Struct ContextualEmbeddingModel 

pub struct ContextualEmbeddingModel { /* private fields */ }

Main contextual embedding model

Implementations

impl ContextualEmbeddingModel

pub fn new(config: ContextualConfig) -> Result<Self>

Create a new contextual embedding model

pub async fn embed_with_context( &mut self, triples: &[Triple], context: &EmbeddingContext, ) -> Result<Vec<Vector>>

Generate contextual embeddings for triples with context

pub fn get_stats(&self) -> &ModelStats

Get model statistics

Trait Implementations

impl EmbeddingModel for ContextualEmbeddingModel

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