A storage engine for vector machine learning embeddings
Embeddinghub is a database built for machine learning embeddings. It is built with four goals in mind. Store embeddings durably and with high availability Allow for approximate nearest neighbor operations Enable other operations like partitioning, sub-indices, and averaging Manage versioning, access control, and rollbacks painlessly Features Supported Operations: Run approximate nearest neighbor lookups, average multiple embeddings, partition tables (spaces), cache locally while training, and more. Storage: Store and index billions vectors embeddings from our storage layer. Versioning: Create, manage, and […]
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