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sqlite-vec

Perform vector similarity search and KNN queries within SQLite databases.

Updated Feb 17, 2026
Search Tags:#semantic search#embeddings#sqlite#vector search#database extension#knn
One-click install
npx skills add https://github.com/javierhbr/random-poc --skill sqlite-vec-javierhbr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sqlite-vec
Source: https://github.com/javierhbr/random-poc/tree/main/custom-skills/beagle-main/plugins/beagle-core/skills/sqlite-vec
Command: npx skills add https://github.com/javierhbr/random-poc --skill sqlite-vec-javierhbr

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables efficient vector similarity search directly within SQLite databases, eliminating the need for separate vector databases.

Core Features & Use Cases

  • In-Database Vector Storage: Store embeddings as native SQLite data types.
  • KNN Queries: Perform k-Nearest Neighbors searches on vector embeddings.
  • Use Case: Build a semantic search feature for your application by storing document embeddings in SQLite and querying them directly when a user searches for content.

Quick Start

Use the sqlite-vec skill to create a virtual table named 'vec_items' with a float embedding column.

Frequently Asked Questions about sqlite-vec

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform vector similarity search directly in SQLite?▼

You can achieve vector similarity search in SQLite by using an extension that creates virtual tables for embeddings and executes k-Nearest Neighbors queries directly within your existing database.

Can I store embeddings as native data types in SQLite without a separate vector database?▼

Yes, you can store embeddings as native SQLite data types using a dedicated extension, eliminating the need to deploy and manage a separate vector database for semantic search workloads.

What vector data types and distance metrics are supported for KNN queries in SQLite?▼

SQLite vector search supports float, int8, and binary vector types alongside various distance metrics, enabling flexible k-Nearest Neighbors queries and metadata filtering for semantic search applications.

How do I build a semantic search feature using SQLite and document embeddings?▼

To build a semantic search feature, store your document embeddings in a SQLite virtual table and query them directly using k-Nearest Neighbors search when users search for related content.

Does SQLite vector search support metadata filtering alongside KNN queries?▼

Yes, SQLite vector search supports metadata filtering capabilities alongside k-Nearest Neighbors queries, allowing you to combine vector similarity matching with traditional database filtering operations.

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