veclite
SQLite for vectors — embedded AI memory engine
No server. No infra. Just a file and a Rust library.
Demo
Problem
Adding semantic search to a small project means spinning up Pinecone, Weaviate, or a Postgres extension. That's a server, a connection pool, and infra for something that should be as simple as opening a file.
What it is
Veclite is an embedded vector database written in Rust. It lives inside your process and persists to a single file — same mental model as SQLite, but for vector search.
- insert text or raw embeddings
- approximate nearest-neighbour search via HNSW
- single file, zero dependencies at runtime
- Rust library or CLI
Features
Embedded
No server. No daemon. Runs inside your process.
File-based
Single file storage — deploy, copy, and backup trivially.
HNSW Index
Fast approximate nearest-neighbour search via HNSW.
Rust Core
Memory-safe, zero-overhead, no GC pauses.
Architecture
Query example
Input
veclite search --query "machine learning optimizer" --k 3
Output
id=12 score=0.94 "Adam optimizer and learning rate scheduling" id=7 score=0.91 "SGD momentum and weight decay" id=23 score=0.88 "gradient clipping strategies"
Add to your Rust project
cargo add vecliteWhy I Built This
I wanted semantic search in a local tool without standing up a vector database. Every option was either too heavy or too opinionated.
Veclite is the SQLite of vector stores — embed it, forget the infra.