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Does RAG Even Scale? EyeLevel vs LangChain
Manage episode 445344642 series 3605861
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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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