For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
CloudGitHub
  • API Reference
LogoLogo
CloudGitHub

API Reference

Client Libraries

ClientRepositoryInstallation
pythonPythonpip install qdrant-client
typescriptTypescriptnpm install @qdrant/js-client-rest
rustRustcargo add qdrant-client
golangGogo get github.com/qdrant/go-client
.net.NETdotnet add package Qdrant.Client
javaJavaAvailable on Maven Central
Was this page helpful?

Get collection details

Next
Built with

Qdrant is a vector database and a semantic search engine. You can use its REST API to develop a production-ready service to store, search, and manage vectors with an additional payload.

How does Qdrant work?

  1. First, you should create a collection to store all your data.
  2. Then upsert data points and enrich them with a custom payload.
  3. With a full collection, run a search to find relevant results.
  4. Collections can be snapshotted, downloaded and restored.
  5. When ready, setup a distributed system for production.

Just getting started?

Try the development quickstart guide.