Embeddings and reranking

The same loaded Model type exposes task-specific methods. Calling the wrong method for a checkpoint returns Error::TaskMismatch.

Embeddings

use libmir::{EmbeddingRequest, GenerationOverrides, Library, RuntimeConfig};

let model = Library::new(RuntimeConfig::default()).load(
    "/models/embedding",
    GenerationOverrides::default(),
    &mut |_| {},
)?;

let output = model.embed(EmbeddingRequest {
    inputs: vec!["first text".into(), "second text".into()],
    dimensions: None,
    prompt_name: None,
})?;

println!("vectors={}", output.embeddings.len());
println!("tokens={}", output.prompt_tokens);
Ok::<(), libmir::Error>(())

dimensions may retain a prefix no larger than the checkpoint’s native dimension. prompt_name must name a preset declared by the checkpoint.

Reranking

use libmir::{GenerationOverrides, Library, RerankRequest, RuntimeConfig};

let model = Library::new(RuntimeConfig::default()).load(
    "/models/reranker",
    GenerationOverrides::default(),
    &mut |_| {},
)?;

let output = model.rerank(RerankRequest {
    query: "native inference".into(),
    documents: vec!["Rust and Metal".into(), "a hosted service".into()],
    max_length: None,
    raw_scores: false,
})?;

for result in output.results {
    println!("{} {:.4} {}", result.index, result.score, result.document);
}
Ok::<(), libmir::Error>(())

Results are relevance-sorted but preserve each candidate’s original index. Set raw_scores to return classifier logits instead of a logistic score.