AI & Embeddings
Vector Similarity Search is built into Turso and libSQL Server as a native feature.
Turso and libSQL enables vector search capability without an extension.
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Full support for vector search in the Turso platform starts from version v0.24.24
(use the turso group show <group-name>
command to check the group version).
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 theCREATE 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.
We suggest library authors use type names with the _BLOB
suffix to make results more generic and universal. For regular applications, developers can choose either alternative, as the type name only serves as a hint for SQLite and external extensions.
As LibSQL does not introduce a new storage class, all metadata about vectors is also encoded in the BLOB
itself. This comes at the cost of a few bytes per row but greatly simplifies the design of the feature.
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 |
For most applications, the FLOAT32
type should be a good starting point, but you may want to explore more compact options if your table has a large number of rows with vectors.
While FLOAT16
and FLOATB16
use the same amount of storage, they provide different trade-offs between speed and accuracy. Generally, operations over bfloat16
are faster but come at the expense of lower precision.
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 shiwh accepts valid vector and convert 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 | Euclidian distance function which operates over vector of same type with same dimensionality |
Vectors usage
Create a table
Begin by declaring a column used for storing vectors with the F32_BLOB
datatype:
CREATE TABLE movies (
title TEXT,
year INT,
full_emb F32_BLOB(4), -- 4-dimensional f32 vector
);
The number in parentheses (4)
specifies the dimensionality of the vector. This means each vector in this column will have exactly 4 components.
Generate and insert embeddings
Once you generate embeddings for your data (via an LLM), you can insert them into your table:
INSERT INTO movies (title, year, embedding)
VALUES
('Napoleon', 2023, vector32('[0.800, 0.579, 0.481, 0.229]')),
('Black Hawk Down', 2001, vector32('[0.406, 0.027, 0.378, 0.056]')),
('Gladiator', 2000, vector32('[0.698, 0.140, 0.073, 0.125]')),
('Blade Runner', 1982, vector32('[0.379, 0.637, 0.011, 0.647]'))
Popular tools like LangChain, Hugging Face or OpenAI can be used to generate embeddings.
Peform a vector similarity search
You can now write queries combining vectors and standard SQLite data:
SELECT title,
vector_extract(embedding),
vector_distance_cos(embedding, vector32('[0.064, 0.777, 0.661, 0.687]'))
FROM movies
ORDER BY
vector_distance_cos(embedding, vector32('[0.064, 0.777, 0.661, 0.687]'))
ASC;
The vector_distance_cos
function calculates the cosine distance, which
equals to 1 - cosine
similarity. Therefore, a
smaller distance indicates that the vectors are closer to each other.
Vector Limitations
- Euclidian 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 neareast neighbors queries for tables with vector colums.
The DiskANN algorithm trades search accuracy for speed, so LibSQL queries may return slightly suboptimal neighbors for tables with a large number of rows.
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
CREATE INDEX movies_idx ON movies (libsql_vector_idx(embedding));
Vector index works only for column with one of the vector types described above
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:
CREATE INDEX movies_idx ON movies (libsql_vector_idx(embedding))
WHERE year >= 2000;
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 search for k
approximate nearest neighbors and return ROWID
of these rows or PRIMARY KEY
if base index do not have ROWID.
In order for table-valued function to work query vector must have same vector type and same dimensionality.
Settings
LibSQL vector index optionall can accept settings which must be specified as a variadic parameters of the libsql_vector_idx
function as a strings in the format key=value
:
CREATE INDEX movies_idx
ON movies(libsql_vector_idx(embedding, 'metric=l2', 'compress_neighbors=float8'));
At the momen LibSQL supports following settings:
Setting key | Value type | Description |
---|---|---|
metric | cosine | l2 | Which distance function to use for building 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 comperssion (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 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 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 |
Vector index for column of type T1
with max_neighbors=M
and compress_neighbors=T2
will approximately use storage bytes for N
rows.
Index usage
Create a table
Begin by declaring a column used for storing vectors with the F32_BLOB
datatype:
CREATE TABLE movies (
title TEXT,
year INT,
full_emb F32_BLOB(4), -- 4-dimensional f32 vector
);
The number in parentheses (4)
specifies the dimensionality of the vector. This means each vector in this column will have exactly 4 components.
Generate and insert embeddings
Once you generate embeddings for your data (via an LLM), you can insert them into your table:
INSERT INTO movies (title, year, embedding)
VALUES
('Napoleon', 2023, vector32('[0.800, 0.579, 0.481, 0.229]')),
('Black Hawk Down', 2001, vector32('[0.406, 0.027, 0.378, 0.056]')),
('Gladiator', 2000, vector32('[0.698, 0.140, 0.073, 0.125]')),
('Blade Runner', 1982, vector32('[0.379, 0.637, 0.011, 0.647]'))
Popular tools like LangChain, Hugging Face or OpenAI can be used to generate embeddings.
Create an Index
Create an index using the libsql_vector_idx
function:
CREATE INDEX movies_idx ON movies(libsql_vector_idx(embedding));
This creates an index optimized for vector similarity searches on the embedding
column.
The libsql_vector_idx
marker function is required and used by libSQL to
distinguish ANN
-indices from ordinary B-Tree indices.
Query the indexed table
SELECT title, year
FROM vector_top_k('movies_idx', vector32('[0.064, 0.777, 0.661, 0.687]'), 3)
JOIN movies ON movies.rowid = id
WHERE year >= 2020;
This query uses the vector_top_k
table-valued function to efficiently find the top 3 most similar vectors to [0.064, 0.777, 0.661, 0.687]
using the index.
Index limitations
- Vector index works only for tables with
ROWID
or with singularPRIMARY KEY
. CompositePRIMARY KEY
withoutROWID
is not supported
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