Snowflake recently announced the launch of Arctic Embed L 2.0 and Arctic Embed M 2.0, two small and powerful embedding models tailored for multilingual search and retrieval. The models are available in medium and large variants, with the medium model incorporating 305 million parameters and the large variant with 568 million parameters. Both models support context lengths of up to 8,192 tokens. They demonstrate high-quality retrieval across multiple languages and excel in benchmarks like MTEB and CLEF.
This article explores 7 popular approaches to generating unique IDs in distributed systems, including UUIDs, database auto-increment, Snowflake IDs, Redis-based generation, NanoID, hash-based IDs, and ULIDs.
