Don't let a billion RAG docs drown your 25-result pipeline
The article discusses the importance of implementing backpressure in streaming pipelines to manage large volumes of data efficiently. It highlights how a naive approach can lead to unnecessary processing of data, while a more sophisticated method can ensure that only the required amount of data is processed. The author provides examples of using the WorkIt library to demonstrate effective handling of backpressure in a pipeline with a billion documents.
- ▪Backpressure allows a producer to pause when the consumer cannot keep up, preventing unnecessary work.
- ▪A naive promise collection can lead to excessive data processing, while an async iterator can manage data flow more effectively.
- ▪The WorkIt library provides tools for creating streaming pipelines that respect backpressure and optimize data handling.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 2851804) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } AdmilsonCossa Posted on May 25 Don't let a billion RAG docs drown your 25-result pipeline #ai #programming #webdev #rag Backpressure For Streaming Pipelines Last time we showed how to terminate non-cooperative CPU work at the worker boundary. This article stays cooperative but adds the missing piece: backpressure, the runtime contract that lets a producer pause the moment the consumer can't keep up. A RAG ingest pipeline has a billion candidate documents.
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