Indexing

Similar to ivfflat, the index type of VectorChord, RaBitQ(vchordrq) also divides vectors into lists, and then searches a subset of those lists that are closest to the query vector. It inherits the advantages of ivfflat, such as fast build times and less memory usage, but has much better performance than hnsw and ivfflat.

The RaBitQ(vchordrq) index is supported on some pgvector types and metrics:

	vector	halfvec	bit(n)	sparsevec
L2 distance / <->	✅	✅	🆖	❌
inner product / <#>	✅	✅	🆖	❌
cosine distance / <=>	✅	✅	🆖	❌
L1 distance / <+>	❌	❌	🆖	❌
Hamming distance/ <~>	🆖	🆖	❌	🆖
Jaccard distance/ <%>	🆖	🆖	❌	🆖

Where:

• ✅ means supported by pgvector and VectorChord
• ❌ means supported by pgvector but not by VectorChord
• 🆖 means not planned by pgvector and VectorChord
• 🔜 means supported by pgvector now and will be supported by VectorChord soon

To create the VectorChord RaBitQ(vchordrq) index, you can use the following SQL.

L2 distance

CREATE INDEX ON items USING vchordrq (embedding vector_l2_ops) WITH (options = $$
residual_quantization = true
[build.internal]
lists = [1000]
spherical_centroids = false
$$);

NOTE

• options are specified using a TOML: Tom's Obvious Minimal Language string.
• Set residual_quantization to true and spherical_centroids to false for L2 distance embeddings
• Use halfvec_l2_ops for halfvec
• The recommended lists could be rows / 1000 for up to 1M rows and 4 * sqrt(rows) for over 1M rows

Inner product

CREATE INDEX ON items USING vchordrq (embedding vector_ip_ops) WITH (options = $$
residual_quantization = false
[build.internal]
lists = [1000]
spherical_centroids = true
$$);

Cosine distance

CREATE INDEX ON items USING vchordrq (embedding vector_cosine_ops) WITH (options = $$
residual_quantization = false
[build.internal]
lists = [1000]
spherical_centroids = true
$$);

NOTE

• Set residual_quantization to false and spherical_centroids to true for cosine similarity embeddings
• Use halfvec_cosine_ops/halfvec_ip_ops for halfvec

To construct an index for vectors, first create a table named items with a column named embedding of type vector(n). Then, populate the table with generated data.

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

You can create a vector index for squared Euclidean distance with the following SQL.

CREATE INDEX ON items USING vchordrq (embedding vector_l2_ops) WITH (options = $$
residual_quantization = true
[build.internal]
lists = [4096]
spherical_centroids = false
$$);

The [build.internal] section contains the following options:

• lists: The number of lists in the inverted file.
• spherical_centroids: Whether to use spherical centroids.

The index will be built internally.