Generate vector embeddings and rerank them using fastembed-rs, a Rust library.
Embeddings that you can use in machine learning models for tasks like product recommendations and text classification.
Using fastembed-rs can save time when building and testing machine learning models, and can also improve their performance by using optimized embeddings.
"A developer can use fastembed-rs to create embeddings for their app's product catalog, then use a machine learning model to recommend related products to users."
Novice developers may find it best to first gain some experience with machine learning and Rust before using fastembed-rs.
A senior engineer would reach for fastembed-rs when they need to optimize the performance and efficiency of their large-scale machine learning models.
fastembed-rs is specifically designed for generating vector embeddings, not for building complete machine learning models.
Generates ultra-fast vector embeddings and enables local semantic search and text ranking for AI applications without external dependencies.
EmbeddingModel::from_pretrained(Model::BGESmall).embed(vec!["search query"])
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