From bd1aee732d877fa22b4e94a790d899ae1a41ca04 Mon Sep 17 00:00:00 2001 From: Iain Date: Tue, 11 Jun 2024 14:27:25 +0200 Subject: [PATCH] fix: link. --- ai/sql-interface-for-pgvector-and-timescale-vector.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ai/sql-interface-for-pgvector-and-timescale-vector.md b/ai/sql-interface-for-pgvector-and-timescale-vector.md index 76a27e0ba0..eae394231f 100644 --- a/ai/sql-interface-for-pgvector-and-timescale-vector.md +++ b/ai/sql-interface-for-pgvector-and-timescale-vector.md @@ -104,7 +104,7 @@ You can see the details of each index below. The StreamingDiskANN index is a graph-based algorithm that was inspired by the [DiskANN](https://github.com/microsoft/DiskANN) algorithm. You can read more about it in -[How We Made PostgreSQL as Fast as Pinecone for Vector Data](www.timescale.com/blog/how-we-made-postgresql-as-fast-as-pinecone-for-vector-data). +[How We Made PostgreSQL as Fast as Pinecone for Vector Data](https://www.timescale.com/blog/how-we-made-postgresql-as-fast-as-pinecone-for-vector-data/). To create an index named `document_embedding_idx` on table `document_embedding` having a vector column named `embedding`, run: