Struct VisionLanguageGraphModel 

pub struct VisionLanguageGraphModel {

    pub config: VisionLanguageGraphConfig,
    pub model_id: Uuid,
    pub vision_encoder: VisionEncoder,
    pub language_encoder: LanguageEncoder,
    pub graph_encoder: GraphEncoder,
    pub multimodal_transformer: MultiModalTransformer,
    pub meta_learner: MetaLearner,
    pub vision_embeddings: HashMap<String, Array1<f32>>,
    pub language_embeddings: HashMap<String, Array1<f32>>,
    pub graph_embeddings: HashMap<String, Array1<f32>>,
    pub unified_embeddings: HashMap<String, Array1<f32>>,
    pub training_stats: Option<TrainingStats>,
    pub is_trained: bool,
}

Vision-Language-Graph embedding model

Fields

config: VisionLanguageGraphConfig

model_id: Uuid

vision_encoder: VisionEncoder

Vision encoder

language_encoder: LanguageEncoder

Language encoder

graph_encoder: GraphEncoder

Graph encoder

multimodal_transformer: MultiModalTransformer

Multi-modal transformer

meta_learner: MetaLearner

Meta-learner for adaptation

vision_embeddings: HashMap<String, Array1<f32>>

Cached embeddings

language_embeddings: HashMap<String, Array1<f32>>

graph_embeddings: HashMap<String, Array1<f32>>

unified_embeddings: HashMap<String, Array1<f32>>

training_stats: Option<TrainingStats>

Training state

is_trained: bool

Implementations

impl VisionLanguageGraphModel

Vision encoder

pub fn new(config: VisionLanguageGraphConfig) -> Self

Create new vision-language-graph model

pub async fn generate_unified_embedding( &mut self, image: Option<&Array3<f32>>, text: Option<&str>, graph_data: Option<(&Array2<f32>, &Array2<f32>, &Array2<f32>)>, ) -> Result<Array1<f32>>

Generate unified multi-modal embedding

pub fn zero_shot_predict( &self, query_embedding: &Array1<f32>, class_prototypes: &HashMap<String, Array1<f32>>, ) -> Result<String>

Zero-shot prediction

pub fn few_shot_adapt( &mut self, support_examples: &[(Array1<f32>, String)], query_examples: &[Array1<f32>], ) -> Result<Vec<String>>

Few-shot adaptation

Trait Implementations

impl Debug for VisionLanguageGraphModel

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

Formats the value using the given formatter.

impl EmbeddingModel for VisionLanguageGraphModel

fn config(&self) -> &ModelConfig

fn model_id(&self) -> &Uuid

fn model_type(&self) -> &'static str

fn add_triple(&mut self, _triple: Triple) -> Result<()>

fn train<'life0, 'async_trait>( &'life0 mut self, epochs: Option<usize>, ) -> Pin<Box<dyn Future<Output = Result<TrainingStats>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait,

fn get_entity_embedding(&self, entity: &str) -> Result<Vector>

fn get_relation_embedding(&self, relation: &str) -> Result<Vector>

fn score_triple( &self, subject: &str, predicate: &str, object: &str, ) -> Result<f64>

fn predict_objects( &self, subject: &str, predicate: &str, k: usize, ) -> Result<Vec<(String, f64)>>

fn predict_subjects( &self, predicate: &str, object: &str, k: usize, ) -> Result<Vec<(String, f64)>>

fn predict_relations( &self, subject: &str, object: &str, k: usize, ) -> Result<Vec<(String, f64)>>

fn get_entities(&self) -> Vec<String>

fn get_relations(&self) -> Vec<String>

fn get_stats(&self) -> ModelStats

fn save(&self, _path: &str) -> Result<()>

fn load(&mut self, _path: &str) -> Result<()>

fn clear(&mut self)

fn is_trained(&self) -> bool

fn encode<'life0, 'life1, 'async_trait>( &'life0 self, texts: &'life1 [String], ) -> Pin<Box<dyn Future<Output = Result<Vec<Vec<f32>>>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Encode text strings into embeddings