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/chunking.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/chunking)<a href="https://agentmods.dev/instructions/azure/gpt-rag-ingestion/chunking"><img src="https://agentmods.dev/badge/instructions/azure/gpt-rag-ingestion/chunking.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 | $0.00225 | $0.00225 |
| Opus 5 | $0.00112 | $0.00112 |
| Sonnet 5 | $0.00045 | $0.00045 |
| Haiku 4.5 | $0.00022 | $0.00022 |
Grade A, and why
gpt-rag-ingestion chunking.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 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.
What it actually says
Document chunking
- Add a new format with a focused class under
chunking/chunkers/and register it inchunking/chunker_factory.py. - Keep extension dispatch centralized. Do not add format
if/elifbranches to jobs, APIs, or callers. - Reuse base chunker behavior and shared parsing helpers before adding new abstractions.
- Preserve chunk IDs, ordering, source/page references, content, embeddings, metadata, and error/warning shapes unless a coordinated contract change is approved.
- Treat document bytes and extracted content as untrusted and potentially sensitive. Enforce format, size, page, and resource bounds.
- Errors must identify the document operation safely without logging document content or credentials.
- Test representative valid, malformed, empty, large/bounded, and format-routing cases when independently testable.
- For behavior changes, complete an end-to-end representative ingestion and verify the resulting Azure AI Search documents when an environment is available.
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 · 25 lines · 225 tokens per session scan A da7f63923a77
gpt-rag-ingestion chunking.instructions.md is an instructions file published in the GitHub repository Azure/gpt-rag-ingestion (189 stars, last pushed today), licensed MIT. It adds 225 tokens to every session, about $0.0011 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.
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