Qdrant - Vector Database

Bayer is a global life sciences company operating at the intersection of two of the most consequential fields in human life: health and nutrition. Its pharmaceutical work supports drug discovery and patient care, while its crop science work supports food production at planetary scale. The company’s guiding ambition, “Health for all, hunger for none,” frames how it thi...

Every crawl, retried job, and embedding pipeline change writes points into a vector collection. The stored data keeps moving even when the query code never changes, and the top results move with it.

At first, little looks wrong. Search returns results, and latency stays normal. In our baseline run, the context-relevance score sat at 0.92 out of 1.00 while four answers ...

Adding a metadata filter to vector search can make good results disappear without making the query look broken. It still runs fast, returns something, and keeps the dashboards quiet, while some of the true nearest matches drop out. In the benchmark behind this post, a broad-value filter lowers recall to 90.8% and an AND filter over two broad values lowers it to 39.7%, while e...

Filtered vector search breaks when metadata filters turn a healthy nearest-neighbor graph into scattered islands. HNSW’s m parameter controls how many links each point gets. At Qdrant’s default m=16, the one-million-point collection benchmarked below averaged about 21 links per node on layer 0. Filter out 96% of the points and fewer than one link per node survives on average,...

Qdrant 1.19.0 is out! Let’s look at the main features for this version:

TurboQuant Datatype: A new storage format that compresses vectors to four bits without keeping their original full-precision representation, reducing sto...