From afaa88fdce648fd5a48d9c2ef2ea1365cf29e0e8 Mon Sep 17 00:00:00 2001 From: Sabrina Aquino Date: Thu, 25 Jan 2024 18:18:23 -0300 Subject: [PATCH] new image for similarity search --- qdrant-landing/content/blog/what-is-a-vector-database.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/blog/what-is-a-vector-database.md b/qdrant-landing/content/blog/what-is-a-vector-database.md index c0ede1085..5c5660117 100644 --- a/qdrant-landing/content/blog/what-is-a-vector-database.md +++ b/qdrant-landing/content/blog/what-is-a-vector-database.md @@ -127,7 +127,7 @@ The way it works is, when the user queries the database, this query is also conv The search then moves down progressively narrowing down to more closely related vectors. The goal is to narrow down the dataset to the most relevant items. The image below illustrates this. -![](/blog/what-is-a-vector-database/Similarity-Search-and-Retrieval) +![](/blog/what-is-a-vector-database/Similarity-Search-and-Retrieval.jpg) Once the closest vectors are identified at the bottom layer, these points translate back to actual data, like images or music, representing your search results.