From 430c8048e8c0bd15448c8ba6faae67a568dffe69 Mon Sep 17 00:00:00 2001 From: Amey Pawar <138877912+ameyypawar@users.noreply.github.com> Date: Tue, 30 Jun 2026 11:47:06 +0530 Subject: [PATCH 1/2] docs: remove duplicate usage link and restore [^4] footnote reference in README The "For more usage" list had "Quantization Types" twice (identical text and URL); remove the duplicate. Footnote [^4] was defined but never referenced, so its blog/doc links never rendered; attach the marker to the "Accelerated Index Build" claim it documents (100M vectors in 20 minutes via hierarchical K-means). --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index e305937c..6071cf55 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ VectorChord introduces remarkable enhancements over pgvecto.rs and pgvector: [^2]: Please check out our [blog post](https://blog.vectorchord.ai/vectorchord-store-400k-vectors-for-1-in-postgresql) for more details. [^3]: Please check out our [blog post](https://blog.vectorchord.ai/scaling-vector-search-to-1-billion-on-postgresql) for more details. -**⚡ Accelerated Index Build**: Index 100 million vectors in just 20 minutes. Powered by hierarchical K-means and highly optimized disk operations, VectorChord eliminates the bottleneck of vector indexing on a single machine with limited hardware resources. +**⚡ Accelerated Index Build**: Index 100 million vectors in just 20 minutes[^4]. Powered by hierarchical K-means and highly optimized disk operations, VectorChord eliminates the bottleneck of vector indexing on a single machine with limited hardware resources. [^4]: Please check out our [blog post](https://blog.vectorchord.ai/how-we-made-100m-vector-indexing-in-20-minutes-possible-on-postgresql#heading-hierarchical-k-means) for more technique details and [document](https://docs.vectorchord.ai/vectorchord/usage/partitioning-tuning.html#hierarchical-k-means) for usages. @@ -102,7 +102,6 @@ For more usage, please read: - [Multi-Vector Retrieval](https://docs.vectorchord.ai/vectorchord/usage/indexing-with-maxsim-operators.html) - [Quantization Types](https://docs.vectorchord.ai/vectorchord/usage/quantization-types.html) - [Graph Index](https://docs.vectorchord.ai/vectorchord/usage/graph-index.html) -- [Quantization Types](https://docs.vectorchord.ai/vectorchord/usage/quantization-types.html) - [Similarity Filter](https://docs.vectorchord.ai/vectorchord/usage/range-query.html) - [PostgreSQL Tuning](https://docs.vectorchord.ai/vectorchord/usage/performance-tuning.html) - [Monitoring](https://docs.vectorchord.ai/vectorchord/usage/monitoring.html) From 1c162106b0107fb177c5bda6ed9f04674dce460f Mon Sep 17 00:00:00 2001 From: Amey Pawar <138877912+ameyypawar@users.noreply.github.com> Date: Tue, 30 Jun 2026 17:30:44 +0530 Subject: [PATCH 2/2] docs: attach [^4] marker to the hierarchical K-means clause The footnote's links both anchor on #hierarchical-k-means, so place the inline marker on that clause (mirroring how [^5] sits on "sampling"), rather than on the "20 minutes" claim. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6071cf55..e1115831 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ VectorChord introduces remarkable enhancements over pgvecto.rs and pgvector: [^2]: Please check out our [blog post](https://blog.vectorchord.ai/vectorchord-store-400k-vectors-for-1-in-postgresql) for more details. [^3]: Please check out our [blog post](https://blog.vectorchord.ai/scaling-vector-search-to-1-billion-on-postgresql) for more details. -**⚡ Accelerated Index Build**: Index 100 million vectors in just 20 minutes[^4]. Powered by hierarchical K-means and highly optimized disk operations, VectorChord eliminates the bottleneck of vector indexing on a single machine with limited hardware resources. +**⚡ Accelerated Index Build**: Index 100 million vectors in just 20 minutes. Powered by hierarchical K-means[^4] and highly optimized disk operations, VectorChord eliminates the bottleneck of vector indexing on a single machine with limited hardware resources. [^4]: Please check out our [blog post](https://blog.vectorchord.ai/how-we-made-100m-vector-indexing-in-20-minutes-possible-on-postgresql#heading-hierarchical-k-means) for more technique details and [document](https://docs.vectorchord.ai/vectorchord/usage/partitioning-tuning.html#hierarchical-k-means) for usages.