A vector database stores and indexes high-dimensional vectors (embeddings) for fast similarity search. It enables semantic search where queries find conceptually similar content, not just keyword matches.
How It Works
- Embed: Convert text to vectors using embedding models
- Store: Index vectors in the database
- Query: Convert query to vector
- Search: Find nearest neighbors by similarity
Use Cases
- RAG retrieval for LLM applications
- Semantic search
- Recommendation systems
- Duplicate detection
Why use for RAG?
Vector databases enable semantic retrieval—finding documents by meaning rather than keywords. This is essential for RAG because user queries rarely match document text exactly. The database finds conceptually relevant content to include in the LLM prompt.
Practical example
A search index stores document embeddings together with version and access metadata.
What to check
Evaluate retrieval quality, metadata filtering, deletion, index freshness, and permission handling.
Limits and tradeoffs
Deleting the source document may not remove every derived vector or cached result without an explicit workflow.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.