What is a Vector Database? - Explanation & Meaning

Definition

A vector database is a specialized database system designed for storing, indexing, and searching high-dimensional vectors (embeddings), enabling fast retrieval of the most similar items based on semantic similarity.

Technical explanation

Vector databases store data as dense vectors — numerical representations generated by embedding models that capture the semantic meaning of text, images, or other data. The core problem vector databases solve is approximate nearest neighbor (ANN) search: efficiently finding the vectors closest to a query vector in high-dimensional space. Indexing algorithms such as HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), and product quantization make this possible with sub-linear complexity. Distance metrics include cosine similarity, Euclidean distance, and dot product. Popular vector databases in 2026 include Pinecone (fully managed), Weaviate (open-source with hybrid search), Qdrant (high-performance Rust-based), Milvus (enterprise-scalable), and pgvector (PostgreSQL extension for existing Postgres users). Metadata filtering enables combining vector search with traditional filters (date, category, permissions). Hybrid search combines vector and keyword search for optimal relevance.

How MG Software applies this

At MG Software, vector databases are a core component of our RAG implementations. We use pgvector for clients already working with PostgreSQL and Weaviate or Pinecone for more advanced use cases. We optimize embedding models, chunking strategies, and index configurations to ensure the best search results.

Practical examples

• A legal platform using a vector database to make millions of legal documents semantically searchable, enabling attorneys to find relevant case law based on content and context rather than exact search terms.
• A knowledge management system using Weaviate to index internal wiki pages, Slack messages, and emails, so employees can ask questions in natural language and receive the most relevant internal information instantly.
• An e-commerce platform using a vector database for visual search: customers upload a photo of a product and the vector database finds visually similar products from the catalog.

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