candding 0.1.0

Pure candle embeddings for Rust with no ONNX: dense, sparse, multi-vector and rerank models today, image models on the roadmap
Documentation 
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//! Embed two passages and a query on the auto-selected device and score them.

use candding::TextEmbedding;

fn main() -> candding::Result<()> {
  let model = TextEmbedding::builder("BAAI/bge-small-en-v1.5").build()?;
  let passages = model.passage_embed(
    &[
      "Embeddings map text to vectors.",
      "Rust is a systems programming language.",
    ],
    None,
  )?;
  let query = model.query_embed(&["what do embeddings do"], None)?;
  for (i, passage) in passages.iter().enumerate() {
    let score: f32 = query[0].iter().zip(passage).map(|(q, p)| q * p).sum();
    println!("passage {i}: {score:.4}");
  }
  Ok(())
}