How does this compare to dedicated vector databases?

It offers a much lower operational overhead and allows you to keep your relational data and embeddings in a single SQLite file, making it ideal for edge use cases and smaller-to-medium datasets.

sqlite-vec is a lightweight SQLite extension that enables vector similarity search directly within your database, eliminating the need for external vector DBs.

What are partition keys in sqlite-vec?

Partition keys are used for internal sharding, which significantly speeds up filtered queries in multi-tenant or temporal datasets.

Which distance metrics are supported by this skill?

The skill provides patterns for using L2 (default), Cosine, and Hamming distance metrics (for bit vectors).

Can I use sqlite-vec with Python?

Yes, the skill includes specific guidance for Python integration, including how to serialize vectors and use NumPy arrays with the extension.

SQLite Vector Search (sqlite-vec)

Name: SQLite Vector Search (sqlite-vec)
Author: existential-birds

byexistential-birds

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データベース管理

Implements high-performance vector similarity search and embedding storage directly within SQLite databases.

概要

The sqlite-vec skill equips Claude with specialized knowledge to implement lightweight, local vector databases using the sqlite-vec extension. It provides standardized patterns for creating vec0 virtual tables, executing K-Nearest Neighbor (KNN) queries with metadata filtering, and managing various vector types like float32, int8, and bit vectors. This skill is essential for developers building semantic search, recommendation systems, or RAG (Retrieval-Augmented Generation) applications who prefer the simplicity of SQLite over the overhead of a standalone vector database.

主な機能

Python integration patterns using serialization helpers and NumPy
Implementation of L2, Cosine, and Hamming distance metrics
Comprehensive KNN query patterns using the MATCH operator
Advanced metadata filtering and partition key optimization for multi-tenant search
14 GitHub stars
Support for multiple vector types including float32, int8, and bit vectors

ユースケース

Implementing lightweight RAG workflows in Python or Go applications
Building local semantic search for documentation or knowledge bases
Creating efficient multi-tenant recommendation engines on edge devices

概要

主な機能

Python integration patterns using serialization helpers and NumPy
Implementation of L2, Cosine, and Hamming distance metrics
Comprehensive KNN query patterns using the MATCH operator
Advanced metadata filtering and partition key optimization for multi-tenant search
14 GitHub stars
Support for multiple vector types including float32, int8, and bit vectors

ユースケース

Implementing lightweight RAG workflows in Python or Go applications
Building local semantic search for documentation or knowledge bases
Creating efficient multi-tenant recommendation engines on edge devices