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AI2026-05-17

Vector Database

Also known as: Vector Database / ベクトルデータベース / Vector DB

A database purpose-built to store high-dimensional embedding vectors and return nearest-neighbor results via approximate search (ANN) — the core storage layer in most RAG architectures.

Overview

A vector database stores high-dimensional embeddings and returns semantically similar documents for a query vector using approximate nearest-neighbor (ANN) search. Purpose-built options include Pinecone, Weaviate, Qdrant, and Chroma. Managed alternatives such as pgvector and Amazon S3 Vectors lower operational overhead.

Role in RAG

In a RAG pipeline, documents are pre-embedded and stored in the vector DB. At query time the user input is embedded, ANN search retrieves the top-N relevant chunks, and those chunks are injected into the LLM prompt — grounding the response in current or proprietary data.

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Related Terms

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