This repository contains the SQLite embedded SQL database engine source code, mirrored from the actively maintained SQLite project. The core is a C library that parses SQL, compiles it into a custom bytecode virtual machine (VDBE), and executes it against a B-tree storage layer backed by a transactional pager and a pluggable OS abstraction (VFS). Primary entry points are the public C API in src/sqlite.h.in, the sqlite3 command-line shell built from src/shell.c.in, and the init functions of many loadable extensions.
This repository implements a C-based SQLite loadable extension that adds vector search capabilities directly within SQLite databases. It enables similarity search using distance metrics (L2, Cosine, Dot, L1, Hamming) on various vector types (F32, F16, BF16, I8, U8, BIT) without requiring virtual tables. The core architecture is a modular C extension with runtime SIMD dispatch for hardware-accelerated distance calculations, supporting platforms including Linux, macOS, Windows, Android, iOS, and RISC-V. The project provides official language bindings for Python, Node.js, Swift, Flutter, and Android, facilitating integration into diverse application stacks. It includes a TurboQuant implementation for compact 2/3/4-bit quantization, allowing for reduced memory footprint and approximate search with high recall. The codebase demonstrates high craftsmanship in low-level optimization, particularly in its SIMD intrinsics handling and cross-platform binary distribution strategy. While the repository contains benchmarking scripts and quantization research documentation, its primary function is as a production-ready library for edge AI and mobile workloads. It is designed for offline, on-device AI with a low memory footprint (~30MB default). The zero open issues and recent commit activity indicate a stable and actively maintained codebase with low technical drift.
VexDB-Lite is a vector similarity search library providing Approximate Nearest Neighbor (ANN) capabilities as extensions for PostgreSQL, DuckDB, and SQLite databases. The system implements Hierarchical Navigable Small World (HNSW) graphs for efficient similarity search, with optional Product Quantization and RaBitQ compression for memory efficiency. It features hardware-accelerated distance computation using SIMD instructions (SSE, AVX, AVX512, NEON) and provides multiple distance metrics (L2, inner product, cosine). The architecture separates core algorithms from database-specific adapters, enabling consistent functionality across different storage engines. The codebase demonstrates strong engineering practices with reusable components like the SIMD dispatch system, vector buffer manager, and product quantization implementation.