Qdrant
Open-source vector search engine written in Rust, with REST and gRPC APIs for collections, points and queries.
- Category
- Cloud & Data Platforms
- Type
- Vector database
- Interface
- REST API
- Built with
- Connect Any API
- Credentials
- API key
- Docs
- Qdrant API docs
02Data
What you can sync.
- Points
- Collections
- Search results
- Snapshots
Put Qdrant points next to revenue and usage, so every cost has an owner and a reason.
Need more? Name any other Qdrant data the API returns, and the AI adds it to the connector.
03Credentials
Credentials you will need.
- An API key
Qdrant uses an API key. You add the credentials once, when the AI asks for them, and it tells you where to create them in Qdrant if you need help.
04How it works
How it works in DataMind.
-
Pick Qdrant.
Open Lake, pick Qdrant in the source picker and name the connection. Connect Any API opens with a ready prompt that points the AI at the Qdrant API docs.
-
Answer a few questions.
The AI reads the Qdrant documentation, then asks which credentials you will use, which data to sync (for example points and collections), whether you want a one-time load or a recurring sync, and which accounts or filters apply.
-
Your data lands in Lake.
It builds the connector, tests it and shows you sample rows. Then Qdrant data loads into your Lake on your schedule, ready for the Warehouse, DataPrism BI and AI Minds.
