Vector Databases

Specialised database systems designed to store, index, and rapidly retrieve high-dimensional vector embeddings, enabling efficient semantic search and similarity matching essential for RAG systems and AI applications. Unlike traditional databases that match exact keywords, vector databases find conceptually similar information by measuring mathematical distances between vector representations of data. Businesses can leverage vector databases to build intelligent search systems that understand user intent, power recommendation engines based on semantic similarity, and implement RAG architectures that provide AI systems with relevant context. Applications include customer support systems that retrieve contextually relevant documentation, e-commerce platforms that recommend semantically similar products, and knowledge management tools that find related information across document collections. Leading solutions include Pinecone, Weaviate, and integrated vector search capabilities in platforms like Elasticsearch and MongoDB.