What are Vector Databases? - Definition & Meaning

Definition

Vector databases are specialized databases that store data as numeric vectors (embeddings) and support efficient similarity search. They are optimized to find the "most similar" items based on semantic likeness, which is crucial for RAG and AI recommendation systems.

Technical explanation

Vector databases index high-dimensional vectors (often 256–1536 dimensions) via algorithms such as HNSW, IVF, or brute-force ANN. They support k-nearest neighbor (k-NN) and approximate nearest neighbor (ANN) queries. Popular options include Pinecone, Weaviate, Milvus, Qdrant, Chroma, and pgvector (PostgreSQL extension). Data is converted to vectors via embedding models (OpenAI, Cohere, open-source). Hybrid search combines vector similarity with keyword matching for better relevance. Metadata filtering enables restricting searches to specific subsets.

How AVARC Solutions applies this

AVARC Solutions integrates vector databases into RAG systems, recommendation engines, and semantic search applications. We help clients choose between managed (Pinecone, Weaviate) and self-hosted (pgvector, Chroma) solutions and build indexing and retrieval pipelines that are accurate and scalable.

Practical examples

• A RAG system that stores document chunks as vectors and retrieves the most relevant chunks for context in the LLM prompt when a question is asked.
• A product recommendation system using product embeddings to find similar items based on descriptions and behavior.
• A semantic search engine that converts natural language questions into vectors and returns the most relevant documents, regardless of exact keywords.

Related terms