Embeddings are dense, numerical representations of real-world objects and relationships, expressed as vectors. They are often created by first defining a supervised machine learning problem, known as a "surrogate problem." Embeddings intend to capture the semantics of the inputs they were derived from, subsequently getting shared and reused for improved learning across machine learning models. Embeddinghub lets you achieve this in a streamlined, intuitive way.
Embeddinghub's Alpha version is exclusively for single-node configurations. It uses RocksDB to durably store embeddings and metadata, taking advantage of HNSWLib to build approximate nearest neighbor indices. The related Python client also has the ability to use HNSWLib for building local embeddings but does not currently handle durable storage. Embeddinghub's server communicates via gRPC, with a proto service file accessible here. All metadata is also stored in serialized protobuf form, as defined here.
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