How I rescued a RAG assistant from memory leaks and got it running on a 512MB RAM free tier
The article discusses the author's experience in optimizing a Retrieval-Augmented Generation (RAG) assistant for deployment on a limited-resource server. It highlights the challenges faced when applying standard RAG techniques to complex technical manuals in the manufacturing sector. The author details the innovative solutions implemented to enhance performance and compliance with industry standards.
- ▪The author faced Out-Of-Memory errors when deploying a RAG prototype on a 512MB RAM free-tier instance.
- ▪Standard RAG methods struggled with technical manuals due to domain-specific terminology and context fragmentation.
- ▪A multi-stage retrieval engine was developed using LlamaIndex, Qdrant, and Mistral-7B to improve retrieval accuracy.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3957218) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } shaikhadibbb Posted on May 29 How I rescued a RAG assistant from memory leaks and got it running on a 512MB RAM free tier #rag #ai #devops #python A few weeks ago, I had a classic "works on my machine" moment. I had built a nice RAG prototype locally using Ollama and PyTorch. But when I tried to deploy it for staging on a Render free-tier instance (which has a brutal 512MB RAM limit), the server instantly crashed with Out-Of-Memory (OOM) errors.
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