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title, tabid, icon, landing_image, landing_image_png, image, image_caption, default_link, default_link_name, weight, short_description, sitemapExclude
| title | tabid | icon | landing_image | landing_image_png | image | image_caption | default_link | default_link_name | weight | short_description | sitemapExclude |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Semantic Text Search | textsearch | paper | /content/images/semantic_search_big.webp | /content/images/semantic_search_big.png | /content/images/solutions/semantic_text_search.svg | Neural Text Search | https://qdrant.to/semantic-search-demo | Demo | 20 | The vector search uses **semantic embeddings** instead of keywords and works best with short texts. With Qdrant, you can build and deploy semantic neural search on your data in minutes. Check out our [demo](https://qdrant.to/semantic-search-demo)! | True |
Full-text search does not always provide the desired result. Documents may have too few keywords, or queries might be too large.
One way to overcome these problems is a neural network-based semantic search, which can be used in conjunction with traditional search. The neural search uses semantic embeddings to find texts with similar meaning.
With Qdrant vector search engine, you can build and deploy semantic neural search on your data in minutes! Compare the results of a semantic and full-text search in our demo.