vectrust

Vectrust Documentation

Welcome to the official documentation for Vectrust, a high-performance, embedded vector database engine written in pure Rust.

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      High-Performance Embedded Vector Database Engine

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Documentation Sections

• Getting Started: Installation, first database creation, ingestion, search, and deletion.
• RAG Pipelines & AI Agent Memory: Guide to using Vectrust for LLM retrieval and agent long-term memory.
• Local Development Setup: Complete guide for setting up Rust, building executables, and running tests.
• Contributing Guidelines: Workflow, coding standards, issue reporting, and pull request checklist.
• Architecture & Internals: Deep-dive into SIMD distance metrics, HNSW graph mechanics, WAL format, and concurrency design.
• Metadata Filtering: Complete guide to structured predicates, nested JSON dot-paths, and boolean filters.
• Benchmarks & Tuning: Throughput benchmarks, QPS, latency vs recall analysis, and parameter tuning guide.
• API Reference: Detailed reference documentation for all public structs, traits, and functions.

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Key Highlights

• Embedded First: Runs directly in-process within your application. No servers, no Docker, zero network overhead.
• State-of-the-art ANN: Hierarchical Navigable Small World (HNSW) graph index with sub-millisecond query latency.
• Exact Ground Truth: Flat brute-force linear scan index powered by multi-threaded Rayon execution.
• Crash-Resilient Persistence: Append-only Write-Ahead Log (WAL) and atomic snapshots with CRC32 integrity checks.
• Rich Metadata Filtering: Pre-filtering and filter-aware graph traversal over nested JSON payloads.
• Concurrent & Thread-Safe: Concurrent reader architecture using parking_lot::RwLock and parallel Rayon batch search.
• Pure Rust: Zero C/C++ dependencies. Works out-of-the-box across Linux, macOS, and Windows.

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