operator API and dashboard
- Keep FastAPI handlers thin and move reusable job, configuration, storage, or domain behavior to the owning module.
- Preserve each established authentication boundary. Operator job and configuration mutations use the configured Admin role, while ingestion endpoints retain their API-key contract. Do not weaken authentication or broaden read exposure without an explicit security decision.
- Validate external input and return actionable HTTP errors without leaking secrets, document content, or internal credentials.
- Preserve API response compatibility or coordinate frontend and documentation updates in the same change.
- Keep frontend types aligned with API payloads and reuse existing components, tokens, and interaction patterns.
- Use the scripts in
frontend/package.json:npm test,npm run lint, andnpm run build. - Test loading, empty, success, failure, authorization, polling/timer cleanup, and accessibility-relevant behavior as applicable.
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.
- 5d ago First seen · 23 lines · 208 tokens per session scan A 7006de08e269
gpt-rag-ingestion api-frontend.instructions.md is an instructions file published in the GitHub repository Azure/gpt-rag-ingestion (189 stars, last pushed yesterday), licensed MIT. It adds 208 tokens to every session, about $0.0010 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
GPT-RAG release.instructions.md
Instructions for Azure/GPT-RAG, a project described as: Enterprise-grade accelerator for agentic RAG on Azure. Built on Microsoft Foundry with Foundry IQ as the default retrieval backend, Microsoft Agent Framework orchestration, Zero-Trust architecture and IaC.
RAGElo copilot-instructions.md
Instructions for zetaalphavector/RAGElo, covering copilot instructions — ragelo, project overview, commands, install for development and run with openai integration tests (requires openaiapikey).
pdf-brain AGENTS.md
Instructions for joelhooks/pdf-brain, covering pdf-brain agent notes, libsql quirks, ai sdk pattern, key files and docs.
rag-code-mcp copilot-instructions.md
Instructions for doITmagic/rag-code-mcp, covering copilot instructions - ragcode mcp, ⚖️ the golden rule, project overview, architecture & patterns and developer workflows.
autonomous-rag CLAUDE.md
Claude Code instructions for BhavyaFattania/autonomous-rag, covering claude code configuration, rules, knowledge graph navigation (graphify-out/), design principles (derived from codebase audit) and build & test.
ragdocs-mcp copilot-instructions.md
Copilot instructions for andnp/ragdocs-mcp, covering github copilot instructions for mcp-markdown-ragdocs, project overview, technology stack, architecture and project layout.