From 3f674c9994547913f3011edc82d2d6c3c73378d8 Mon Sep 17 00:00:00 2001 From: Andrew Kane Date: Mon, 25 Mar 2024 23:33:17 -0700 Subject: [PATCH] Updated readme [skip ci] --- README.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index ff4de3a..8bff108 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ Open-source vector similarity search for Postgres Store your vectors with the rest of your data. Supports: - exact and approximate nearest neighbor search -- L2 distance, inner product, and cosine distance +- L2 distance, inner product, cosine distance, and more - any [language](#languages) with a Postgres client Plus [ACID](https://en.wikipedia.org/wiki/ACID) compliance, point-in-time recovery, JOINs, and all of the other [great features](https://www.postgresql.org/about/) of Postgres @@ -221,7 +221,19 @@ Cosine distance CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops); ``` -Vectors with up to 2,000 dimensions can be indexed. +Hamming distance - added in 0.7.0 + +```sql +CREATE INDEX ON items USING hnsw (embedding bit_hamming_ops); +``` + +Jaccard distance - added in 0.7.0 + +```sql +CREATE INDEX ON items USING hnsw (embedding bit_jaccard_ops); +``` + +Vectors with up to 2,000 dimensions can be indexed, or bit vectors with up to 64,000 dimensions. ### Index Options