Day 22 of 60: I Built a Production Background Task Pipeline That Processes AI Jobs Asynchronously
The article discusses the development of a production background task pipeline for processing AI jobs asynchronously. It highlights the challenges of synchronous processing and how the new system improves user experience by allowing immediate responses and background processing. The pipeline utilizes FastAPI and PostgreSQL to manage long-running tasks efficiently.
- ▪Processing large documents through an AI model can take 30-60 seconds, leading to potential timeouts and a poor user experience.
- ▪The new pipeline allows clients to submit documents and receive a job ID almost instantly, while the processing occurs in the background.
- ▪The system supports three task types: summarization, extraction, and evaluation, with results stored in PostgreSQL for full audit trails.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3626809) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Cess Mbugua Posted on May 29 Day 22 of 60: I Built a Production Background Task Pipeline That Processes AI Jobs Asynchronously #ai #architecture #backend #python ** The Problem** Processing large documents through an AI model takes 30-60 seconds. A synchronous API makes the client wait the entire time. Browsers timeout. Users think it crashed. The experience feels broken. Yesterday I learned why this happens. Today I built the fix.
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