Expand description
§OxiRS Embed: Advanced Knowledge Graph Embeddings
Status: Alpha Release (v0.1.0-alpha.2) ⚠️ APIs may change. Not recommended for production use.
State-of-the-art knowledge graph embedding methods including TransE, DistMult, ComplEx, and RotatE models, enhanced with biomedical AI, GPU acceleration, and specialized text processing.
§Key Features
§🧬 Biomedical AI
- Specialized biomedical knowledge graph embeddings
- Gene-disease association prediction
- Drug-target interaction modeling
- Pathway analysis and protein interactions
- Domain-specific text embeddings (SciBERT, BioBERT, etc.)
§🚀 GPU Acceleration
- Advanced GPU memory pooling and management
- Intelligent tensor caching
- Mixed precision training and inference
- Multi-stream parallel processing
- Pipeline parallelism for large-scale training
§🤖 Advanced Models
- Traditional KG embeddings (TransE, DistMult, ComplEx, RotatE, etc.)
- Graph Neural Networks (GCN, GraphSAGE, GAT)
- Transformer-based embeddings with fine-tuning
- Ontology-aware embeddings with reasoning
§📊 Production-Ready
- Comprehensive evaluation and benchmarking
- Model registry and version management
- Intelligent caching and optimization
- API server for deployment
§Quick Start
use oxirs_embed::{TransE, ModelConfig, Triple, NamedNode, EmbeddingModel};
// Create a knowledge graph embedding model
let config = ModelConfig::default().with_dimensions(128);
let mut model = TransE::new(config);
// Add knowledge triples
let triple = Triple::new(
NamedNode::new("http://example.org/alice")?,
NamedNode::new("http://example.org/knows")?,
NamedNode::new("http://example.org/bob")?,
);
model.add_triple(triple)?;
// Train the model
let stats = model.train(Some(100)).await?;
println!("Training completed: {stats:?}");§Biomedical Example
use oxirs_embed::{BiomedicalEmbedding, BiomedicalEmbeddingConfig, EmbeddingModel};
// Create biomedical embedding model
let config = BiomedicalEmbeddingConfig::default();
let mut model = BiomedicalEmbedding::new(config);
// Add biomedical knowledge
model.add_gene_disease_association("BRCA1", "breast_cancer", 0.95);
model.add_drug_target_interaction("aspirin", "COX1", 0.92);
// Train and predict
model.train(Some(100)).await?;
let predictions = model.predict_gene_disease_associations("BRCA1", 5)?;§GPU Acceleration Example
use oxirs_embed::{GpuAccelerationConfig, GpuAccelerationManager};
// Configure GPU acceleration
let config = GpuAccelerationConfig {
enabled: true,
mixed_precision: true,
tensor_caching: true,
multi_stream: true,
num_streams: 4,
..Default::default()
};
let mut gpu_manager = GpuAccelerationManager::new(config);
// Use accelerated embedding generation
let entities = vec!["entity1".to_string(), "entity2".to_string()];
let embeddings = gpu_manager.accelerated_embedding_generation(
entities,
|entity| { /* compute embedding */ vec![0.0; 128].into() }
).await?;§Examples
See the examples/ directory for comprehensive demonstrations:
biomedical_embedding_demo.rs- Biomedical AI capabilitiesgpu_acceleration_demo.rs- GPU acceleration featuresintegrated_ai_platform_demo.rs- Complete AI platform showcase
Re-exports§
pub use adaptive_learning::AdaptationMetrics;pub use adaptive_learning::AdaptationStrategy;pub use adaptive_learning::AdaptiveLearningConfig;pub use adaptive_learning::AdaptiveLearningSystem;pub use adaptive_learning::QualityFeedback;pub use acceleration::AdaptiveEmbeddingAccelerator;pub use acceleration::GpuEmbeddingAccelerator;pub use batch_processing::BatchJob;pub use batch_processing::BatchProcessingConfig;pub use batch_processing::BatchProcessingManager;pub use batch_processing::BatchProcessingResult;pub use batch_processing::BatchProcessingStats;pub use batch_processing::IncrementalConfig;pub use batch_processing::JobProgress;pub use batch_processing::JobStatus;pub use batch_processing::OutputFormat;pub use batch_processing::PartitioningStrategy;pub use batch_processing::RetryConfig;pub use biomedical_embeddings::BiomedicalEmbedding;pub use biomedical_embeddings::BiomedicalEmbeddingConfig;pub use biomedical_embeddings::BiomedicalEntityType;pub use biomedical_embeddings::BiomedicalRelationType;pub use biomedical_embeddings::FineTuningConfig;pub use biomedical_embeddings::PreprocessingRule;pub use biomedical_embeddings::SpecializedTextConfig;pub use biomedical_embeddings::SpecializedTextEmbedding;pub use biomedical_embeddings::SpecializedTextModel;pub use caching::CacheConfig;pub use caching::CacheManager;pub use caching::CachedEmbeddingModel;pub use causal_representation_learning::CausalDiscoveryAlgorithm;pub use causal_representation_learning::CausalDiscoveryConfig;pub use causal_representation_learning::CausalGraph;pub use causal_representation_learning::CausalRepresentationConfig;pub use causal_representation_learning::CausalRepresentationModel;pub use causal_representation_learning::ConstraintSettings;pub use causal_representation_learning::CounterfactualConfig;pub use causal_representation_learning::CounterfactualQuery;pub use causal_representation_learning::DisentanglementConfig;pub use causal_representation_learning::DisentanglementMethod;pub use causal_representation_learning::ExplanationType;pub use causal_representation_learning::IndependenceTest;pub use causal_representation_learning::InterventionConfig;pub use causal_representation_learning::ScoreSettings;pub use causal_representation_learning::StructuralCausalModelConfig;pub use cloud_integration::AWSSageMakerService;pub use cloud_integration::AutoScalingConfig;pub use cloud_integration::AzureMLService;pub use cloud_integration::BackupConfig;pub use cloud_integration::CloudIntegrationConfig;pub use cloud_integration::CloudIntegrationManager;pub use cloud_integration::CloudProvider;pub use cloud_integration::CloudService;pub use cloud_integration::ClusterStatus;pub use cloud_integration::CostEstimate;pub use cloud_integration::CostOptimizationResult;pub use cloud_integration::CostOptimizationStrategy;pub use cloud_integration::DeploymentConfig;pub use cloud_integration::DeploymentResult;pub use cloud_integration::DeploymentStatus;pub use cloud_integration::EndpointInfo;pub use cloud_integration::FunctionInvocationResult;pub use cloud_integration::GPUClusterConfig;pub use cloud_integration::GPUClusterResult;pub use cloud_integration::LifecyclePolicy;pub use cloud_integration::OptimizationAction;pub use cloud_integration::PerformanceTier;pub use cloud_integration::ReplicationType;pub use cloud_integration::ServerlessDeploymentResult;pub use cloud_integration::ServerlessFunctionConfig;pub use cloud_integration::ServerlessStatus;pub use cloud_integration::StorageConfig;pub use cloud_integration::StorageResult;pub use cloud_integration::StorageStatus;pub use cloud_integration::StorageType;pub use compression::CompressedModel;pub use compression::CompressionStats;pub use compression::CompressionTarget;pub use compression::DistillationConfig;pub use compression::ModelCompressionManager;pub use compression::NASConfig;pub use compression::OptimizationTarget;pub use compression::PruningConfig;pub use compression::PruningMethod;pub use compression::QuantizationConfig;pub use compression::QuantizationMethod;pub use consciousness_aware_embeddings::AttentionMechanism;pub use consciousness_aware_embeddings::ConsciousnessAwareEmbedding;pub use consciousness_aware_embeddings::ConsciousnessInsights;pub use consciousness_aware_embeddings::ConsciousnessLevel;pub use consciousness_aware_embeddings::MetaCognition;pub use consciousness_aware_embeddings::WorkingMemory;pub use continual_learning::ArchitectureConfig;pub use continual_learning::BoundaryDetection;pub use continual_learning::ConsolidationConfig;pub use continual_learning::ContinualLearningConfig;pub use continual_learning::ContinualLearningModel;pub use continual_learning::MemoryConfig;pub use continual_learning::MemoryType;pub use continual_learning::MemoryUpdateStrategy;pub use continual_learning::RegularizationConfig;pub use continual_learning::ReplayConfig;pub use continual_learning::ReplayMethod;pub use continual_learning::TaskConfig;pub use continual_learning::TaskDetection;pub use continual_learning::TaskSwitching;pub use cross_module_performance::CoordinatorConfig;pub use cross_module_performance::CrossModulePerformanceCoordinator;pub use cross_module_performance::GlobalPerformanceMetrics;pub use cross_module_performance::ModuleMetrics;pub use cross_module_performance::ModulePerformanceMonitor;pub use cross_module_performance::OptimizationCache;pub use cross_module_performance::PerformanceSnapshot;pub use cross_module_performance::PredictivePerformanceEngine;pub use cross_module_performance::ResourceAllocator;pub use cross_module_performance::ResourceTracker;pub use delta::ChangeRecord;pub use delta::ChangeStatistics;pub use delta::ChangeType;pub use delta::DeltaConfig;pub use delta::DeltaManager;pub use delta::DeltaResult;pub use delta::DeltaStats;pub use delta::IncrementalStrategy;pub use enterprise_knowledge::BehaviorMetrics;pub use enterprise_knowledge::CareerPredictions;pub use enterprise_knowledge::Category;pub use enterprise_knowledge::CategoryHierarchy;pub use enterprise_knowledge::CategoryPerformance;pub use enterprise_knowledge::ColdStartStrategy;pub use enterprise_knowledge::CommunicationFrequency;pub use enterprise_knowledge::CommunicationPreferences;pub use enterprise_knowledge::CustomerEmbedding;pub use enterprise_knowledge::CustomerPreferences;pub use enterprise_knowledge::CustomerRatings;pub use enterprise_knowledge::CustomerSegment;pub use enterprise_knowledge::Department;pub use enterprise_knowledge::DepartmentPerformance;pub use enterprise_knowledge::EmployeeEmbedding;pub use enterprise_knowledge::EnterpriseConfig;pub use enterprise_knowledge::EnterpriseKnowledgeAnalyzer;pub use enterprise_knowledge::EnterpriseMetrics;pub use enterprise_knowledge::ExperienceLevel;pub use enterprise_knowledge::FeatureType;pub use enterprise_knowledge::MarketAnalysis;pub use enterprise_knowledge::OrganizationalStructure;pub use enterprise_knowledge::PerformanceMetrics as EnterprisePerformanceMetrics;pub use enterprise_knowledge::ProductAvailability;pub use enterprise_knowledge::ProductEmbedding;pub use enterprise_knowledge::ProductFeature;pub use enterprise_knowledge::ProductRecommendation;pub use enterprise_knowledge::Project;pub use enterprise_knowledge::ProjectOutcome;pub use enterprise_knowledge::ProjectParticipation;pub use enterprise_knowledge::ProjectPerformance;pub use enterprise_knowledge::ProjectStatus;pub use enterprise_knowledge::Purchase;pub use enterprise_knowledge::PurchaseChannel;pub use enterprise_knowledge::RecommendationConfig;pub use enterprise_knowledge::RecommendationEngine;pub use enterprise_knowledge::RecommendationEngineType;pub use enterprise_knowledge::RecommendationPerformance;pub use enterprise_knowledge::RecommendationReason;pub use enterprise_knowledge::SalesMetrics;pub use enterprise_knowledge::Skill;pub use enterprise_knowledge::SkillCategory;pub use enterprise_knowledge::Team;pub use enterprise_knowledge::TeamPerformance;pub use evaluation::QueryAnsweringEvaluator;pub use evaluation::QueryEvaluationConfig;pub use evaluation::QueryEvaluationResults;pub use evaluation::QueryMetric;pub use evaluation::QueryResult;pub use evaluation::QueryTemplate;pub use evaluation::QueryType;pub use evaluation::ReasoningChain;pub use evaluation::ReasoningEvaluationConfig;pub use evaluation::ReasoningEvaluationResults;pub use evaluation::ReasoningRule;pub use evaluation::ReasoningStep;pub use evaluation::ReasoningTaskEvaluator;pub use evaluation::ReasoningType;pub use evaluation::TypeSpecificResults;pub use federated_learning::AggregationEngine;pub use federated_learning::AggregationStrategy;pub use federated_learning::AuthenticationConfig;pub use federated_learning::AuthenticationMethod;pub use federated_learning::CertificateConfig;pub use federated_learning::ClippingMechanisms;pub use federated_learning::ClippingMethod;pub use federated_learning::CommunicationConfig;pub use federated_learning::CommunicationManager;pub use federated_learning::CommunicationProtocol;pub use federated_learning::CompressionAlgorithm;pub use federated_learning::CompressionConfig;pub use federated_learning::CompressionEngine;pub use federated_learning::ConvergenceMetrics;pub use federated_learning::ConvergenceStatus;pub use federated_learning::DataSelectionStrategy;pub use federated_learning::DataStatistics;pub use federated_learning::EncryptionScheme;pub use federated_learning::FederatedConfig;pub use federated_learning::FederatedCoordinator;pub use federated_learning::FederatedEmbeddingModel;pub use federated_learning::FederatedMessage;pub use federated_learning::FederatedRound;pub use federated_learning::FederationStats;pub use federated_learning::GlobalModelState;pub use federated_learning::HardwareAccelerator;pub use federated_learning::KeyManager;pub use federated_learning::LocalModelState;pub use federated_learning::LocalTrainingStats;pub use federated_learning::LocalUpdate;pub use federated_learning::MetaLearningConfig;pub use federated_learning::NoiseGenerator;pub use federated_learning::NoiseMechanism;pub use federated_learning::OutlierAction;pub use federated_learning::OutlierDetection;pub use federated_learning::OutlierDetectionMethod;pub use federated_learning::Participant;pub use federated_learning::ParticipantCapabilities;pub use federated_learning::ParticipantStatus;pub use federated_learning::PersonalizationConfig;pub use federated_learning::PersonalizationStrategy;pub use federated_learning::PrivacyAccountant;pub use federated_learning::PrivacyConfig;pub use federated_learning::PrivacyEngine;pub use federated_learning::PrivacyMetrics;pub use federated_learning::PrivacyParams;pub use federated_learning::RoundMetrics;pub use federated_learning::RoundStatus;pub use federated_learning::SecurityConfig;pub use federated_learning::SecurityFeature;pub use federated_learning::SecurityManager;pub use federated_learning::TrainingConfig;pub use federated_learning::VerificationEngine;pub use federated_learning::VerificationMechanism;pub use federated_learning::VerificationResult;pub use federated_learning::WeightingScheme;pub use gpu_acceleration::GpuAccelerationConfig;pub use gpu_acceleration::GpuAccelerationManager;pub use gpu_acceleration::GpuMemoryPool;pub use gpu_acceleration::GpuPerformanceStats;pub use gpu_acceleration::MixedPrecisionProcessor;pub use gpu_acceleration::MultiStreamProcessor;pub use gpu_acceleration::TensorCache;pub use graphql_api::create_schema;pub use graphql_api::BatchEmbeddingInput;pub use graphql_api::BatchEmbeddingResult;pub use graphql_api::BatchStatus;pub use graphql_api::DistanceMetric;pub use graphql_api::EmbeddingFormat;pub use graphql_api::EmbeddingQueryInput;pub use graphql_api::EmbeddingResult;pub use graphql_api::EmbeddingSchema;pub use graphql_api::GraphQLContext;pub use graphql_api::ModelInfo;pub use graphql_api::ModelType;pub use graphql_api::SimilarityResult;pub use graphql_api::SimilaritySearchInput;pub use models::AggregationType;pub use models::ComplEx;pub use models::DistMult;pub use models::GNNConfig;pub use models::GNNEmbedding;pub use models::GNNType;pub use models::PoolingStrategy;pub use models::RotatE;pub use models::TransE;pub use models::TransformerConfig;pub use models::TransformerEmbedding;pub use models::TransformerType;pub use monitoring::Alert;pub use monitoring::AlertSeverity;pub use monitoring::AlertThresholds;pub use monitoring::AlertType;pub use monitoring::CacheMetrics;pub use monitoring::ConsoleAlertHandler;pub use monitoring::DriftMetrics;pub use monitoring::ErrorEvent;pub use monitoring::ErrorMetrics;pub use monitoring::ErrorSeverity;pub use monitoring::LatencyMetrics;pub use monitoring::MonitoringConfig;pub use monitoring::PerformanceMetrics as MonitoringPerformanceMetrics;pub use monitoring::PerformanceMonitor;pub use monitoring::QualityAssessment;pub use monitoring::QualityMetrics;pub use monitoring::ResourceMetrics;pub use monitoring::SlackAlertHandler;pub use monitoring::ThroughputMetrics;pub use multimodal::AlignmentNetwork;pub use multimodal::AlignmentObjective;pub use multimodal::ContrastiveConfig;pub use multimodal::CrossDomainConfig;pub use multimodal::CrossModalConfig;pub use multimodal::KGEncoder;pub use multimodal::MultiModalEmbedding;pub use multimodal::MultiModalStats;pub use multimodal::TextEncoder;pub use neural_symbolic_integration::ConstraintSatisfactionConfig;pub use neural_symbolic_integration::ConstraintType;pub use neural_symbolic_integration::KnowledgeIntegrationConfig;pub use neural_symbolic_integration::KnowledgeRule;pub use neural_symbolic_integration::LogicIntegrationConfig;pub use neural_symbolic_integration::LogicProgrammingConfig;pub use neural_symbolic_integration::LogicalFormula;pub use neural_symbolic_integration::NeuralSymbolicConfig;pub use neural_symbolic_integration::NeuralSymbolicModel;pub use neural_symbolic_integration::NeuroSymbolicArchitectureConfig;pub use neural_symbolic_integration::OntologicalConfig;pub use neural_symbolic_integration::ReasoningEngine;pub use neural_symbolic_integration::RuleBasedConfig;pub use neural_symbolic_integration::SymbolicReasoningConfig;pub use novel_architectures::ActivationType;pub use novel_architectures::ArchitectureParams;pub use novel_architectures::ArchitectureState;pub use novel_architectures::ArchitectureType;pub use novel_architectures::CurvatureComputation;pub use novel_architectures::CurvatureMethod;pub use novel_architectures::CurvatureType;pub use novel_architectures::DynamicsConfig;pub use novel_architectures::EntanglementStructure;pub use novel_architectures::EquivarianceGroup;pub use novel_architectures::FlowType;pub use novel_architectures::GeometricConfig;pub use novel_architectures::GeometricParams;pub use novel_architectures::GeometricSpace;pub use novel_architectures::GeometricState;pub use novel_architectures::GraphTransformerParams;pub use novel_architectures::GraphTransformerState;pub use novel_architectures::HyperbolicDistance;pub use novel_architectures::HyperbolicInit;pub use novel_architectures::HyperbolicManifold;pub use novel_architectures::HyperbolicParams;pub use novel_architectures::HyperbolicState;pub use novel_architectures::IntegrationScheme;pub use novel_architectures::IntegrationStats;pub use novel_architectures::ManifoldLearning;pub use novel_architectures::ManifoldMethod;pub use novel_architectures::ManifoldOptimizer;pub use novel_architectures::NeuralODEParams;pub use novel_architectures::NeuralODEState;pub use novel_architectures::NovelArchitectureConfig;pub use novel_architectures::NovelArchitectureModel;pub use novel_architectures::ODERegularization;pub use novel_architectures::ODESolverType;pub use novel_architectures::ParallelTransport;pub use novel_architectures::QuantumGateSet;pub use novel_architectures::QuantumMeasurement;pub use novel_architectures::QuantumNoise;pub use novel_architectures::QuantumParams;pub use novel_architectures::QuantumState;pub use novel_architectures::StabilityConstraints;pub use novel_architectures::StructuralBias;pub use novel_architectures::TimeEvolution;pub use novel_architectures::TransportMethod;pub use quantum_circuits::Complex;pub use quantum_circuits::MeasurementStrategy;pub use quantum_circuits::QNNLayerType;pub use quantum_circuits::QuantumApproximateOptimization;pub use quantum_circuits::QuantumCircuit;pub use quantum_circuits::QuantumGate;pub use quantum_circuits::QuantumNeuralNetwork;pub use quantum_circuits::QuantumNeuralNetworkLayer;pub use quantum_circuits::QuantumSimulator;pub use quantum_circuits::VariationalQuantumEigensolver;pub use research_networks::AuthorEmbedding;pub use research_networks::Citation;pub use research_networks::CitationNetwork;pub use research_networks::CitationType;pub use research_networks::Collaboration;pub use research_networks::CollaborationNetwork;pub use research_networks::NetworkMetrics;pub use research_networks::PaperSection;pub use research_networks::PublicationEmbedding;pub use research_networks::PublicationType;pub use research_networks::ResearchCommunity;pub use research_networks::ResearchNetworkAnalyzer;pub use research_networks::ResearchNetworkConfig;pub use research_networks::TopicModel;pub use research_networks::TopicModelingConfig;pub use vision_language_graph::AggregationFunction;pub use vision_language_graph::CNNConfig;pub use vision_language_graph::CrossAttentionConfig;pub use vision_language_graph::DomainAdaptationConfig;pub use vision_language_graph::DomainAdaptationMethod;pub use vision_language_graph::EpisodeConfig;pub use vision_language_graph::FewShotConfig;pub use vision_language_graph::FewShotMethod;pub use vision_language_graph::FusionStrategy;pub use vision_language_graph::GraphArchitecture;pub use vision_language_graph::GraphEncoder;pub use vision_language_graph::GraphEncoderConfig;pub use vision_language_graph::JointTrainingConfig;pub use vision_language_graph::LanguageArchitecture;pub use vision_language_graph::LanguageEncoder;pub use vision_language_graph::LanguageEncoderConfig;pub use vision_language_graph::LanguageTransformerConfig;pub use vision_language_graph::MetaLearner;pub use vision_language_graph::ModalityEncoding;pub use vision_language_graph::MultiModalTransformer;pub use vision_language_graph::MultiModalTransformerConfig;pub use vision_language_graph::NormalizationType;pub use vision_language_graph::PoolingType;pub use vision_language_graph::PositionEncodingType;pub use vision_language_graph::ReadoutFunction;pub use vision_language_graph::TaskCategory;pub use vision_language_graph::TaskSpecificParams;pub use vision_language_graph::TrainingObjective;pub use vision_language_graph::TransferLearningConfig;pub use vision_language_graph::TransferStrategy;pub use vision_language_graph::ViTConfig;pub use vision_language_graph::VisionArchitecture;pub use vision_language_graph::VisionEncoder;pub use vision_language_graph::VisionEncoderConfig;pub use vision_language_graph::VisionLanguageGraphConfig;pub use vision_language_graph::VisionLanguageGraphModel;pub use vision_language_graph::VisionLanguageGraphStats;pub use vision_language_graph::ZeroShotConfig;pub use vision_language_graph::ZeroShotMethod;pub use crate::model_registry::ModelRegistry;pub use crate::model_registry::ModelVersion;pub use crate::model_registry::ResourceAllocation as ModelResourceAllocation;
Modules§
- acceleration
- Hardware acceleration modules for embedding computations
- adaptive_
learning - Adaptive Learning System for Real-Time Embedding Enhancement
- advanced_
profiler - Advanced Performance Profiler
- application_
tasks - Application-specific evaluation tasks for embedding models
- batch_
processing - Offline batch embedding generation with incremental updates
- biological_
computing - Biological Computing for Knowledge Graph Embeddings
- biomedical_
embeddings - Biomedical knowledge graph embeddings for scientific applications
- caching
- Advanced caching and precomputation system for embedding models
- causal_
representation_ learning - Causal Representation Learning
- cloud_
integration - Cloud provider integration for embedding services
- compression
- Model compression and quantization for efficient embedding deployment
- consciousness_
aware_ embeddings - Consciousness-Aware Embedding System
- continual_
learning - Continual Learning Capabilities
- cross_
domain_ transfer - Cross-domain transfer learning for embedding models
- cross_
module_ performance - Cross-Module Performance Coordinator
- delta
- Delta computation and incremental update system for embeddings
- diffusion_
embeddings - Diffusion Model-Based Knowledge Graph Embeddings
- enterprise_
knowledge - Enterprise Knowledge Graphs - Business Domain Embeddings
- evaluation
- Evaluation module for embeddings and knowledge graphs
- federated_
learning - Federated learning module with organized sub-modules
- gpu_
acceleration - GPU acceleration and optimization features for embedding models
- graphql_
api - Advanced GraphQL API for embedding queries and management
- inference
- High-performance inference engine for embedding models
- integration
- Integration utilities with other OxiRS components
- mamba_
attention - Mamba and State Space Model Attention Mechanisms
- model_
registry - Model Registry and Versioning System
- models
- Embedding model implementations
- monitoring
- Comprehensive monitoring and metrics system for embedding models
- multimodal
- Multi-modal embeddings and cross-modal alignment for unified representation learning
- neural_
symbolic_ integration - Neural-Symbolic Integration
- neuro_
evolution - Neuro-Evolution for Automated Neural Architecture Search
- novel_
architectures - Novel architectures for cutting-edge embedding techniques
- persistence
- Model persistence and serialization utilities
- quantum_
circuits - Advanced Quantum Circuit Implementations for Quantum-Inspired Embeddings
- quick_
start - Convenience functions for quick setup and common operations
- real_
time_ fine_ tuning - Real-time Fine-tuning System
- real_
time_ optimization - Real-time optimization system for knowledge graph embeddings
- research_
networks - Research Publication Networks - Academic Knowledge Graph Embeddings
- training
- Training utilities and advanced optimizers for embedding models
- utils
- Utility functions and helpers for embedding operations
- vision_
language_ graph - Vision-Language-Graph Multi-Modal Integration
Structs§
- Model
Config - Configuration for embedding models
- Model
Stats - Model statistics
- Named
Node - Named node for RDF resources
- Training
Stats - Training statistics
- Triple
- Triple structure for RDF triples
- VecVector
- Multi-precision vector with enhanced functionality
- Vector
- Compatibility wrapper for Vector that provides the old interface while using the sophisticated oxirs-vec Vector internally
Enums§
- Embedding
Error - Embedding errors
Traits§
- Embedding
Model - Basic embedding model trait