sqlite-vec

sqlite-vec is a lightweight SQLite extension for vector similarity search. It enables storing and querying vector embeddings directly in SQLite databases without external vector databases.

Gates

When wiring storage and KNN queries, run these steps in order; each step has an objective pass before you rely on results in production or in review comments.

1. Dimension lock — The N in float[N], int8[N], or bit[N] matches the embedding model’s output length (and any Matryoshka slice you apply). Pass: the same N appears in the CREATE VIRTUAL TABLE DDL and in the serialized vector length (len(vector) or the model’s documented dimension).
2. Serialization match — Column type and Python (or other) helpers align: serialize_float32 for float[N], serialize_int8 for int8[N], binary rules for bit[N]. Pass: at least one round-trip insert of a known test vector and a MATCH query using the same serializer returns the expected row (e.g. k = 1 returns that row).
3. Doc-backed edge claims — Distance metric choice, metadata filter operators, partition-key rules, or version-specific behavior are only asserted if they appear in this skill or in the official docs linked under Resources. Pass: the relevant URL or doc section is cited in the artifact (issue, PR, or note) before the claim is treated as settled.

Quick Reference

Load Extension

import sqlite3
import sqlite_vec
from sqlite_vec import serialize_float32