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gpt-rag-ingestion: Instructions file for GitHub Copilot

.github/instructions/python.instructions.md

gpt-rag-ingestion python.instructions.md is an instructions file for GitHub Copilot from Azure/gpt-rag-ingestion. It costs 203 tokens per session, scanned A, original, MIT.

Python 3.12 project guidance for an Azure document-ingestion service. It covers code structure, asynchronous operations, external calls, logging, types, errors, concurrency, and tests.

In plain words
What is it for?
It guides Python module design, type hints, async API and Azure code, timeouts, retries, batching, logging, exception handling, and focused or full test runs.
Why use it?
It helps keep the service responsive and predictable by avoiding blocking work, limiting retries and resource use, and keeping transformations testable without live Azure access.

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 203 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/python.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
<a href="https://agentmods.dev/instructions/azure/gpt-rag-ingestion/python"><img src="https://agentmods.dev/badge/instructions/azure/gpt-rag-ingestion/python.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 203 This file is loaded in full into every session.
When invoked 203 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 $0.00203 $0.00203
Opus 5.5 $0.00081 $0.00081
Sonnet 5.5 $0.00041 $0.00041
Haiku 4.5 $0.00020 $0.00020

Measured 2mo ago against content hash 898ce6a0d7af, 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 python.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/python.instructions.md · 20 lines

What it actually says

Python 3.12

  • Keep modules focused and follow local naming and import conventions.
  • Use explicit type hints and typed boundary records where practical.
  • Preserve async behavior through API, job, and Azure SDK paths. Do not call blocking network or file I/O on the event loop.
  • Bound concurrency, retries, batches, memory, and external-call duration.
  • Catch expected exceptions where context and recovery are known; do not add broad catches or success-shaped fallbacks.
  • Use configured logging and telemetry, never print.
  • Keep pure transformations testable without Azure credentials or network access.
  • Run focused pytest tests, then the broader suite according to risk.
  • Load engineering-principles for meaningful design, security, data, integration, or operational changes.
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 · 20 lines · 203 tokens per session scan A 898ce6a0d7af

Subscribe to this mod's changes

gpt-rag-ingestion python.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 203 tokens to every session, about $0.0008 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.

LanguagesPython
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