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Does RAG Even Scale? EyeLevel vs LangChain

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Manage episode 445344642 series 3605861
Innhold levert av Brian Carter. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Brian Carter eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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/

  continue reading

71 episoder

Artwork
iconDel
 
Manage episode 445344642 series 3605861
Innhold levert av Brian Carter. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Brian Carter eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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/

  continue reading

71 episoder

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