I built a self-hosted RAG system for Journalism — What Production Retrieval Taught Me
The article discusses the development of Atlas, a self-hosted retrieval system for journalism. It highlights the challenges faced during deployment, particularly regarding retrieval quality and the importance of hybrid search methods. Key features of Atlas include grounded Q&A, claim-level fact-checking, and a full story workspace for reporters.
- ▪Atlas ingests live RSS feeds from major news outlets every 15 minutes and uses local models for content embedding.
- ▪The author learned that pure vector search is inadequate for current events journalism due to the significance of proper nouns.
- ▪Batch embedding significantly improved the efficiency of the system, reducing processing time from 51 seconds to approximately 3 seconds.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3944287) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Preetha Posted on May 22 I built a self-hosted RAG system for Journalism — What Production Retrieval Taught Me #rag #mcp #postgressql #agents Over the last few months, I built Atlas — a fully self-hosted retrieval system designed for journalism workflows. No paid APIs. No hosted vector databases or AI infrastructure. Just local models, PostgreSQL, pgvector, Celery, and a retrieval pipeline built to survive production traffic.
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