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/.github/instructions/jobs-indexing.instructions.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/jobs-indexing)<a href="https://agentmods.dev/instructions/azure/gpt-rag-ingestion/jobs-indexing"><img src="https://agentmods.dev/badge/instructions/azure/gpt-rag-ingestion/jobs-indexing/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.
<a href="https://agentmods.dev/instructions/azure/gpt-rag-ingestion/jobs-indexing"><img src="https://agentmods.dev/badge/instructions/azure/gpt-rag-ingestion/jobs-indexing.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00261 | $0.00261 |
| Opus 5.5 | $0.00104 | $0.00104 |
| Sonnet 5.5 | $0.00052 | $0.00052 |
| Haiku 4.5 | $0.00026 | $0.00026 |
Grade A, and why
gpt-rag-ingestion jobs-indexing.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 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.
What it actually says
Runtime jobs and Azure AI Search
These files implement runtime workers. They are not GitHub Copilot engineering agents.
- Preserve job mutual exclusion, run IDs, terminal states, cancellation, summaries, retries, and per-file failure visibility.
- Bound source downloads, memory, concurrency, Search batch size, scans, and
retries. Do not create unbounded
gatheror collection behavior. - Treat Azure AI Search batch responses as authoritative per-document outcomes. Do not report a submitted upload or delete as confirmed success.
- Preserve stable document keys, parent/child relationships, index field types, metadata, ACL fields, and deletion semantics.
- Reserved security metadata must not leak into
custom_metadata. - Keep elevated-read headers limited to service-side operations that require them; never expose that capability through user input.
- A retry, purge, or reindex path must be idempotent or document its duplicate and recovery behavior.
- Audit events must remain correlated and bounded. Audit export is best-effort; indexing and deletion are not.
- Add focused tests and, when possible, verify terminal job state plus actual Search documents in a controlled environment.
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 · 27 lines · 261 tokens per session scan A 3bcb44387fcc
gpt-rag-ingestion jobs-indexing.instructions.md is an instructions file published in the GitHub repository Azure/gpt-rag-ingestion (188 stars, last pushed 1mo ago), licensed MIT. It adds 261 tokens to every session, about $0.0010 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.
gpt-rag-ingestion jobs-indexing.instructions.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| gpt-rag-ingestion jobs-indexing.instructions.mdAzure/gpt-rag-ingestion | Instructions file | 188 | 1mo ago | MIT | A | 261 | GitHub Copilot |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
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| 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 |