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/agents/architecture.agent.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/agents/azure/gpt-rag-ingestion/architecture)<a href="https://agentmods.dev/agents/azure/gpt-rag-ingestion/architecture"><img src="https://agentmods.dev/badge/agents/azure/gpt-rag-ingestion/architecture/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/agents/azure/gpt-rag-ingestion/architecture"><img src="https://agentmods.dev/badge/agents/azure/gpt-rag-ingestion/architecture.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 | $0.00041 | $0.00256 |
| Opus 5.5 | $0.00016 | $0.00102 |
| Sonnet 5.5 | $0.00008 | $0.00051 |
| Haiku 4.5 | $0.00004 | $0.00026 |
Grade A, and why
architecture 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
Ingestion architecture
Follow AGENTS.md and load engineering-principles and
architecture-decision.
Start from the retrieval or operator outcome, constraints, and measurable characteristics. Compare alternatives in the context of source systems, chunking quality, Azure AI Search contracts, document authorization, multimodal processing, throughput, memory, cost, failure recovery, and cross-repository compatibility.
Treat App Configuration behavior, chunking/chunker_factory.py, Search
payloads, schemas under contracts/, and current jobs as executable sources
of truth. Do not turn an Azure service or framework preference into a
requirement without evidence.
Explicitly distinguish the Copilot architecture role from runtime workers in
jobs/; this role designs changes but is never scheduled by the ingestion
service.
Output handoff to implementation: decision, affected repositories,
boundaries, contracts, fitness functions, risks, migration and rollback, and
open questions.
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 · 30 lines · 41 tokens per session scan A 95bf952bf4aa
architecture is an agent published in the GitHub repository Azure/gpt-rag-ingestion (188 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 256 once invoked, about $0.0002 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.
architecture compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| architectureAzure/gpt-rag-ingestion | Agent | 188 | 1mo ago | MIT | A | 41 | Claude Code |
| playwright-test-generatormicrosoft/playwright | Agent | 99k | yesterday | Apache-2.0 | A | 151 | Claude Code |
| .NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginners | Agent | 79k | 4d ago | MIT | A | 33 | Claude Code |
| code-revieweranthropics/claude-cookbooks | Agent | 54k | 2d ago | MIT | A | 52 | Claude Code |
| AVM Owner Triagegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 61 | Claude Code |
| Ultimate Transparent Thinking Beast Modegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 11 | Claude Code |
| WinForms Expertgithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 19 | Claude Code |