velesdb
VelesDB is a local‑first AI data engine written in Rust that unifies vectors, full‑text and graph in a single file with a familiar SQL‑like language. Instead of sending every RAG or semantic search query to a remote cluster, VelesDB runs directly on your server, laptop, browser, mobile or edge device — no cloud dependency, no external services, ..
Learn to build and compile VelesDB from source with this complete installation guide. Follow simple steps to get the VelesDB binaries ready for use.
How to Manage Database Collections and Data in VelesDBLearn to manage VelesDB database collections and data. Explore vector, metadata, and graph types for efficient storage, upserts, and indexed queries using the Database and Collection structs.
VelesDB Local-First Database: Architecture, Performance, and Trade-offsExplore VelesDB's local-first architecture for sub-millisecond vector search. Eliminate network overhead and cloud costs with this efficient in-process database.
How VelesDB WASM Integration Works for Browser-Based ApplicationsDiscover how VelesDB WASM integration enables browser-based vector search. Explore its Rust core compiled with wasm-bindgen and the TypeScript SDK for efficient client-side operations. Learn more today.
Use Cases for VelesDB's ColumnStore Feature: High-Throughput Metadata Filtering and Hybrid SearchDiscover VelesDBs ColumnStore use cases for lightning fast metadata filtering at over 50 million items per second. Optimize recommendation engines and hybrid search queries.
How to Achieve High Recall Rates in VelesDB's Accurate Search ModeAchieve high recall rates in VelesDB accurate search mode. Optimize with the `accurate` preset, `ef_search`, and two-stage reranking for guaranteed ≥95% recall on collections up to 100k points.
VelesDB Mobile Deployment: A Complete Guide to iOS and Android IntegrationDeploy VelesDB on iOS and Android with the velesdb-mobile crate. This guide details native integration using UniFFI-generated Swift and Kotlin bindings for seamless mobile deployment.
How VelesDB's Storage Layer Combines Memory-Mapped Files and WAL for High-Performance Vector StorageDiscover how VelesDB's storage layer uses memory-mapped files and WAL for efficient zero-copy vector access and durable payload storage, boosting performance.
VelesDB Multi-Score Fusion Strategies: RRF, Average, Max, and Weighted ExplainedExplore VelesDB's multi-score fusion strategies like RRF, Average, Max, and Weighted. Learn how VelesDB combines ranked results from multiple query vectors for unified rankings.
How VelesDB Implements Approximate Nearest Neighbor Search with HNSWDiscover how VelesDB implements approximate nearest neighbor search with HNSW using its native Rust engine. Explore SIMD acceleration, hierarchical traversal, and adaptive multi-probe strategies for efficient search.
VelesQL Syntax for Filtering Data Based on Vector Similarity: A Complete GuideMaster VelesQL syntax for vector similarity filtering. Learn how to use similarity predicates in WHERE clauses to efficiently search your VelesDB with HNSW index support.
How VelesDB Handles Graph Traversal Using BFS and DFS: A Technical Deep DiveDiscover how VelesDB tackles graph traversal with efficient BFS and DFS in pure Rust. Explore configurable options and multi-platform support for your graph database needs.
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