What is Vector database?
A vector database stores data based on meaning, not keywords. Think of it as a specialized search library that finds content by what you intend rather than the exact words you use. This is done by storing content as unique mathematical codes ('vectors') that represent its meaning.
How does it work?
We convert text or objects to embeddings and write them to the index with metadata. At query time we compute a query vector, retrieve the nearest neighbors, then filter/rerank before passing context to the model.
When does it matter? (Examples)
- RAG needs fast, relevant chunks at scale.
- You have many document types and must filter by source, date, or permissions.
- Latency and accuracy are slipping as content grows.
Benefits
- Finds relevant context fast
- Scales with data growth
- Supports fine-grained filters
Risks
- Index bloat and cost growth
- Drift from poor re‑indexing
- Security gaps without metadata controls
Antire and Vector database
We choose and tune vector stores, set metadata and filters, and design refresh jobs so search stays accurate and affordable.
Services
Data platforms and applied AI.
Related words
Semantic search
Data lakehouse