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);
}

OpenAI long text

The OpenAI gateway splits long text into parts of at most 8191 tokens with the cl100k_base tokenizer. It weights each part vector by the number of tokens in that part, then normalizes the result to length 1. A short trailing part has less weight than a full part. For a single part, the gateway returns the provider vector unchanged.