Turso and libSQL enable vector search capability without an extension.

How it works

Create a table with one or more vector columns (e.g. FLOAT32)
Provide vector values in binary format or convert text representation to binary using the appropriate conversion function (e.g. vector32(...))
Calculate vector similarity between vectors in the table or from the query itself using dedicated vector functions (e.g. vector_distance_cos)
Create a special vector index to speed up nearest neighbors queries (use the libsql_vector_idx(column) expression in the CREATE INDEX statement to create vector index)
Query the index with the special vector_top_k(idx_name, q_vector, k) table-valued function

Vectors

Types

LibSQL uses the native SQLite BLOB storage class for vector columns. To align with SQLite affinity rules, all type names have two alternatives: one that is easy to type and another with a _BLOB suffix that is consistent with affinity rules.

The table below lists six vector types currently supported by LibSQL. Types are listed from more precise and storage-heavy to more compact but less precise alternatives (the number of dimensions in vector is used to estimate storage requirements for a single vector).

Type name	Storage (bytes)	Description
`FLOAT64` \| `F64_BLOB`		Implementation of IEEE 754 double precision format for 64-bit floating point numbers
`FLOAT32` \| `F32_BLOB`		Implementation of IEEE 754 single precision format for 32-bit floating point numbers
`FLOAT16` \| `F16_BLOB`		Implementation of IEEE 754-2008 half precision format for 16-bit floating point numbers
`FLOATB16` \| `FB16_BLOB`		Implementation of bfloat16 format for 16-bit floating point numbers
`FLOAT8` \| `F8_BLOB`		LibSQL specific implementation which compresses each vector component to single `u8` byte `b` and reconstruct value from it using simple transformation:
`FLOAT1BIT` \| `F1BIT_BLOB`		LibSQL-specific implementation which compresses each vector component down to 1-bit and packs multiple components into a single machine word, achieving a very compact representation

Functions

To work with vectors, LibSQL provides several functions that operate in the vector domain. Each function understands vectors in binary format aligned with the six types described above or in text format as a single JSON array of numbers. Currently, LibSQL supports the following functions:

Function name	Description
`vector64` \| `vector32` \| `vector16` \| `vectorb16` \| `vector8` \| `vector1bit`	Conversion function which accepts a valid vector and converts it to the corresponding target type
`vector`	Alias for `vector32` conversion function
`vector_extract`	Extraction function which accepts valid vector and return its text representation
`vector_distance_cos`	Cosine distance (1 - cosine similarity) function which operates over vector of same type with same dimensionality
`vector_distance_l2`	Euclidean distance function which operates over vector of same type with same dimensionality

Vectors usage

Understanding Distance Results

The vector_distance_cos function calculates the cosine distance, which is defined as:

Cosine Distance = 1 — Cosine Similarity

The cosine distance ranges from 0 to 2, where:

A distance close to 0 indicates that the vectors are nearly identical or exactly matching.
A distance close to 1 indicates that the vectors are orthogonal (perpendicular).
A distance close to 2 indicates that the vectors are pointing in opposite directions.

Vector Limitations

Euclidean distance is not supported for 1-bit FLOAT1BIT vectors
LibSQL can only operate on vectors with no more than 65536 dimensions

Indexing

Nearest neighbors (NN) queries are popular for various AI-powered applications (RAG uses NN queries to extract relevant information, and recommendation engines can suggest items based on embedding similarity). LibSQL implements DiskANN algorithm in order to speed up approximate nearest neighbors queries for tables with vector columns.

Vector Index

LibSQL introduces a custom index type that helps speed up nearest neighbors queries against a fixed distance function (cosine similarity by default). From a syntax perspective, the vector index differs from ordinary application-defined B-Tree indices in that it must wrap the vector column into a libsql_vector_idx marker function like this

The vector index is fully integrated into the LibSQL core, so it inherits all operations and most features from ordinary indices:

An index created for a table with existing data will be automatically populated with this data
All updates to the base table will be automatically reflected in the index
You can rebuild index from scratch using REINDEX movies_idx command
You can drop index with DROP INDEX movies_idx command
You can create partial vector index with a custom filtering rule:

Query

At the moment vector index must be queried explicitly with special vector_top_k(idx_name, q_vector, k) table-valued function. The function accepts index name, query vector and amount of neighbors to return. This function searches for k approximate nearest neighbors and returns ROWID of these rows or PRIMARY KEY if base index does not have ROWID. In order for table-valued function to work query vector must have the same vector type and dimensionality.

Settings

LibSQL vector index optionally can accept settings which must be specified as variadic parameters of the libsql_vector_idx function as strings in the format key=value:

At the moment LibSQL supports the following settings:

Setting key	Value type	Description
`metric`	`cosine` \| `l2`	Which distance function to use for building the index. Default: `cosine`
`max_neighbors`	positive integer	How many neighbors to store for every node in the DiskANN graph. The lower the setting — the less storage index will use in exchange to search precision. Default: where — dimensionality of vector column
`compress_neighbors`	`float1bit`\|`float8`\| `float16`\|`floatb16`\| `float32`	Which vector type must be used to store neighbors for every node in the DiskANN graph. The more compact vector type is used for neighbors — the less storage index will use in exchange to search precision. Default: no compression (neighbors has same type as base table)
`alpha`	positive float	“Density” parameter of general sparse neighborhood graph build during DiskANN algorithm. The lower parameter — the more sparse is DiskANN graph which can speed up query speed in exchange to lower search precision. Default: `1.2`
`search_l`	positive integer	Setting which limits the amount of neighbors visited during vector search. The lower the setting — the faster will be search query in exchange to search precision. Default: `200`
`insert_l`	positive integer	Setting which limits the amount of neighbors visited during vector insert. The lower the setting — the faster will be insert query in exchange to DiskANN graph navigability properties. Default: `70`

Index usage

Index limitations

Vector index works only for tables with ROWID or with singular PRIMARY KEY. Composite PRIMARY KEY without ROWID is not supported