Crate ruvector_core 

Ruvector Core

High-performance Rust-native vector database with HNSW indexing and SIMD-optimized operations.

Working Features (Tested & Benchmarked)

• HNSW Indexing: Approximate nearest neighbor search with O(log n) complexity
• SIMD Distance: SimSIMD-powered distance calculations (~16M ops/sec for 512-dim)
• Quantization: Scalar (4x) and binary (32x) compression with distance support
• Persistence: REDB-based storage with config persistence
• Search: ~2.5K queries/sec on 10K vectors (benchmarked)

⚠️ Experimental/Incomplete Features - READ BEFORE USE

• AgenticDB: ⚠️⚠️⚠️ CRITICAL WARNING ⚠️⚠️⚠️
  • Uses PLACEHOLDER hash-based embeddings, NOT real semantic embeddings
  • “dog” and “cat” will NOT be similar (different characters)
  • “dog” and “god” WILL be similar (same characters) - This is wrong!
  • MUST integrate real embedding model for production (ONNX, Candle, or API)
  • See agenticdb module docs and /examples/onnx-embeddings for integration
• Advanced Features: Conformal prediction, hybrid search - functional but less tested

What This Is NOT

• This is NOT a complete RAG solution - you need external embedding models
• Examples use mock embeddings for demonstration only

Re-exports

pub use advanced_features::ConformalConfig;

pub use advanced_features::ConformalPredictor;

pub use advanced_features::EnhancedPQ;

pub use advanced_features::FilterExpression;

pub use advanced_features::FilterStrategy;

pub use advanced_features::FilteredSearch;

pub use advanced_features::HybridConfig;

pub use advanced_features::HybridSearch;

pub use advanced_features::MMRConfig;

pub use advanced_features::MMRSearch;

pub use advanced_features::PQConfig;

pub use advanced_features::PredictionSet;

pub use advanced_features::BM25;

pub use agenticdb::AgenticDB;

pub use embeddings::EmbeddingProvider;

pub use embeddings::HashEmbedding;

pub use embeddings::ApiEmbedding;

pub use embeddings::BoxedEmbeddingProvider;

pub use error::Result;

pub use error::RuvectorError;

pub use types::DistanceMetric;

pub use types::SearchQuery;

pub use types::SearchResult;

pub use types::VectorEntry;

pub use types::VectorId;

pub use vector_db::VectorDB;

Modules

advanced

Advanced techniques: hypergraphs, learned indexes, neural hashing, TDA (Phase 6)

advanced_features

Advanced Features for Ruvector

agenticdb

AgenticDB API Compatibility Layer

arena

Arena allocator for batch operations

cache_optimized

Cache-optimized data structures using Structure-of-Arrays (SoA) layout

distance

SIMD-optimized distance metrics using SimSIMD

embeddings

Text Embedding Providers

error

Error types for Ruvector

index

Index structures for efficient vector search

lockfree

Lock-free data structures for high-concurrency operations

quantization

Quantization techniques for memory compression

simd_intrinsics

Custom SIMD intrinsics for performance-critical operations

storage

Storage layer with redb for metadata and memory-mapped vectors

types

Core types and data structures

vector_db

Main VectorDB interface