Embeddings

Embeddings allow you to convert text into vector representations, which are useful for semantic search, clustering, and similarity comparisons.

Setup

You need an embedding model. Ollama supports models like mxbai-embed-large or nomic-embed-text.

use mojentic::prelude::*;
use std::sync::Arc;

#[tokio::main]
async fn main() -> Result<()> {
    // Initialize gateway
    let gateway = Arc::new(EmbeddingsGateway::new("mxbai-embed-large"));
    
    Ok(())
}

Generating Embeddings

#![allow(unused)]
fn main() {
let text = "The quick brown fox jumps over the lazy dog.";
let vector = gateway.embed(text).await?;

println!("{:?}", &vector[0..5]);
// => [0.123, -0.456, ...]
}

Batch Processing

You can embed multiple texts at once:

#![allow(unused)]
fn main() {
let texts = vec!["Hello", "World"];
let vectors = gateway.embed_batch(texts).await?;
}

Cosine Similarity

Mojentic provides utilities to calculate similarity between vectors:

#![allow(unused)]
fn main() {
use mojentic::utils::math::cosine_similarity;

let similarity = cosine_similarity(&vector1, &vector2);
}