Reference: Vector Type

CedarDB supports vectors for similarity search, which are compatible with pgvector. Vectors allow representing data in a vector space model, usually as fixed-dimensional floating-point weights.

Usage Example

create table example (
    word text,
    embedding vector(3)
);
insert into example
    values ('cat', '[1,2,3]'),
           ('dog', '[1,3,4]');
select * from example;

 word | embedding 
------+-----------
 cat  | [1,2,3]
 dog  | [1,3,4]
(2 rows)

This creates a basic word embedding, where you can quickly retrieve the embedding vector for a specific word. Vectors can be converted to and from arrays, but provide optimized vector search operations. You can find details about the operations on vectors in the advanced section.

CedarDB also supports vectors with different dimensions in the same column as vector (without the dimension parameter in vector(3)).

Value Range

Vectors have a maximum data size of 4 GiB per vector. Because vectors internally store their values as single-precision 4 Byte floats, CedarDB supports a maximum dimension of 2³⁰, which is about one billion. For practical reasons, we recommend keeping vectors below 10k dimensions.

Limitations

Currently, CedarDB only supports exact nearest neighbor search on vectors with perfect recall. Support for approximate search with faster queries is planned for future versions.