VectorChord

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Quantization Types since v1.1.0 ​

Quantization can be applied not only to indexes but also directly to vectors. To this end, VectorChord provides data types for quantized vectors.

rabitq8 is the data type of a quantized vector. For the same dimension, its cost is about that of a full-precision vector (vector) and that of a half-precision vector (halfvec). This type is named after its quantization algorithm.[1]

sql
CREATE TABLE items (id bigserial PRIMARY KEY, embedding rabitq8(3));
INSERT INTO items (embedding) VALUES (quantize_to_rabitq8('[0,0,0]'::vector));
INSERT INTO items (embedding) VALUES (quantize_to_rabitq8('[1,1,1]'::vector));
INSERT INTO items (embedding) VALUES (quantize_to_rabitq8('[2,2,2]'::vector));
--- ...
SELECT id FROM items ORDER BY embedding <-> quantize_to_rabitq8('[0.9,0.9,1.1]'::vector) LIMIT 100;

Like them, an index can be created on columns of type rabitq8, too.

sql
CREATE INDEX ON items USING vchordrq (embedding rabitq8_l2_ops);

By using rabitq8, both the table size and index size become of what they would be when the type of the column is vector. The cost is that both performance and precision are compromised by quantization noticeably. Therefore, it is applicable in scenarios that retrieve a large number of results.

In addition, there is also the rabitq4 type. rabitq8 quantized vectors use bits per dimension, whereas rabitq4 quantized vectors use only bits per dimension. It is designed for constrained situations that favor low storage over high precision.

Reference ​

Operator Classes ​

Refer to

Types ​

rabitq8 since v1.1.0 ​

  • Description: The data type of a quantized vector that uses uint8 for internal storage. It's based on extended RaBitQ.[1:1]

rabitq4 since v1.1.0 ​

  • Description: The data type of a quantized vector that uses uint4 for internal storage. It's based on extended RaBitQ.[1:2]

Functions ​

quantize_to_rabitq8(vector) since v1.1.0 ​

  • Description: Quantizes a vector to rabitq8.
  • Result: rabitq8, the quantized vector
  • Arguments:
    • vector, the vector to be quantized
  • Example:
    • SELECT quantize_to_rabitq8('[1,1,1]'::vector)

quantize_to_rabitq8(halfvec) since v1.1.0 ​

  • Description: Quantizes a vector to rabitq8.
  • Result: rabitq8, the quantized vector
  • Arguments:
    • halfvec, the vector to be quantized
  • Example:
    • SELECT quantize_to_rabitq8('[1,1,1]'::halfvec)

quantize_to_rabitq4(vector) since v1.1.0 ​

  • Description: Quantizes a vector to rabitq4.
  • Result: rabitq4, the quantized vector
  • Arguments:
    • vector, the vector to be quantized
  • Example:
    • SELECT quantize_to_rabitq4('[1,1,1]'::vector)

quantize_to_rabitq4(halfvec) since v1.1.0 ​

  • Description: Quantizes a vector to rabitq4.
  • Result: rabitq4, the quantized vector
  • Arguments:
    • halfvec, the vector to be quantized
  • Example:
    • SELECT quantize_to_rabitq4('[1,1,1]'::halfvec)

Operators ​

<->(rabitq8, rabitq8) since v1.1.0 ​

  • Description: Calculates L2 distance of two quantized vectors.
  • Result: real, L2 distance of two quantized vectors
  • Arguments:
    • rabitq8
    • rabitq8

<#>(rabitq8, rabitq8) since v1.1.0 ​

  • Description: Calculates negative dot product distance of two quantized vectors.
  • Result: real, negative dot product distance of two quantized vectors
  • Arguments:
    • rabitq8
    • rabitq8

<=>(rabitq8, rabitq8) since v1.1.0 ​

  • Description: Calculates cosine distance of two quantized vectors.
  • Result: real, cosine distance of two quantized vectors
  • Arguments:
    • rabitq8
    • rabitq8

<->(rabitq4, rabitq4) since v1.1.0 ​

  • Description: Calculates L2 distance of two quantized vectors.
  • Result: real, L2 distance of two quantized vectors
  • Arguments:
    • rabitq4
    • rabitq4

<#>(rabitq4, rabitq4) since v1.1.0 ​

  • Description: Calculates negative dot product distance of two quantized vectors.
  • Result: real, negative dot product distance of two quantized vectors
  • Arguments:
    • rabitq4
    • rabitq4

<=>(rabitq4, rabitq4) since v1.1.0 ​

  • Description: Calculates cosine distance of two quantized vectors.
  • Result: real, cosine distance of two quantized vectors
  • Arguments:
    • rabitq4
    • rabitq4

  1. Jianyang Gao, Yutong Gou, Yuexuan Xu, Yongyi Yang, Cheng Long, Raymond Chi-Wing Wong, "Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search", SIGMOD 2025, available at https://arxiv.org/abs/2409.09913. ↩︎ ↩︎ ↩︎