From b21e9d2ab67373a1462380f153c8830e4fac3870 Mon Sep 17 00:00:00 2001 From: David Myriel Date: Wed, 26 Mar 2025 10:51:58 +0100 Subject: [PATCH] Update qdrant-landing/content/blog/qdrant-1.14.x.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Kacper Łukawski --- qdrant-landing/content/blog/qdrant-1.14.x.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/qdrant-1.14.x.md b/qdrant-landing/content/blog/qdrant-1.14.x.md index 778b838d6..55dd3fe1d 100644 --- a/qdrant-landing/content/blog/qdrant-1.14.x.md +++ b/qdrant-landing/content/blog/qdrant-1.14.x.md @@ -119,7 +119,7 @@ POST /collections/{collection_name}/points/query ### Idea 3: Factor in Geographical Proximity -Let’s say you’re searching for a restaurant serving Currywurst. Sure, Berlin has some of the best, but you probably don’t want to spend two days traveling for a sausage covered in magical seasoning. The best match is the one that **balances the distance with a real-world geographical distance**. You want your users see relevant and conveniently located options. +Let’s say you’re searching for a restaurant serving Currywurst. Sure, Berlin has some of the best, but you probably don’t want to spend two days traveling for a sausage covered in magical seasoning. The best match is the one that **balances the distance in the vector space with a real-world geographical distance**. You want your users see relevant and conveniently located options. This feature introduces a multi-objective optimization: combining semantic similarity with geographical proximity. Suppose each point has a `geo.location` payload field (latitude, longitude). You can use a `gauss_decay` function to clamp the distance into a 0–1 range and add that to your similarity score: