Vector Search

Turso supports vector search as a native feature — no extensions required. Store vector embeddings alongside your relational data and query them using built-in distance functions for similarity search.

Vector Types

Turso supports dense, sparse, quantized, and binary vector representations, each suited to different workloads.

Dense Vectors

Dense vectors store a value for every dimension. Turso provides two precision levels:

Function	Precision	Storage per dimension	Best for
`vector32`	32-bit float	4 bytes	Most ML embeddings (OpenAI, sentence transformers)
`vector64`	64-bit float	8 bytes	Applications requiring higher precision

Sparse Vectors

Sparse vectors only store non-zero values and their indices, making them memory-efficient for high-dimensional data with many zero values.

Function	Storage	Best for
`vector32_sparse`	Non-zero values + indices	TF-IDF, bag-of-words, high-dimensional sparse data

Quantized Vectors

Function	Storage per dimension	Best for
`vector8`	1 byte (+8 bytes overhead)	Large-scale search where ~4x compression vs Float32 is worth minimal precision loss

Values are linearly quantized to the 0-255 range using min/max scaling. Dequantization: f_i = alpha * q_i + shift.

Binary Vectors

Function	Storage per dimension	Best for
`vector1bit`	1 bit	Binary hashing, approximate nearest neighbor search (~32x compression vs Float32)

Positive values become 1, non-positive values become 0. Extracted values are displayed as +1/-1.

For most applications, vector32 is a good starting point. Explore more compact types if your table has a large number of rows.

Storing Vectors

Create a table with a BLOB column to store embeddings alongside your relational data:

CREATE TABLE documents (
    id INTEGER PRIMARY KEY,
    title TEXT,
    content TEXT,
    embedding BLOB
);

Insert rows with vector embeddings using the appropriate conversion function:

INSERT INTO documents (title, content, embedding) VALUES
    ('Machine learning basics', 'An introduction to ML concepts...', vector32('[0.2, 0.5, 0.1, 0.8]')),
    ('Database fundamentals', 'How relational databases work...', vector32('[0.1, 0.3, 0.9, 0.2]')),
    ('Neural networks guide', 'Deep learning architectures...', vector32('[0.3, 0.6, 0.2, 0.7]'));

For sparse vectors, zero values are automatically compressed:

INSERT INTO documents (title, content, embedding) VALUES
    ('Sparse example', 'A document with sparse features...', vector32_sparse('[0.0, 1.5, 0.0, 2.3, 0.0]'));

Similarity Search

Use distance functions to find the most similar vectors. All distance functions require both vectors to have the same type and dimensionality. Lower values indicate greater similarity.

Cosine Distance

Measures the angle between vectors, ignoring magnitude. Returns a value between 0 (identical direction) and 2 (opposite direction).

SELECT title,
       vector_distance_cos(embedding, vector32('[0.25, 0.55, 0.15, 0.75]')) AS distance
FROM documents
ORDER BY distance
LIMIT 5;

Best for text embeddings and document similarity where direction matters more than magnitude.

Euclidean (L2) Distance

Measures straight-line distance in n-dimensional space. Not supported for vector1bit vectors.

SELECT title,
       vector_distance_l2(embedding, vector32('[0.25, 0.55, 0.15, 0.75]')) AS distance
FROM documents
ORDER BY distance
LIMIT 5;

Best for image embeddings, spatial data, and unnormalized embeddings where absolute differences matter.

Dot Product Distance

Computes the negative dot product: -sum(v1[i] * v2[i]). Lower (more negative) values indicate higher similarity.

SELECT title,
       vector_distance_dot(embedding, vector32('[0.25, 0.55, 0.15, 0.75]')) AS distance
FROM documents
ORDER BY distance
LIMIT 5;

Best for normalized embeddings (equivalent to cosine distance when vectors are unit-length) and maximum inner product search (MIPS).

Jaccard Distance

Computes weighted Jaccard distance based on the ratio of minimum to maximum values across dimensions. For vector1bit vectors, computes binary Jaccard distance.

SELECT title,
       vector_distance_jaccard(embedding, vector32_sparse('[0.0, 1.0, 0.0, 2.0]')) AS distance
FROM documents
ORDER BY distance
LIMIT 5;

Best for sparse vectors, set-like comparisons, TF-IDF representations, and binary similarity with vector1bit.

Utility Functions

vector_extract

Convert a vector BLOB back to a readable JSON representation:

SELECT vector_extract(embedding) FROM documents WHERE id = 1;
-- [0.200000,0.500000,0.100000,0.800000]

vector_concat

Concatenate two vectors into one:

SELECT vector_extract(
    vector_concat(vector32('[1.0, 2.0]'), vector32('[3.0, 4.0]'))
);
-- [1.000000,2.000000,3.000000,4.000000]

vector_slice

Extract a contiguous portion of a vector (zero-based, end exclusive):

SELECT vector_extract(
    vector_slice(vector32('[1.0, 2.0, 3.0, 4.0, 5.0]'), 1, 4)
);
-- [2.000000,3.000000,4.000000]

Limitations

Euclidean distance is not supported for vector1bit vectors
Maximum vector dimensionality is 65,536
vector1bit cosine distance returns Hamming distance (number of differing bits) instead of standard cosine distance

Example: Semantic Search

A complete end-to-end example combining vector storage and similarity search:

-- Create a table with vector embeddings
CREATE TABLE articles (
    id INTEGER PRIMARY KEY,
    title TEXT NOT NULL,
    content TEXT,
    embedding BLOB
);

-- Insert articles with precomputed embeddings
INSERT INTO articles (title, content, embedding) VALUES
    ('Introduction to Machine Learning', 'ML is a subset of AI...', vector32('[0.12, -0.34, 0.56, 0.78]')),
    ('Database Design Patterns', 'Relational databases organize...', vector32('[0.82, 0.15, -0.44, 0.23]')),
    ('Neural Networks Explained', 'Neural networks are computing...', vector32('[0.14, -0.28, 0.62, 0.71]')),
    ('SQL Query Optimization', 'Efficient queries start with...', vector32('[0.75, 0.22, -0.38, 0.19]'));

-- Find the 3 most similar articles to a query vector
SELECT
    title,
    content,
    vector_distance_cos(embedding, vector32('[0.10, -0.30, 0.50, 0.80]')) AS distance
FROM articles
ORDER BY distance
LIMIT 3;

Similarity searches use a linear scan over the table. For large datasets, consider limiting the search to a subset of rows with a WHERE clause.

Getting Started

Fundamentals

AI and Agents

CLI Reference

SQL Reference

Changelog

Vector Search

Vector Types

Dense Vectors

Sparse Vectors

Quantized Vectors

Binary Vectors

Storing Vectors

Similarity Search

Cosine Distance

Euclidean (L2) Distance

Dot Product Distance

Jaccard Distance

Utility Functions

vector_extract

vector_concat

vector_slice

Limitations

Example: Semantic Search

See Also

​Vector Types

​Dense Vectors

​Sparse Vectors

​Quantized Vectors

​Binary Vectors

​Storing Vectors

​Similarity Search

​Cosine Distance

​Euclidean (L2) Distance

​Dot Product Distance

​Jaccard Distance

​Utility Functions

​vector_extract

​vector_concat

​vector_slice

​Limitations

​Example: Semantic Search

​See Also

Vector Types

Dense Vectors

Sparse Vectors

Quantized Vectors

Binary Vectors

Storing Vectors

Similarity Search

Cosine Distance

Euclidean (L2) Distance

Dot Product Distance

Jaccard Distance

Utility Functions

vector_extract

vector_concat

vector_slice

Limitations

Example: Semantic Search

See Also