krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-28 23:48:31 +02:00
Code Issues Packages Projects Releases Wiki Activity

Typo correction in the article

Browse Source
This commit is contained in:
atita arora
2024-02-01 11:51:12 +01:00
parent 113140c234
commit 75620ab5f8
1 changed files with 1 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/articles/discovery-search.md
+1 -1
View File
@@ -31,7 +31,7 @@ This is where a __vector _context___ can help. We define _context_ as a list of
![Discovery search visualization](/articles_data/discovery-search/discovery-search.png)
While positive and negative vectors might suggest the use of the <a href="/documentation/concepts/explore/#recommendation-api" target="_blank">recommendation interface</a>, in the case of _context_ they require to be paired up in a positive-negative fashion. This is inspired from the machine-learning concept of <a href="https://en.wikipedia.org/wiki/Triplet_loss" target="_blank">_triplet loss_</a>, where you have three vectors: an anchor, a positive, and a negative. Triplet loss is an evaluation of how much the anchor is closer to the positive than to the negative vector, so that learning happens by "moving" the positive and negative points to try to get a better evaluation. However, during discovery, we consider the positive and negative vectors as static points, and we search through the whole dataset for the "anchors", or result cantidates, which fit this characteristic better.
While positive and negative vectors might suggest the use of the <a href="/documentation/concepts/explore/#recommendation-api" target="_blank">recommendation interface</a>, in the case of _context_ they require to be paired up in a positive-negative fashion. This is inspired from the machine-learning concept of <a href="https://en.wikipedia.org/wiki/Triplet_loss" target="_blank">_triplet loss_</a>, where you have three vectors: an anchor, a positive, and a negative. Triplet loss is an evaluation of how much the anchor is closer to the positive than to the negative vector, so that learning happens by "moving" the positive and negative points to try to get a better evaluation. However, during discovery, we consider the positive and negative vectors as static points, and we search through the whole dataset for the "anchors", or result candidates, which fit this characteristic better.
![Triplet loss](/articles_data/discovery-search/triplet-loss.png)