---
title: Semantic Text Search
tabid: textsearch
icon: paper
landing_image: /content/images/semantic_search_big.webp
landing_image_png: /content/images/semantic_search_big.png
image: /content/images/solutions/semantic_text_search.svg
image_caption: Neural Text Search
default_link: https://qdrant.to/semantic-search-demo
default_link_name: Demo
weight: 20
short_description: |
    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)!
sitemapExclude: 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.