pgvecto.rs

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Vector Types ​

In the previous sections Indexing and Search, we encountered the vector type. This section will explore additional vector types and their applications in the realm of vector search.

Low-precision Indexing since v0.3.0 ​

To improve the speed of queries on the vector column, consider creating a low-precision index using vecf16, veci8, or bvector types instead of a vector type.

sql
CREATE TABLE t (val vector(3));
INSERT INTO t (val) SELECT ARRAY[random(), random(), random()]::real[] FROM generate_series(1, 1000);

-- Create bvector index at vector column
-- Warning: Float32 vectors are truncated to 
--          integers of either 0 or 1 during index construction
CREATE INDEX lp_bin ON t USING vectors ((binarize(val)::bvector(3)) bvector_l2_ops);

-- Create vecf16 index at vector column
CREATE INDEX lp_f16 ON t USING vectors ((val::vecf16(3)) vecf16_l2_ops);

-- Create veci8 index at vector column
-- Warning: Float32 vectors are truncated to 
--          integers ranging from -128 to 127 during index construction
CREATE INDEX lp_i8 ON t USING vectors ((val::veci8(3)) veci8_l2_ops);

Shortening Embedding since v0.3.0 ​

Certain embedding models like OpenAI text-embedding-3-small support shortening embeddings, which involves removing some numbers from the end of the sequence without losing the embedding's concept-representing properties.

In some cases, it may be necessary to change the embedding dimension after generation. The pgvecto.rs offers an approach to query by shortened embeddings.

sql
CREATE TABLE t (val vector(3));
INSERT INTO t (val) SELECT ARRAY[random(), random(), random()]::real[] FROM generate_series(1, 1000);
CREATE INDEX ON t USING vectors (val vector_l2_ops);

-- Measure distance using only the first two dimensions
SELECT * from t ORDER BY (val[:2])::vector(2) <-> '[3, 2]'::vector;

Shortened embeddings are supported by all data types in pgvecto.rs since version v0.3.0.

Casts ​

This diagram shows the conversion between different data types, where the types connected by arrows can be cast to each other:

Among them, ARRAY is a native type of postgreSQL, others are types defined by pgvecto.rs.

Here are some examples of vector type casting:

sql
-- OK: String ⇒ vector
SELECT '{1,2,3}'::vector;

-- OK: vector ⇒ ARRAY
SELECT '{1,2,3}'::vector::real[];

-- OK: ARRAY ⇒ vector ⇒ bvector
SELECT ARRAY[1.0 ,0 ,0]::real[]::vector::bvector;

-- ERROR: cannot cast type vecf16 to veci8
SELECT '[3, 2]'::vecf16::veci8;

-- OK: vecf16 ⇒ vector ⇒ veci8
SELECT '[3, 2]'::vecf16::vector::veci8;