VecLite

VecLite is an embedded vector database. It runs in-process and stores data in a single file.

It provides exact and approximate nearest neighbor search, metadata filtering, and a vector-native SQL dialect. It is designed for applications that require vector search without operating a separate database server.

Features

Embedded architecture (no server required)
Single-file storage
SIMD-accelerated vector metrics (Cosine, L2, Dot Product, Manhattan)
HNSW indexing
SQL query layer
Hybrid search (vector similarity combined with metadata filters)
Cross-language support via C ABI

Language Bindings

VecLite is written in Rust. It provides native bindings for multiple languages.

Rust

The core database.

Status: Stable
Install: cargo add veclite-db

Python

Native Python module using PyO3. Releases the Global Interpreter Lock (GIL) during search operations.

Status: Stable
Install: pip install veclite-db

Go

CGO wrapper over the C ABI.

Status: Stable
Install: go get github.com/rithulkamesh/veclite/bindings/go@v1.0.9

C / C++

Standard C header and C++ RAII wrapper.

Status: Stable
Install: Copy bindings/c/veclite.h or bindings/cpp/veclite.hpp.

Zig

Zig wrapper over the C ABI.

Status: Experimental
Install: Import bindings/zig/veclite.zig.

Example

use veclite_db::VecLite;

let mut db = VecLite::open("memory.vec").unwrap();
db.insert("doc_1", vec![0.1, 0.2, 0.3], None).unwrap();

let results = db.search(vec![0.1, 0.2, 0.3]).top_k(5).execute().unwrap();

SQL Example

VecLite supports a custom SQL dialect for vector operations.

CREATE TABLE memory;
INSERT INTO memory VALUES ('doc_1', '[0.1, 0.2, 0.3]', '{"tag":"note"}');
SELECT * FROM memory WHERE metadata.tag = 'note' ORDER BY vector <-> '[0.1, 0.2, 0.3]' LIMIT 5;

Installation

Use the package manager for your target language. For the CLI and HTTP server:

cargo add veclite-db

Status

VecLite is under active development.

Roadmap

SQL execution engine completion
Compaction and retention policies
Node.js bindings

License

MIT

veclite-db 1.1.1