[Tutorial] Efficient RAG Pipelines with Qdrant on Upsun 🧠

Context stuffing is a massive drain on resources and increases your cost per query. It is a messy way to handle large datasets when you need fast and relevant answers.

Vector search with Qdrant offers a more technical and cost effective alternative. By building a proper retrieval pipeline, you can get better results without the overhead. Here is what this workflow covers:

Moving from context stuffing to vector search for 25x lower costs
Implementing chunking strategies that actually work for your data
Setting up embedding scripts and retrieval logic
Using a debug panel for local testing and deployment

Check out the full technical write-up to see how to deploy this pipeline on Upsun:

Building a RAG pipeline with Qdrant on Upsun - Upsun Developer

Replace context stuffing with vector search using Qdrant for 25x lower cost per query.

developer.upsun.com

[Tutorial] Efficient RAG Pipelines with Qdrant on Upsun 🧠

Building a RAG pipeline with Qdrant on Upsun - Upsun Developer

Tags

Author

Stats

Published

You Might Also Like

I Got Tired of Reading Strangers’ Codebases, So I Built an AI That Reads Them For Me

Add a PASS/WARN/FAIL Quality Gate to Your RAG Pipeline in 30 Seconds

Production-Grade RAG: Why Vector Search Isn't Enough (and How Hybrid Search Fills the Gaps)

RAG-Based Testing Series — Part 1: What Is RAG & Why Your Old Testing Playbook Won't Work Here

RAG-Based Testing Series — Part 4: Edge Cases — What Breaks RAG & How to Catch It

Search bug or model bug - testing a RAG system to tell them apart