Function vectorize_image_concurrently

pub async fn vectorize_image_concurrently<C>(
    prompts: Vec<String>,
    vector: &mut Vector<DynamicImage>,
    client: Client<C>,
    model_parameters: ModelParameters,
) -> Result<(), Error>
where
    C: Config + Send + Sync + 'static,

Concurrently vectorizes an image with multiple prompts.

Arguments

• model - The name/identifier of the LLM model to use
• prompts - A vector of prompts to process concurrently
• vector - A mutable reference to the Vector struct containing the image
• client - The OpenAI API client

Returns

• Result<(), Error> - Ok(()) on success, Error on failure

Each prompt’s dimensionality is specified by how many digits that it requires the LLM to return. The final dimensionality of the vector is calculated by number of prompts * digits specified by each prompt.

Examples found in repository

examples/vectorize_images.rs (lines 45-50)

async fn main() -> Result<(), Error> {
    // Load image
    let image_path: &str = "./examples/images/54e2c8ea-58ef-4871-ae3f-75eabd9a2c6c.jpg";
    let test_image: DynamicImage = image::open(image_path).unwrap();

    // Create a Vector object from the image
    let mut vector: Vector<DynamicImage> = Vector::from_image(test_image);

    // Initialize client
    let client: Client<OpenAIConfig> = Client::with_config(
        OpenAIConfig::new()
            .with_api_base("http://192.168.0.101:11434/v1") // comment this out if you use OpenAI instead of Ollama
            .with_api_key("your_api_key")
    );

    // Initialize prompts
    let prompts: Vec<String> = vec![
        "output in json. Rate the image's offensiveness from 0.0 to 10.0. {'offensiveness': your score}".to_string(),
        "output in json. Rate the image's friendliness from 0.0 to 10.0. {'friendliness': your score}".to_string(),
    ];

    // Initialize model parameters
    let model_parameters = ModelParameters::new(
        "minicpm-v".to_string(), 
        Some(0.7), 
        None
    );

    // Vectorize image
    vectorize_image_concurrently(
        prompts,
        &mut vector, 
        client,
        model_parameters
    ).await?;

    // Print vectorized result
    println!("Vector: {:?}", vector.get_vector());
    println!("Vector Length: {:?}", vector.get_vector().len());

    Ok(())
}