sqlite-vec

Store and query vector embeddings in SQLite using vec0 virtual tables.

20|4|Updated Nov 9, 2025

Search Tags:#python #semantic search #embeddings #vector database #sqlite #database extension #knn

One-click install

npx skills add https://github.com/anderskev/amelia --skill sqlite-vec

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: sqlite-vec
Source: https://github.com/anderskev/amelia/tree/main/.claude/skills/amelia/sqlite-vec
Command: npx skills add https://github.com/anderskev/amelia --skill sqlite-vec

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Integrating vector similarity search often requires setting up and managing a separate vector database, adding complexity to your application stack. sqlite-vec brings this capability directly into SQLite, simplifying deployment and maintenance.

Core Features & Use Cases

In-Database Vector Search: Store and query vector embeddings (float, int8, bit) directly within SQLite using the vec0 virtual table.
Efficient KNN Queries: Perform K-Nearest Neighbor searches with support for L2, cosine, and Hamming distance metrics.
Metadata & Partition Filtering: Combine vector search with traditional SQL filtering on metadata columns and use partition keys for multi-tenant or temporal data.
Use Case: Build a lightweight semantic search engine for a local application, storing text embeddings alongside document metadata in a single SQLite database, enabling fast and relevant content retrieval.

Quick Start

Show me how to create a vec0 table in SQLite to store 768-dimension float embeddings and perform a basic KNN query.