Implementing vector search in SQLite

Modern semantic search systems often rely on external vector databases such as Pinecone, Milvus, or Redis, which add operational complexity and infrastructure overhead. But what if you didn’t need an external service at all? SQLite-Vector makes this possible, enabling vector search directly inside SQLite without additional dependencies.

# What is SQLite-Vector

SQLite‑Vector is an extension that exposes SQL functions to initialize vector columns, store embeddings, and run similarity searches.

• - Vectors are stored as columns in normal tables
• - Search is executed using SQL functions such as vector_quantize_scan and vector_full_scan
• - Works offline, in embedded environments, and across platforms
• - No pre-indexing or external services required

# High level architecture

Internally, SQLite-Vector stores embeddings as binary BLOB columns and computes distances at query time. Unlike systems that build precomputed approximate nearest neighbor (ANN) indices, it performs similarity calculations at query time, trading indexing performance for simplicity and offline usability.

Similarity queries look like normal SQL joins and return row IDs and distances, keeping queries simple and readable. Since this interface only returns rowid and distance, if you need to access additional columns from the original table, you must use a SELF JOIN.

# Setup

Load the extension into your SQLite connection

.load ./sqlite-vector/dist/vector.so

Once loaded, the vector functions become available. Loading must succeed for each connection, otherwise vector queries will fail.

# How to use it

Before running similarity queries, register a vector column with vector_init:

SELECT vector_init('foods', 'embedding', 'type=FLOAT32,dimension=1536')

# Store embeddings

Insert embeddings like any other column. Each blob must match the declared type and dimension. Typically, embeddings come from a single model to ensure consistency.

INSERT INTO foods (id, name, embedding)
VALUES
(1, 'Apple', X'01020304...'),
(2, 'Banana', X'01020305...');

# Perform quantization

After inserting data, you must precompute internal data structures to enable fast ANN search. If this function is called multiple times, previously quantized data will be replaced.

SELECT vector_quantize('foods', 'embedding', 'type=FLOAT32,dimension=1536')

# Run a similarity search

SELECT i.id, v.distance
FROM foods AS i
JOIN vector_quantize_scan('foods', 'embedding', ?, 10) AS v
  ON i.id = v.rowid;

Here, ? is your query embedding, and 10 is the number of closest matches you want. Simple, readable, and SQL-native.

# Powering my blog search with SQLite-Vector

Until now, this blog lacked a built-in search feature. The new search is powered entirely by SQLite-Vector, applying the same vector search technique outlined above to surface the most relevant posts.

Every post is embedded when it’s published. The embedding is stored directly in my SQLite database. Your search query is embedded in real time, and a similarity query returns the closest matching posts.

# Final thoughts

Vector search in SQLite using SQLite-Vector gives you a lightweight, integrated semantic search capability without extra infrastructure. It’s ideal for embedded systems, test setups, and offline workflows, and works well for small to medium-sized projects. If you need horizontal scaling or advanced ANN performance, other vector database solutions still have an edge.