Vector Database

Data
About 1 min read

A specialized database that stores high-dimensional vector data and supports high-speed similarity-based searches. By utilizing embedding values that quantify unstructured data, it enables AI to quickly locate semantically similar information and serves as a core infrastructure for Retrieval-Augmented Generation (RAG) systems.

Also known as
Vector DatabaseVector DBVector Store

Detailed explanation

Vector databases convert and store unstructured data—such as text, images, and audio—into high-dimensional numerical values (embeddings) that AI models can understand. Unlike traditional relational databases that determine whether exact values match, vector databases specialize in finding 'semantically similar' data through mathematical distance calculations like cosine similarity. To guarantee fast response times even across massive datasets, they utilize dedicated indexing techniques such as Approximate Nearest Neighbor (ANN) algorithms and HNSW. Recently, to improve search accuracy, they have begun to offer hybrid search features that combine traditional keyword search as a necessity. In modern generative AI environments, they serve as an external memory that supplements the LLM's lack of real-time knowledge and play a pivotal role in RAG systems.

Why it matters in tool selection

The response quality of an AI service depends on how accurately and quickly it can retrieve the required information. Vector databases find contextually similar information among billions of data points in milliseconds, preventing LLM hallucinations and forming the basis for implementing personalized recommendation features.

What to look for

  • Horizontal scalability depending on data volume (millions vs. billions of records)
  • Whether it is a fully managed (SaaS) service or an open-source option that requires self-hosting
  • Support for hybrid search, which queries keywords and vectors simultaneously
  • Tuning options to balance response latency and search recall
  • Assessing whether adding vector extensions to existing data stacks (e.g., PostgreSQL, MongoDB) is sufficient

Examples

A search engine in a shoe online store where a user searches for 'cool shoes to wear in summer' and the system exposes semantically similar products—such as slippers, sandals, and mesh sneakers—at the top of the results, rather than just products containing the literal word 'cool'.

Confusing terms

Relational Database (RDBMS)

Suitable for precise data matching and transaction processing using SQL based on a predefined schema.

Vector Database

Searches for semantic similarity by calculating distances in a high-dimensional space; optimized for handling unstructured data.

Related terms

EmbeddingRAGSemantic SearchHybrid Search