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Documentation

Qdrant is an AI-native vector dabatase and a semantic search engine. You can use it to extract meaningful information from unstructured data. Want to see how it works? Clone this repo now and build a search engine in five minutes.

Cloud Quickstart	Local Quickstart

Ready to start developing?

Qdrant is open-source and can be self-hosted. However, the quickest way to get started is with our free tier on Qdrant Cloud. It scales easily and provides an UI where you can interact with data.

Qdrant's most popular features:

Filtrable HNSW Single-stage payload filtering	Recommendations & Context Search Exploratory advanced search	Pure-Vector Hybrid Search Full text and semantic search in one
Multitenancy Payload-based partitioning	Custom Sharding For data isolation and distribution	Role Based Access Control Secure JWT-based access
Quantization Compress data for drastic speedups	Multivector Support For ColBERT late interaction	Built-in IDF Advanced similarity calculation

Developer guidebooks:

A Complete Guide to Filtering in Vector Search Beginner & advanced examples showing how to improve precision in vector search.	Building Hybrid Search with Query API Build a pure vector-based hybrid search system with our new fusion feature.
Multitenancy and Sharding: Best Practices Combine two powerful features for complete data isolation and scaling.	Benefits of Binary Quantization in Vector Search Compress data points while retaining essential meaning for extreme search performance.