Vector Database

A vector database is a database purpose-built for storing and querying vector embeddings — the numerical representations that AI models use to capture the meaning of text, images, and other data. Unlike traditional databases that search by exact matches or keyword patterns, vector databases find results based on semantic similarity. When you search a vector database, you provide a query vector (an embedding of your search query), and the database returns the stored vectors that are most similar to it. This enables searching by meaning — finding documents about "reducing employee turnover" even when the stored documents use phrases like "improving staff retention".

Vector databases use specialised indexing algorithms to make similarity search fast and efficient. The most common approach is Approximate Nearest Neighbour (ANN) search, which trades a small amount of accuracy for dramatically faster query times. Algorithms like HNSW (Hierarchical Navigable Small World) and IVF (Inverted File Index) organise vectors into structures that allow the database to quickly narrow down candidates without comparing every stored vector. Beyond pure vector search, modern vector databases support hybrid search (combining vector similarity with traditional keyword filtering), metadata filtering (narrowing results by attributes like date, category, or source), and multi-tenancy (isolating data between different users or applications). Popular vector databases include Pinecone, Weaviate, Qdrant, Milvus, and ChromaDB. Traditional databases like PostgreSQL (with the pgvector extension) also offer vector search capabilities, which can be sufficient for smaller-scale applications.

Vector databases are essential infrastructure for any AI application that needs to search or retrieve information by meaning. They are the backbone of RAG systems, powering the retrieval step that finds relevant documents to ground LLM responses in factual data. For businesses, vector databases transform how information is accessed. Instead of relying on employees to know the right keywords or navigate complex folder structures, a vector database enables natural language queries across the entire knowledge base. This dramatically improves information discovery and reduces the time spent searching for answers. The choice of vector database impacts application performance, cost, and scalability. Managed cloud solutions offer simplicity but have ongoing costs, while self-hosted options provide more control and can be more economical at scale.

Vector databases power RAG pipelines for enterprise AI assistants, semantic search engines that understand user intent, recommendation systems that surface similar products or content, and anomaly detection systems that identify unusual patterns. They are also used for deduplication (finding near-duplicate documents or records), image search (finding visually similar images), and customer support routing (matching incoming queries to the most relevant response templates or knowledge articles). Any application that needs to find "things like this" rather than "things matching exactly this" benefits from vector database technology.