Your RAG Pipeline Is Leaking: Fine-Grained Authorization with OpenFGA

Your RAG pipeline probably has an authorization problem you haven't hit yet.

A Field Officer and a Head Office Analyst both ask your internal LLM: "which agents are showing signs of default?" Standard LangChain doesn't know the difference. It retrieves what it finds and passes everything to the model. The Field Officer sees data they were never cleared to see. No error. No warning. You've just broken data isolation at inference time.

A bigger system prompt won't save you. The fix is earlier at the retrieval layer, before the vector database returns anything.

I'll show you how to wire OpenFGA into a Python RAG pipeline in this talk so the LLM only ever see context the requesting user is authorized to access. We'll work through a real fintech scenario, push OpenFGA's permission results into Pinecone metadata filters, and to the model of our choice(OpenAI/Anthropic).

The demo runs twice. Same prompt. Two users. Two completely different answers. You'll leave with the architecture and the code to build it yourself.