Module advanced_features 

Advanced Features for Ruvector

This module provides advanced vector database capabilities:

• Enhanced Product Quantization with precomputed lookup tables
• Filtered Search with automatic strategy selection
• MMR (Maximal Marginal Relevance) for diversity
• Hybrid Search combining vector and keyword matching
• Conformal Prediction for uncertainty quantification
• Multi-Vector Retrieval (ColBERT-style late interaction)
• Matryoshka Representation Learning (adaptive-dimension search)
• Optimized Product Quantization (OPQ) with learned rotation matrix

Re-exports

pub use graph_rag::CommunityDetection;

pub use graph_rag::Community;

pub use graph_rag::Entity;

pub use graph_rag::GraphRAGConfig;

pub use graph_rag::GraphRAGPipeline;

pub use graph_rag::KnowledgeGraph;

pub use graph_rag::Relation;

pub use graph_rag::RetrievalResult;

pub use conformal_prediction::ConformalConfig;

pub use conformal_prediction::ConformalPredictor;

pub use conformal_prediction::NonconformityMeasure;

pub use conformal_prediction::PredictionSet;

pub use filtered_search::FilterExpression;

pub use filtered_search::FilterStrategy;

pub use filtered_search::FilteredSearch;

pub use hybrid_search::HybridConfig;

pub use hybrid_search::HybridSearch;

pub use hybrid_search::NormalizationStrategy;

pub use hybrid_search::BM25;

pub use matryoshka::FunnelConfig;

pub use matryoshka::MatryoshkaConfig;

pub use matryoshka::MatryoshkaIndex;

pub use mmr::MMRConfig;

pub use mmr::MMRSearch;

pub use multi_vector::MultiVectorConfig;

pub use multi_vector::MultiVectorIndex;

pub use multi_vector::ScoringVariant;

pub use opq::OPQConfig;

pub use opq::OPQIndex;

pub use opq::RotationMatrix;

pub use product_quantization::EnhancedPQ;

pub use product_quantization::LookupTable;

pub use product_quantization::PQConfig;

pub use sparse_vector::FusionConfig;

pub use sparse_vector::FusionStrategy;

pub use sparse_vector::ScoredDoc;

pub use sparse_vector::SparseIndex;

pub use sparse_vector::SparseVector;

pub use sparse_vector::fuse_rankings;

pub use diskann::DiskIndex;

pub use diskann::DiskNode;

pub use diskann::IOStats;

pub use diskann::MedoidFinder;

pub use diskann::PageCache;

pub use diskann::VamanaConfig;

pub use diskann::VamanaGraph;

pub use compaction::BloomFilter;

pub use compaction::CompactionConfig;

pub use compaction::LSMIndex;

pub use compaction::LSMStats;

pub use compaction::MemTable;

pub use compaction::Segment;

Modules

compaction

LSM-Tree Style Streaming Index Compaction

conformal_prediction

Conformal Prediction for Uncertainty Quantification

diskann

DiskANN / Vamana SSD-Backed Approximate Nearest Neighbor Index

filtered_search

Filtered Search with Automatic Strategy Selection

graph_rag

Graph RAG Pipeline

hybrid_search

Hybrid Search: Combining Vector Similarity and Keyword Matching

matryoshka

Matryoshka Representation Learning Support

mmr

Maximal Marginal Relevance (MMR) for Diversity-Aware Search

multi_vector

ColBERT-style Multi-Vector Retrieval

opq

Optimized Product Quantization (OPQ) with learned rotation matrix.

product_quantization

Enhanced Product Quantization with Precomputed Lookup Tables

sparse_vector

Sparse Vector Index with Reciprocal Rank Fusion (RRF)