mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-25 14:08:30 +02:00
seo text changes, also removed obsolete langauge switch
This commit is contained in:
azayarni
@@ -1,5 +1,6 @@
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---
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title: Articles
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page_title: Articles about Vector Search
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description: Articles about vector search and similarity larning related topics. Latest updates on Qdrant vector search engine.
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section_title: Check out our latest publications
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subtitle: Check out our latest publications
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@@ -1,5 +1,5 @@
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---
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title: Benchmarks
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title: Vector Database Benchmarks
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description: The first comparative benchmark and benchmarking framework for vector search engines and vector databases.
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keywords:
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- vector databases comparative benchmark
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@@ -1,6 +1,6 @@
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---
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page_title: Vector Search Demos and Examples
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description: Interactive examples and demos of vector search based applications developed with Qdrant vector search engine.
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title: Demos
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title: Vector Search Demos
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section_title: Interactive Live Examples
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---
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@@ -1,8 +1,8 @@
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---
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draft: false
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title: E-commerce products categorization
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short_description: E-commerce products categorization demo from Qdrant
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description: This demo shows how you can use vector search in e-commerce. Enter the name of the product and the application will understand which category it belongs to, based on the multi-language model. The dots represent clusters of products.
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short_description: E-commerce products categorization demo from Qdrant vector database
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description: This demo shows how you can use vector databse in e-commerce. Enter the name of the product and the application will understand which category it belongs to, based on the multi-language model. The dots represent clusters of products.
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preview_image: /demo/products_categorization_demo.jpg
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link: https://qdrant.to/extreme-classification-demo
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weight: 3
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@@ -4,6 +4,6 @@ icon:
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sitemapExclude: True
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---
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Qdrant is a vector similarity engine.
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Qdrant is a vector similarity engine & vector database.
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It deploys as an API service providing search for the nearest high-dimensional vectors.\
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With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!
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@@ -1,7 +1,7 @@
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---
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page_title: Vector Search Solutions
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title: Solutions
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title: Vector Search Solutions
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section_title: Challenges and tasks solved with Qdrant
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subtitle: Here are just a few examples of how Qdrant can help your Business
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subtitle: Here are just a few examples of how Qdrant vector search database can help your Business
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description: Elevate your business with vector search and vector databse. Tasks and challenges solved with Qdrant vector search engine.
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---
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@@ -10,13 +10,13 @@ default_link: https://qdrant.to/food-discovery
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default_link_name: Demo
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weight: 10
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short_description: |
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Find similar images, detect duplicates, or even find a picture by text description - all of that you can do with Qdrant.
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Find similar images, detect duplicates, or even find a picture by text description - all of that you can do with Qdrant vector database.
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Start with pre-trained models and [fine-tune](https://github.com/qdrant/quaterion) them for better accuracy.
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Check out our [demo](https://qdrant.to/food-discovery)!
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sitemapExclude: True
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---
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Sometimes text search is not enough. Qdrant allows you to find similar images, detect duplicates, or even find a picture by text description.
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Sometimes text search is not enough. Qdrant vector database allows you to find similar images, detect duplicates, or even find a picture by text description.
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Qdrant filters enable you to apply arbitrary business logic on top of a similarity search.
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Look for similar clothes cheaper than $20? Search for a similar artwork published in the last year?
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weight: 30
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short_description: |
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User behavior can be represented as a semantic vector is similar way as text or images.
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Qdrant allows you to create a real-time recommendation engine.
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Vector database allows you to create a real-time recommendation engine.
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No MapReduce cluster required.
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sitemapExclude: True
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---
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@@ -21,5 +21,5 @@ This vector can represent user preferences, behavior patterns, or interest in th
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With Qdrant, user vectors can be updated in real-time, no need to deploy a MapReduce cluster.
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With Qdrant vector database, user vectors can be updated in real-time, no need to deploy a MapReduce cluster.
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Understand user behavior in real time.
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@@ -10,7 +10,7 @@ default_link: https://qdrant.to/semantic-search-demo
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default_link_name: Demo
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weight: 20
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short_description: |
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The neural search uses **semantic embeddings** instead of keywords and works best with short texts.
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The vector search uses **semantic embeddings** instead of keywords and works best with short texts.
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With Qdrant, you can build and deploy semantic neural search on your data in minutes.
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Check out our [demo](https://qdrant.to/semantic-search-demo)!
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sitemapExclude: True
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@@ -22,5 +22,5 @@ Documents may have too few keywords, or queries might be too large.
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One way to overcome these problems is a neural network-based semantic search, which can be used in conjunction with traditional search.
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The neural search uses **semantic embeddings** to find texts with similar meaning.
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With Qdrant, you can build and deploy semantic neural search on your data in minutes!
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With Qdrant vector search engine, you can build and deploy semantic neural search on your data in minutes!
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Compare the results of a semantic and full-text search in our demo.
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@@ -1,5 +1,5 @@
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---
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title: Upgrade your Neural Search Stack
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subtitle: We can integrate with anything, these are some of the featured technologies
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subtitle: Qdrant vector search engine can integrate with anything, these are some of the featured technologies
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sitemapExclude: True
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---
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@@ -1,5 +1,5 @@
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---
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title: Use Cases
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title: Vector Database Use Cases
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section_title: Apps and Ideas Qdrant made possible
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type: page
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description: Applications, business cases and startup ideas you can build with Qdrant vector search engine.
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@@ -5,5 +5,5 @@ sitemapExclude: True
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---
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User interests cannot be described with rules, and that's where neural networks come in.
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Qdrant will allow sufficient flexibility in neural network recommendations so that each user sees only the relevant ad.
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Qdrant vector databse will allow sufficient flexibility in neural network recommendations so that each user sees only the relevant ad.
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Advanced filtering mechanisms, such as geo-location, do not compromise on speed and accuracy, which is especially important for online advertising. Check out [research article by Twitter](https://www.sciencedirect.com/science/article/abs/pii/S0925231217308445).
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@@ -7,6 +7,6 @@ sitemapExclude: True
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Fraud detection is like recommendations in reverse.
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One way to solve the problem is to look for similar cheating behaviors.
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But often this is not enough and manual rules come into play.
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Qdrant allows you to combine both approaches because it provides a way to filter the result using arbitrary conditions.
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Qdrant vector database allows you to combine both approaches because it provides a way to filter the result using arbitrary conditions.
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And all this can happen in the time till the client takes his hand off the terminal.
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Here is some related [research paper](https://arxiv.org/abs/1808.05492).
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@@ -5,6 +5,6 @@ weight: 10
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sitemapExclude: True
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---
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Neural search can be used to match candidates and jobs even if there are no matching keywords or explicit skill descriptions.
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Vector search engine can be used to match candidates and jobs even if there are no matching keywords or explicit skill descriptions.
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For example, it can automatically map **'frontend engineer'** to **'web developer'**, no need for any predefined categorization.
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Neural job matching is used at [MoBerries](https://www.moberries.com/) for automatic job recommendations.
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@@ -6,4 +6,4 @@ sitemapExclude: True
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The wording of court decisions can be difficult not only for ordinary people, but sometimes for the lawyers themselves.
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It is rare to find words that exactly match a similar precedent.
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That's where AI, which has seen hundreds of thousands of court decisions and can compare them, can help. Here is some related [research](https://arxiv.org/abs/2004.12307).
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That's where AI, which has seen hundreds of thousands of court decisions and can compare them using vector similarity search engine, can help. Here is some related [research](https://arxiv.org/abs/2004.12307).
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@@ -110,7 +110,7 @@ docker run -p 6333:6333 qdrant/qdrant</code>
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</div>
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<div class="inner-container">
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<!-- Business Info Tabs -->
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<div class="business-info-tabs splide" aria-label="How Qdrant can help your Business"
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<div class="business-info-tabs splide" aria-label="How Qdrant vector database can help your Business"
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data-splide='{
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"direction": "ttb",
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"type": "loop",
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@@ -4,7 +4,7 @@
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<div class="row">
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<div class="col-12 sec-title">
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<h2>Get Updates from Qdrant</h2>
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<div class="text">We will update you on new features and news regarding Qdrant
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<div class="text">We will update you on new features and news regarding Qdrant and Vector Similarity Search
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</div>
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</div>
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<div class="content-column col-12">
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@@ -99,19 +99,6 @@
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<div class="copyright">© {{ now.Format "2006" }} Qdrant. All Rights Reserved</div>
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</div>
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<!-- Language Column -->
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<div class="language-column col-lg-6 col-md-12 col-sm-12">
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<!--Language-->
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<div class="language dropdown"><a class="btn btn-default dropdown-toggle" id="dropdownMenu2"
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data-toggle="dropdown" aria-haspopup="true" aria-expanded="true"
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href="#">Language:
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English <span class="icon fa fa-angle-down"></span></a>
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<ul class="dropdown-menu style-one" aria-labelledby="dropdownMenu2">
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<li><a href="#">English</a></li>
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</ul>
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</div>
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</div>
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</div>
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</div>
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</div>
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