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Does RAG Even Scale? EyeLevel vs LangChain
Manage episode 445344642 series 3605861
المحتوى المقدم من Brian Carter. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Brian Carter أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.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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