🦀 Embeddings

use mistralai_client::v1::{client::Client, constants::EmbedModel};

static KEY = std::env::var("MISTRAL_API_KEY").unwrap();

// ...

let client = Client::new(Some(KEY.to_string()), None, None, None)?;
let input = vec!["Joram is the main character in the Darksword Trilogy.".to_string()];

let result = client.embeddings_async(MODEL, input, None).await?;
println!("{:?}", result);

use ollama_rs::{Ollama, generation::embeddings::GenerateEmbeddingsRequest};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let ollama = Ollama::default();

    let model = "all-minilm:22m".to_string()
    let prompt = "this is your input text".to_string();

    let request = GenerateEmbeddingsRequest::new(model, prompt);
    let response = ollama.generate_embeddings(request).await?;

    println!("Generated embeddings (first 5): {:?}", &response.embeddings[..5]);
    println!("Embedding vector length: {}", response.embeddings.len());

    Ok(())
}

use rust_bert::sentence_embeddings::{
    SentenceEmbeddingsBuilder, SentenceEmbeddingsModelType,
};

fn main() -> anyhow::Result<()> {
    // Set up the model builder, specifying the model type
    let model = SentenceEmbeddingsBuilder::remote(
        SentenceEmbeddingsModelType::AllMiniLmL6V2
    ).create_model()?;

    // Define the sentences to embed
    let sentences = [
        "this is your text",
        "you can encode more than one in batch"
    ];

    // Generate the embeddings
    let embeddings = model.encode(&sentences)?;

    // Print the results
    for (i, embedding) in embeddings.iter().enumerate() {
        // Truncate for display purposes
        let truncated_embedding: Vec<_> = embedding.iter().take(5).cloned().collect();

        println!("\nSentence: '{}'", sentences[i]);
        println!("Embedding (first 5 values): {:?}", truncated_embedding);
        println!("Embedding dimensions: {}", embedding.len());
    }

    Ok(())
}