26%
Prognozowany udział w pełni elektrycznych pojazdów (BEV, bez hybryd) w europejskim parku samochodowym do 2035 roku, w porównaniu z 4% w 2025 r.
BCG2035Europe
A specialized database storing embeddings, which are numerical representations of content meaning. Enables fast search for semantically similar documents, images, or text fragments for RAG and recommendation systems.
Why it matters
Enables searching by meaning, not by exact words: a query for 'promotion budget' will find a document about 'campaign costs'. It's the engine behind RAG and semantic search.
What's missing without it
Without a vector database, search only sees literal words — synonyms and paraphrases are lost, and the AI assistant has no context to draw from.
When it is used
In semantic search, AI assistants relying on proprietary knowledge, and recommendation systems for similar content.
How we use it
Finding similar articles and documentation fragments before the AI assistant responds.
Numbers worth knowing
Acronyms in data
88%
Meta podaje, że ponad 88% z 137 000 usuniętych reklam oszukańczych w Polsce zostało wykrytych automatycznie przed zgłoszeniem.
Meta09/2026Poland
2 500 000 000 000 USD
Prognozowane światowe wydatki na AI w 2026 roku.
Gartner2026global
Figures from the same field — collected in our market data base.