Documentation Index
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RisingWave supports a dedicated vector(n) data type to represent fixed-length numerical vectors, similar to the pgvector extension in PostgreSQL. Vectors are commonly used to store embeddings and other high-dimensional feature data.
Define a vector type
n is a positive integer specifying the number of dimensions (elements) in the vector. All elements are stored as real (float32) values, and they must be finite numbers,NULL, NaN, inf, and -inf are not allowed. Vectors are represented in SQL using a bracketed, comma-separated list format such as [1.0, 2.0, 3.0].
Examples
-- Create a table with vector columns
CREATE TABLE embeddings (
id INT PRIMARY KEY,
text_content VARCHAR,
embedding vector(128), -- 128-dimensional vector
features vector(3) -- 3-dimensional vector
);
-- Insert vector data
INSERT INTO embeddings VALUES
(1, 'Sample text', '[0.1, 0.2, 0.3, ...]', '[1.0, 2.0, 3.0]'),
(2, 'Another text', '[0.4, 0.5, 0.6, ...]', '[4.0, 5.0, 6.0]');
Type casts
Vectors can be converted between other types using casts:
| From | To | Cast type |
|---|
vector(n) | varchar | assign |
varchar | vector(n) | explicit |
vector(n) | real[] | implicit |
int[] / numeric[] / real[] / double precision[] | vector(n) | assign |
Distance functions
RisingWave provides built-in operators and functions for measuring distance and similarity between vectors.
| Operator / Function | Note |
|---|
vector(n) <-> vector(n) → double precision / l2_distance(vector(n), vector(n)) → double precision | |
vector(n) <=> vector(n) → double precision / cosine_distance(vector(n), vector(n)) → double precision | |
vector(n) <+> vector(n) → double precision / l1_distance(vector(n), vector(n)) → double precision | |
vector(n) <#> vector(n) → double precision | Negative inner product |
inner_product(vector(n), vector(n)) → double precision | |
Other vector operators
In addition to distance functions, RisingWave supports element-wise operations and vector transformations:
| Operator / Function | Description |
|---|
vector(n) + vector(n) → vector(n) | Element-wise addition |
vector(n) - vector(n) → vector(n) | Element-wise subtraction |
vector(n) * vector(n) → vector(n) | Element-wise multiplication |
vector(m) || vector(n) → vector(m+n) | Concatenate two vectors |
vector_norm(vector(n)) → double precision | Euclidean norm |
l2_normalize(vector(n)) → vector(n) | Normalize with Euclidean norm |
