What Is a Vector Database?
Traditional databases are designed for structured data—rows and columns with precise matching. A vector database, by contrast, stores high-dimensional vectors - arrays of floating-point numbers representing meaning or features of objects.
For example:
- A sentence like “How do I reset my password?” becomes a 1,536-dimensional vector via an embedding model.
- Similar phrases (e.g., “Change login credentials”) produce nearby vectors in that same space.
When queried, the vector database retrieves the closest vectors - identifying conceptual similarity, even if exact words differ. This makes vector databases critical for semantic search, recommendation, and context retrieval for generative AI models. It allows applications to find semantically similar items (not just exact matches) by measuring distances between vectors using metrics like cosine similarity or Euclidean distance. Vector databases are foundational for AI search, recommendation systems, and Retrieval-Augmented Generation (RAG) pipelines used by LLMs (Large Language Models).
How Vector Databases Work
- Embedding Generation:
Text, image, or other data is converted into a vector using an embedding model (e.g., OpenAI, Cohere, Sentence Transformers). - Vector Storage:
Vectors are stored as arrays of numbers with associated metadata (e.g., document titles, IDs). - Indexing:
The database builds specialized structures - such as HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) - to enable fast approximate nearest-neighbor (ANN) search. - Similarity Search:
When a query vector is submitted, the database calculates distances to stored vectors and returns the most similar items. - Filtering & Ranking:
Metadata filters and scoring functions refine results for relevance. - Integration:
Results feed downstream workflows, such as chatbots, recommendation engines, or contextual retrieval for LLMs.
