Crate velesdb_core 

VelesDB Core

High-performance vector database engine written in Rust.

VelesDB is a local-first vector database designed for semantic search, recommendation systems, and RAG (Retrieval-Augmented Generation) applications.

Features

• Blazing Fast: HNSW index with explicit SIMD (4x faster)
• 5 Distance Metrics: Cosine, Euclidean, Dot Product, Hamming, Jaccard
• Hybrid Search: Vector + BM25 full-text with RRF fusion
• Quantization: SQ8 (4x) and Binary (32x) memory compression
• Persistent Storage: Memory-mapped files for efficient disk access

Quick Start

use velesdb_core::{Database, DistanceMetric, Point, StorageMode};
use serde_json::json;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create a new database
    let db = Database::open("./data")?;

    // Create a collection (all 5 metrics available)
    db.create_collection("documents", 768, DistanceMetric::Cosine)?;
    // Or with quantization: DistanceMetric::Hamming + StorageMode::Binary

    let collection = db.get_collection("documents").ok_or("Collection not found")?;

    // Insert vectors (upsert takes ownership)
    collection.upsert(vec![
        Point::new(1, vec![0.1; 768], Some(json!({"title": "Hello World"}))),
    ])?;

    // Search for similar vectors
    let query_vector = vec![0.1; 768];
    let results = collection.search(&query_vector, 10)?;

    // Hybrid search (vector + text)
    let hybrid = collection.hybrid_search(&query_vector, "hello", 5, Some(0.7))?;
}

Re-exports

pub use index::HnswIndex;

pub use index::HnswParams;

pub use index::SearchQuality;

pub use index::VectorIndex;

pub use collection::streaming::BackpressureError;

pub use collection::Collection;

pub use collection::CollectionType;

pub use collection::EdgeType;

pub use collection::GraphCollection;

pub use collection::GraphEdge;

pub use collection::GraphNode;

pub use collection::GraphSchema;

pub use collection::IndexInfo;

pub use collection::MetadataCollection;

pub use collection::NodeType;

pub use collection::TraversalResult;

pub use collection::ValueType;

pub use collection::VectorCollection;

pub use distance::DistanceMetric;

pub use error::Error;

pub use error::Result;

pub use filter::Condition;

pub use filter::Filter;

pub use point::Point;

pub use point::SearchResult;

pub use quantization::cosine_similarity_quantized;

pub use quantization::cosine_similarity_quantized_simd;

pub use quantization::dot_product_quantized;

pub use quantization::dot_product_quantized_simd;

pub use quantization::euclidean_squared_quantized;

pub use quantization::euclidean_squared_quantized_simd;

pub use quantization::BinaryQuantizedVector;

pub use quantization::QuantizedVector;

pub use quantization::StorageMode;

pub use column_store::BatchUpdate;

pub use column_store::BatchUpdateResult;

pub use column_store::BatchUpsertResult;

pub use column_store::ColumnStore;

pub use column_store::ColumnStoreError;

pub use column_store::ColumnType;

pub use column_store::ColumnValue;

pub use column_store::ExpireResult;

pub use column_store::StringId;

pub use column_store::StringTable;

pub use column_store::TypedColumn;

pub use column_store::UpsertResult;

pub use config::ConfigError;

pub use config::HnswConfig;

pub use config::LimitsConfig;

pub use config::QuantizationConfig;

pub use config::QuantizationType;

pub use config::SearchConfig;

pub use config::SearchMode;

pub use config::VelesConfig;

pub use config::LoggingConfig;

pub use config::ServerConfig;

pub use config::StorageConfig;

pub use fusion::FusionError;

pub use fusion::FusionStrategy;

pub use metrics::average_metrics;

pub use metrics::compute_latency_percentiles;

pub use metrics::hit_rate;

pub use metrics::mean_average_precision;

pub use metrics::mrr;

pub use metrics::ndcg_at_k;

pub use metrics::precision_at_k;

pub use metrics::recall_at_k;

pub use metrics::LatencyStats;

pub use observer::DatabaseObserver;

Modules

agent

Agent Memory Patterns SDK (EPIC-010)

alloc_guard

RAII guards for safe manual memory management.

cache

Caching layer for VelesDB (SOTA 2026).

collection

Collection management for VelesDB.

column_store

Column-oriented storage for high-performance metadata filtering.

compression

Column compression for VelesDB (SOTA 2026).

config

VelesDB Configuration Module

distance

Distance metrics for vector similarity calculations.

error

Error types for VelesDB.

filter

Metadata filtering for vector search.

fusion

Multi-query fusion strategies for VelesDB.

gpu

GPU-accelerated vector operations using wgpu (WebGPU).

guardrails

Production-grade query guard-rails (EPIC-048).

half_precision

Half-precision floating point support for memory-efficient vector storage.

index

Index implementations for efficient vector search.

metrics

Search quality metrics, operational monitoring, and query diagnostics.

observer

DatabaseObserver — extension hook for velesdb-premium.

perf_optimizations

Performance optimizations module for ultra-fast vector operations.

point

Point data structure representing a vector with metadata.

quantization

Scalar Quantization (SQ8) and Binary Quantization for memory-efficient vector storage.

simd_dispatch

Zero-overhead SIMD function dispatch.

simd_native

Native SIMD intrinsics for maximum performance.

sparse_index

Sparse vector types, inverted index, and search – always compiled (no persistence dependency). Sparse vector types, inverted index, and search.

storage

Storage backends for persistent vector storage.

sync

Synchronization primitives with loom support for concurrency testing.

vector_ref

Zero-copy vector reference abstraction.

velesql

VelesQL - SQL-like query language for VelesDB.

Macros

sum_remainder_unrolled_8

Macro for unrolled remainder sum computation (1-7 elements). Generates optimal code for remainders 1-7 with 4->2->1 unrolling.

sum_squared_remainder_unrolled_8

Macro for unrolled squared L2 remainder (1-7 elements).

Structs

Database

Database instance managing collections and storage.