landing_page/qdrant-landing/content/solutions/text-search.md

title, tabid, icon, landing_image, image, image_caption, default_link, default_link_name, weight, short_description, sitemapExclude

title	tabid	icon	landing_image	image	image_caption	default_link	default_link_name	weight	short_description	sitemapExclude
Semantic Text Search	textsearch	paper	/content/images/abstract_cubes_1.svg	/content/images/solutions/semantic_text_search.svg	Neural Text Search	https://qdrant.to/semantic-search-demo	Demo	10	The neural search uses **semantic embeddings** instead of keywords and works best with short texts. With Qdrant and a pre-trained neural network, you can build and deploy semantic neural search on your data in minutes. Check out our [demo](https://qdrant.to/semantic-search-demo)!	True

In many cases, the usual full-text search does not provide the desired result. Documents may have too few keywords, or queries might be too large. In such cases, the search either finds no intersections or returns a lot of irrelevant results.

One way to overcome these problems is a neural network-based semantic search. It can be used stand-alone or in conjunction with traditional search.The neural search uses semantic embeddings instead of keywords and works best with short texts.

With Qdrant and a pre-trained neural network, you can build and deploy semantic neural search on your data in minutes!

Check out our demo. Compare the results of a semantic search based on a pre-trained transformer NN and a regular full-text search.