Snowflake, a prominent player in AI technology, has unveiled its latest offering – the Snowflake Arctic embed family of models. This open-source initiative aims to revolutionize text embedding tasks and provide organizations with cutting-edge retrieval capabilities. Let’s delve deeper into this exciting new development in Retrieval Augmented Generation (RAG).
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Unveiling the Arctic Embed Family
Snowflake has officially launched the Snowflake Arctic embed family of models, available under the Apache 2.0 license. This family comprises models of varying sizes and context windows, tailored to address diverse text embedding requirements. Ranging from x-small to large, these models promise state-of-the-art performance for RAG applications.
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Leveraging Expertise and Innovation
Neeva is an ad-and-tracker-free search engine that was bought over by Snowflake. Sridhar Ramaswamy, CEO of Snowflake, emphasizes the crucial role played by the Neeva team in developing these new models. Snowflake’s acquisition of Neeva last year has proven instrumental in infusing expertise and innovation into the Arctic embed family, elevating it to new heights.
Performance and Superiority
The largest model in the Arctic embed family, boasting 330 million parameters, has emerged as a frontrunner in the Massive Text Embedding Benchmark (MTEB) Retrieval Leaderboard. It outperforms its counterparts in efficiency and effectiveness with an average retrieval performance exceeding 55.9.
Practicality and Accessibility
Snowflake’s Arctic embed models are readily available on platforms like Hugging Face, with plans for integration into Snowflake Cortex embed function underway. This accessibility underscores Snowflake’s commitment to democratizing advanced AI solutions and empowering organizations of all sizes.
Our Say
Snowflake’s foray into open-source solutions with the Arctic embed family marks a significant milestone in text embedding and RAG. By offering high-performance models under a permissive license, Snowflake aims to challenge the dominance of closed-source API providers. With unparalleled retrieval capabilities and a focus on practicality, the Arctic embed models signal a new era of innovation and accessibility in AI technology.
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Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.
