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gpt-rag-ingestion: Instructions file for GitHub Copilot

.github/instructions/chunking.instructions.md

gpt-rag-ingestion chunking.instructions.md is an instructions file for GitHub Copilot from Azure/gpt-rag-ingestion. It costs 225 tokens per session, scanned A, original, MIT.

Implementation rules for splitting documents into smaller searchable pieces called chunks. They cover adding formats, preserving chunk data and order, handling unsafe input, limiting resources, and testing results.

In plain words
What is it for?
Use them when adding or changing PDF, image, spreadsheet, transcript, or other document-format chunking behavior.
Why use it?
They help new document formats behave consistently with existing ones. They also reduce the risk of losing references, exposing sensitive content, or exhausting system resources.

Instructions file for GitHub Copilot ✓ vendor

Written for GitHub Copilot: a Copilot instructions file.

This is Azure/gpt-rag-ingestion's own configuration. It tells GitHub Copilot how to work on gpt-rag-ingestion itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything gpt-rag-ingestion configures →

not rated 188repo 1mo ago A scan 225 tokens original MIT
View source ↗ Azure/gpt-rag-ingestion
About the project

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.

Azure/gpt-rag-ingestion · 188 stars · on GitHub

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/Azure/gpt-rag-ingestion/main/.github/instructions/chunking.instructions.md
Clone the repo
git clone --depth 1 https://github.com/Azure/gpt-rag-ingestion

Made for: GitHub Copilot.

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.

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README.md
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Your own site
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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.

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Your own site · 80×15
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Per session 225 This file is loaded in full into every session.
When invoked 225 The same file — it is already loaded in full.
Security scan A 0 findings. 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00225 $0.00225
Opus 5.5 $0.00090 $0.00090
Sonnet 5.5 $0.00045 $0.00045
Haiku 4.5 $0.00022 $0.00022

Measured 2mo ago against content hash da7f63923a77, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

.github/instructions/chunking.instructions.md · 25 lines

What it actually says

Document chunking

  • Add a new format with a focused class under chunking/chunkers/ and register it in chunking/chunker_factory.py.
  • Keep extension dispatch centralized. Do not add format if/elif branches 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.
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.

  1. 2mo ago First seen · 25 lines · 225 tokens per session scan A da7f63923a77

Subscribe to this mod's changes

gpt-rag-ingestion chunking.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 225 tokens to every session, about $0.0009 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.

Data and AIRAG and vectors
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