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Does RAG Even Scale? EyeLevel vs LangChain
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Kandungan disediakan oleh Brian Carter. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Brian Carter atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
A research team from EyeLevel.ai has found that vector databases, which are commonly used in RAG (Retrieval-Augmented Generation) systems, have a scaling problem. Their research shows that the accuracy of vector similarity search degrades significantly as the number of pages in the database increases, leading to a substantial performance hit. This problem can be attributed to the way modern encoders organize information in high-dimensional vector spaces. In contrast, EyeLevel's RAG platform, which does not rely on vectors, demonstrates superior performance at scale, losing only 2% accuracy with 100,000 pages. The team's findings highlight the need for developers to be aware of these challenges when scaling RAG applications in production.
Read more: https://www.reddit.com/r/Rag/comments/1g3h9w2/does_rag_have_a_scaling_problem/
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