Struct Vector

pub struct Vector<T> { /* private fields */ }

A vector that contains a vectorized data and the original data. This struct pairs the original data with its vector representation and type information.

Implementations

impl<DynamicImage> Vector<DynamicImage>

pub fn from_image(data: DynamicImage) -> Self

Initialize a new vector from image data

Arguments

• data - The image data to be vectorized

Returns

A new Vector instance containing the image data

Examples found in repository

examples/vectorize_images.rs (line 22)

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(())
}

impl<String> Vector<String>

pub fn from_text(data: String) -> Self

Initialize a new vector from text data

Arguments

• data - The text data to be vectorized

Returns

A new Vector instance containing the text data

Examples found in repository

examples/vectorize_texts.rs (lines 13-15)

async fn main() -> Result<(), Error> {
    // Load text
    let test_text: String = "Hi, this is dim. I am here to vectorize whatever your want."
        .to_string();
    
    // Create a Vector object from the image
    let mut vector: Vector<String> = Vector::from_text(
        test_text
    );
    
    // Initialize client
    let client: async_openai::Client<async_openai::config::OpenAIConfig> = async_openai::Client::with_config(
        async_openai::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![
        "Score the sentiment intensity of the text from 1 (extremely negative) to 9 (extremely positive). Consider emotional language, tone, and context. Format your response exactly like this example: {'sentiment_score': 7}".to_string(),
        "Rate the formality of the text from 1 (highly informal, slang-heavy) to 9 (highly formal, academic/professional). Format your response exactly like this example: {'formality_score': 4}".to_string(),
        "Assess the emotional intensity of the text from 1 (neutral/clinical) to 9 (highly emotional, passionate, or provocative). Format your response exactly like this example: {'emotional_score': 8}".to_string(),
        "Score how subjective the text is from 1 (purely factual/objective) to 9 (heavily opinionated/subjective). Format your response exactly like this example: {'subjectivity_score': 6}".to_string(),
        "Rate the linguistic complexity of the text from 1 (simple vocabulary/short sentences) to 9 (dense jargon/long, intricate sentences). Format your response exactly like this example: {'complexity_score': 3}".to_string(),
        "Score the dominant intent: 1-3 (informative/educational), 4-6 (persuasive/argumentative), 7-9 (narrative/storytelling). Format your response exactly like this example: {'intent_score': 5}".to_string(),
        "Rate how urgent or time-sensitive the text feels from 1 (no urgency) to 9 (immediate action required). Format your response exactly like this example: {'urgency_score': 2}".to_string(),
        "Score the specificity of details from 1 (vague/abstract) to 9 (highly specific/concrete examples). Format your response exactly like this example: {'specificity_score': 7}".to_string(),
        "Rate the politeness of the tone from 1 (rude/confrontational) to 9 (extremely polite/deferential). Format your response exactly like this example: {'politeness_score': 8}".to_string(),
        "Categorize the text's primary domain: 1-3 (technical/scientific), 4-6 (casual/everyday), 7-9 (artistic/creative). Format your response exactly like this example: {'domain_score': 4}".to_string(),
    ];

    // Vectorize image
    let model_parameters = ModelParameters::new("minicpm-v".to_string(), None, None);
    vectorize_string_concurrently(
        prompts,
        &mut vector, 
        client,
        model_parameters
    ).await?;

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

Trait Implementations

impl<T: Clone> Clone for Vector<T>

fn clone(&self) -> Vector<T>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T: Debug> Debug for Vector<T>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de, T> Deserialize<'de> for Vector<T>

where T: Deserialize<'de>,

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<T> Serialize for Vector<T>

where T: Serialize,

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl<T> VectorOperations<T> for Vector<T>

fn get_vector(&self) -> Vec<f32>

Get the vector representation of the data

fn get_data_type(&self) -> DataType

Get the type of data stored

fn get_data(&self) -> &T

Get a reference to the original data

fn overwrite_vector(&mut self, vector: Vec<f32>)

Write a new vector to the vector field

fn get_dimensionality(&self) -> usize

Get dimensionality of the vector