<p>Qdrant is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points –&nbsp;vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.<br><br>Qdrant is written in Rust, which makes it reliable even under high load.<br><br>With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending</p><p>github:&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://github.com/qdrant/qdrant">https://github.com/qdrant/qdrant</a></p><p>web page:&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://qdrant.tech/">https://qdrant.tech/</a></p><h2>Demo Projects</h2><h3>Semantic Text Search</h3><p>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.&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://demo.qdrant.tech/">Try it online!</a></p><h3>Similar Image Search - Food Discovery&nbsp;🍕</h3><p>There are multiple ways to discover things, text search is not the only one. In the case of food, people rely more on appearance than description and ingredients. So why not let people choose their next lunch by its appearance, even if they don’t know the name of the dish?&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://food-discovery.qdrant.tech/">Check it out!</a></p><h2>API</h2><p>Online OpenAPI 3.0 documentation is available&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://qdrant.github.io/qdrant/redoc/index.html">here</a>. OpenAPI makes it easy to generate a client for virtually any framework or programing language.</p><p>You can also download raw OpenAPI&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://github.com/qdrant/qdrant/blob/master/openapi/openapi.yaml">definitions</a>.</p><h2>Features</h2><h2>Filtering</h2><p>Qdrant supports key-value payload associated with vectors. It does not only store payload but also allows filter results based on payload values. It allows any combinations of&nbsp;<code>should</code>,&nbsp;<code>must</code>, and&nbsp;<code>must_not</code>&nbsp;conditions, but unlike ElasticSearch post-filtering, Qdrant guarantees all relevant vectors are retrieved.</p><h2>Rich data types</h2><p>Vector payload supports a large variety of data types and query conditions, including string matching, numerical ranges, geo-locations, and more. Payload filtering conditions allow you to build almost any custom business logic that should work on top of similarity matching.</p><h2>Query planning and payload indexes</h2><p>Using the information about the stored key-value data, the&nbsp;<code>query planner</code>&nbsp;decides on the best way to execute the query. For example, if the search space limited by filters is small, it is more efficient to use a full brute force than an index.</p><h2>SIMD Hardware Acceleration</h2><p>With the&nbsp;<code>BLAS</code>&nbsp;library, Qdrant can take advantage of modern CPU architectures. It allows you to search even faster on modern hardware.</p><h2>Write-ahead logging</h2><p>Once the service confirmed an update - it won't lose data even in case of power shut down. All operations are stored in the update journal and the latest database state could be easily reconstructed at any moment.</p><h2>Stand-alone</h2><p>Qdrant does not rely on any external database or orchestration controller, which makes it very easy to configure.</p><h2>Docs</h2><ul><li>The best place to start is&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://github.com/qdrant/qdrant/blob/master/QUICK_START.md">Quick Start Guide</a></li><li>Use the&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://qdrant.github.io/qdrant/redoc/index.html">OpenAPI specification</a>&nbsp;as a reference</li><li>Follow our&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://qdrant.tech/articles/neural-search-tutorial/">Step-by-Step Tutorial</a>&nbsp;to create your first neural network project with Qdrant</li></ul><h2>Contacts</h2><ul><li>Join our&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://t.me/joinchat/sIuUArGQRp9kMTUy">Telegram group</a></li><li>Follow us on&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://twitter.com/qdrant_engine">Twitter</a></li><li>Subscribe to our&nbsp;<a target="_blank" rel="noopener noreferrer" href="https://tech.us1.list-manage.com/subscribe/post?u=69617d79374ac6280dd2230b2&amp;id=acb2b876fc">Newsletters</a></li><li>Write us an email&nbsp;<a href="mailto:info@qdrant.tech">info@qdrant.tech</a></li><li>slack&nbsp;ods.ai: @generall or #tool_qdrant</li></ul><p>&nbsp;</p>

Andrey Vasnetsov

Vector Similarity Search Engine with extended filtering support

Qdrant – Neural SearchFounded 4 years ago

Qdrant – Neural Search
Founded 4 years ago