Make the most of your Unstructured Data

Qdrant is a vector similarity engine. It deploys as an API service providing search for the nearest high-dimensional vectors.
With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!

Easy to Use API

Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality.

Fast and Accurate

Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results.

Filtrable

Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values.
Unlike Elasticsearch post-filtering, Qdrant guarantees all relevant vectors are retrieved.

Rich data types

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.

Distributed

Cloud-native and scales horizontally.
No matter how much data you need to serve - Qdrant can always be used with just the right amount of computational resources. (Currently - Enterprise only)

Efficient

Effectively utilizes your resources. Developed entirely in Rust language, Qdrant implements dynamic query planning and payload data indexing. Hardware-aware builds are also available for Enterprises.

Elevate Your Business with Qdrant

Here are just a few examples of how Qdrant can help your Business
  • Semantic Text Search
  • Similar Image Search
  • Recommendations

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.

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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. No need to train your own neural network, get started with pre-trained models.

Qdrant filters enable you to apply arbitrary business logic on top of a similarity search. Look for similar clothes cheaper than $20? Search for a similar artwork published in the last year? Qdrant handles all possible conditions!

For the Demo, we put together a food discovery service - it will show you a lunch suggestion based on what you visually like or dislike. It can also search for a place near you.

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User behavior can be represented as a semantic vector is similar way as text or images. This vector can represent user preferences, behavior patterns, or interest in the product.

With Qdrant, user vectors can be updated in real-time, no need to deploy a MapReduce cluster. Understand user behavior in real time.

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Upgrade your Neural Search Stack

We can integrate with anything, these are some of the featured technologies

Articles

Check out our latest publications
Neural Search Tutorial
Our step-by-step guide on how to build a neural search service with BERT + Qdrant + FastAPI.
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Metric Learning Tips & Tricks
Practical recommendations on how to train a matching model and serve it in production. Even with no labeled data.
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Filtrable HNSW
How to make ANN search with custom filtering? Search in selected subsets without loosing the results.
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