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Radient – vectorize many data types, not just text
Radient turns many data types - not just text - into vectors for similarity search. - fzliu/radient
Although still predominantly used for text today, vectors will be used extensively across a variety of different modalities in the upcoming months. This evolution is being powered by two independent occurrences: 1) the shift from large language models to large multimodal models (such as Reka and Fuyu), and 2) the rise in adoption for "traditional" tasks such as recommendation and semantic search. Sparse, binary, and multi-vector support Support all relevant embedding models on Huggingface, e.g. non-seq2seq models Data sources from object storage, Google Drive, Box, etc Vector sinks to Zilliz, Databricks, Confluent, etc
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