A database optimized for storing and searching high-dimensional embeddings — the technical foundation for semantic search and retrieval-augmented generation.
A vector database is a database optimized for storing and searching high-dimensional vector embeddings. Embeddings are numerical representations of text (or images, audio, etc.) that capture semantic meaning. Vector databases support efficient similarity search — given a query embedding, find the most semantically similar stored embeddings — which is the technical foundation for retrieval-augmented generation (RAG) and semantic search in enterprise AI.
Common vector databases include Pinecone, Weaviate, Qdrant, Milvus, Chroma, and pgvector (PostgreSQL extension). The choice between them is typically about deployment posture (managed vs self-hosted vs embedded), scale (number of vectors, query volume), and integration with the existing data infrastructure.
Vector databases are the storage layer for grounding AI agent responses in organizational knowledge. Without them, RAG-based workflows can't operate efficiently at enterprise scale.
Vector storage of internal policy documents for HR FAQ resolution
Vector storage of historical contract clauses for contract review automation
Vector storage of compliance framework content for regulatory monitoring workflows
Vector storage of historical decision records for context-aware decision support
Beth's vector storage is part of the managed infrastructure. Customers don't choose or operate the vector database directly; Huper handles selection, scaling, and operational ownership. Deployment posture for vector storage follows the customer's chosen deployment posture for the rest of the deployment.
No. Vector storage is part of Beth's managed infrastructure. The customer's role is content ingestion (or pointing to the source content); Huper handles vector storage selection and operations.
Tell us what you need. We’ll build, deploy, and manage your AI agents — on our cloud or yours.
Talk to Us