Qdrant - Vector Database

If you speak more than one language, you know the feeling when the mental switch in your head starts up with the rattling sound of a struggling engine, mixing every word you have to produce into some Denglish, Frenglish, or Spanglish. Work-related thoughts come back from your inner search engine of a brain in English, life wisdom – in the mother tongue, and the mix is unpredi...

We choose embedding models, dimensions, and indexes. The geometry usually comes with the package. But why use a flat space, and what else could we choose?

A manifold is the space our embeddings live in. For embeddings, we care about the geometry we give that space. It determines how we measure distance, what the shorte...

Real world vector search workloads are increasingly large and complex. Enterprises are not using vector search to occasionally search through a couple of PDF files. They are indexing and searching billions of vectors at thousands of requests per second (RPS) and sub 50ms tail latency. Large enterprises also can’t tolerate faulty assumptions.

Too many benchmarks use gat...

A growing vector collection eventually outgrows the RAM it started with: Qdrant handles that by letting you assign dense vectors, the HNSW graph, quantized vectors, payloads, and payload indexes each to whichever memory tier that structure supports, instead of forcing one RAM-versus-disk trade-off onto the whole collection.

This article will give you practical guidance...

A bulk load finishes, and the collection looks ready: every point is in, and the upload call has returned. Then the first queries land, and search takes hundreds of milliseconds, sometimes several seconds at a stretch, while Qdrant’s indexing, merge, and vacuum optimizers work through the backlog the upload left behind. How long that lasts, and what it costs each query, depen...