Qdrant (read: quadrant) is a vector similarity search engine. Use our documentation to develop a production-ready service with a convenient API to store, search, and manage vectors with an additional payload. Qdrant’s expanding features allow for all sorts of neural network or semantic-based matching, faceted search, and other applications.

Product Release: Announcing Qdrant Hybrid Cloud!

Now you can attach your own infrastructure to Qdrant Cloud!

Hybrid Cloud

Use Qdrant Hybrid Cloud to build the best private environment that suits your needs. Manage your own clusters via the Qdrant Cloud UI, but continue to run them within your own private infrastructure for complete security and sovereignty.

First-Time Users:

There are three ways to use Qdrant:

  1. Run a Docker image if you don’t have a Python development environment. Setup a local Qdrant server and storage in a few moments.
  2. Get the Python client if you’re familiar with Python. Just pip install qdrant-client. The client also supports an in-memory database.
  3. Spin up a Qdrant Cloud cluster: the recommended method to run Qdrant in production. Read Quickstart to setup your first instance.

Local mode workflow

First, try Qdrant locally using the Qdrant Client and with the help of our Tutorials and Guides. Develop a sample app from our Examples list and try it using a Qdrant Docker container. Then, when you are ready for production, deploy to a Free Tier Qdrant Cloud cluster.

Try Qdrant with Practice Data:

You may always use our Practice Datasets to build with Qdrant. This page will be regularly updated with dataset snapshots you can use to bootstrap complete projects.

InstallationDifferent ways to install Qdrant.CollectionsLearn about the central concept behind Qdrant.
ConfigurationUpdate the default configuration.Bulk UploadEfficiently upload a large number of vectors.
OptimizationOptimize Qdrant’s resource usage.MultitenancySetup Qdrant for multiple independent users.

Common Use Cases:

Qdrant is ideal for deploying applications based on the matching of embeddings produced by neural network encoders. Check out the Examples section to learn more about common use cases. Also, you can visit the Tutorials page to learn how to work with Qdrant in different ways.

Use CaseDescriptionStack
Semantic Search for BeginnersBuild a search engine locally with our most basic instruction set.Qdrant
Build a Simple Neural SearchBuild and deploy a neural search. Check out the live demo app.Qdrant, BERT, FastAPI
Build a Search with Aleph AlphaBuild a simple semantic search that combines text and image data.Qdrant, Aleph Alpha
Developing Recommendations SystemsLearn how to get started building semantic search and recommendation systems.Qdrant
Search and Recommend Newspaper ArticlesWork with text data to develop a semantic search and a recommendation engine for news articles.Qdrant
Recommendation System for SongsUse Qdrant to develop a music recommendation engine based on audio embeddings.Qdrant
Image Comparison System for Skin ConditionsUse Qdrant to compare challenging images with labels representing different skin diseases.Qdrant
Question and Answer System with LlamaIndexCombine Qdrant and LlamaIndex to create a self-updating Q&A system.Qdrant, LlamaIndex, Cohere
Extractive QA SystemExtract answers directly from context to generate highly relevant answers.Qdrant
Ecommerce Reverse Image SearchAccept images as search queries to receive semantically appropriate answers.Qdrant

Qdrant Documentation