retrieval augmented generation

5 stories

Stories

Semantic Caching for RAG
Build a Semantic Cache for a RAG Pipeline Time: 30 min Level: Intermediate What’s a Semantic Cache Traditional caches operate on an exact match basis, while semantic caches search for the meaning of the key rather than an exact match.
QdrantOct 9, 202621 min read
SHIFTing Languages in Multilingual RAG
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.
QdrantSep 16, 202611 min read
Qdrant and Minima Deliver 2.92x More Agentic RAG Tasks per GPU-Hour
Reducing Retrieval and Calls When a retrieval-augmented generation (RAG) agent runs, it often has to plan a search, check the evidence it gets back, and try again when that evidence falls short. Those inefficiencies compound.
QdrantAug 13, 20268 min read
How Bayer Built an Enterprise-Scale Search Engine with Qdrant
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.
QdrantAug 13, 202620 min read
Is RAG Dead? Why Long Context Windows Don't Replace RAG
Is RAG Dead? The Role of Vector Search in AI Efficiency When Anthropic came out with a context window of 100K tokens, they said: “Vector search is dead. LLMs are getting more accurate and won’t need RAG anymore.” Google’s Gemini 1.5 now offers a context window of 10 million tokens.
QdrantAug 4, 20265 min read