Building a Production RAG Pipeline with n8n, Qdrant, and Gemini: A Step-by-Step Walkthrough

The first version of a RAG system always looks convincing. You connect a document loader, a vector database, and a large model, ask a question, and the answer comes back with impressive confidence. Then production happens. A support agent asks about a refund policy that changed last week, and the bot answers with the old policy. A user from the finance team sees chunks they should never see. Gemini starts returning 429 errors during a reindex. A 3,000-document ingestion workflow fails at document 2,412, and you have no idea how to resume safely. That is the gap between a RAG demo and a production RAG pipeline. This walkthrough focuses on building a maintainable retrieval-augmented generation pipeline using n8n for orchestration, Qdrant for vector storage and filtered retrieval, and Gemini for embedding and answer generation. The goal is not just “make it answer.” The goal is to make it operable: idempotent ingestion, access-controlled retrieval, retry-safe automation, grounded answers, and a path for evaluation. TL;DR Treat RAG as two separate pipelines : ingestion and query. Store more than vectors in Qdrant: source_id , acl , version , updated_at , chunk_index , and text. Make ingestion idempotent so reprocessing documents does not create duplicate truth. Use Qdrant filters for permissions, freshness, and document status. Force Gemini to answer only from retrieved evidence and return citations. Add retries, timeouts, dead-letter handling, and evaluation before users do the testing for you. 📋 Table of Contents The Production Problem with Demo RAG 1. Split RAG Into Two Pipelines Before You Automate Anything 2. Design the Qdrant Collection Around Access Control and Freshness 3. Chunk for Retrieval, Not for Reading 4. Make Ingestion Idempotent and Resumable 5. Embed in Controlled Batches Without Dropping Documents 6. Retrieve With Filters, Not Blind Similarity 7. Make Gemini Prove It Used the Evidence 8. Add the Production Guardrails: Retries, Timeouts, and Dead Lette