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How does Exa serve billion-scale vector search? We combine binary quantization, Matryoshka embeddings, SIMD, and IVF into a novel system that can beat alternatives like HNSW. Shreyas gave a talk today at the AI Engineer World's Fair explaining our approach! ⬇️

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Exa

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85,482 Aufrufe • vor 1 Jahr •via X (Twitter)

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10 Kommentare

Profilbild von Jeffrey Wang
Jeffrey Wangvor 1 Jahr

@shreyas4_ @aiDotEngineer I wanna be nearest neighbors w/ @shreyas4_

Profilbild von Tigran III
Tigran IIIvor 1 Jahr

@shreyas4_ @aiDotEngineer i am still struggling to believe how much cracked engineering talent is coming from that one university. @shreyas4_ what's the secret sauce?

Profilbild von Martyn Strydom 🤸
Martyn Strydom 🤸vor 1 Jahr

@shreyas4_ @aiDotEngineer Unreal @shreyas4_

Profilbild von Karan☕
Karan☕vor 1 Jahr

@shreyas4_ @aiDotEngineer great talk learned a lot of new things, had this question: I think if you use binary quantization, for smaller embeddings you will get poorer results because of lossy compression(already dimension reduction is done and then BQ)

Profilbild von Prashant Dixit
Prashant Dixitvor 1 Jahr

@shreyas4_ @aiDotEngineer Anyone wants to just give a quick try and Build Matryoshka Embedding based RAG in a min, Give it a try 🙂

Profilbild von sophia
sophiavor 1 Jahr

@shreyas4_ @aiDotEngineer I'm confused why you said 8TB of memory to hold everything in RAM is too expensive. Back of the envelope Hetzner has 24 core/192GB systems for $366/mo. 8TB would be ~$200k/y or ~18k queries/$ @ 100 QPS

Profilbild von Hamish Ogilvy
Hamish Ogilvyvor 1 Jahr

@shreyas4_ @aiDotEngineer Nice work. So funny how obsessed people were with HNSW…

Profilbild von omkaar
omkaarvor 1 Jahr

@shreyas4_ @aiDotEngineer awesome great job guys

Profilbild von Aarush Sah
Aarush Sahvor 1 Jahr

@shreyas4_ @aiDotEngineer i love shreyas shreyas is so cool

Profilbild von agi
agivor 1 Jahr

@shreyas4_ @aiDotEngineer love this - great insight for my product

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