株式会社オブライト

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.

Related Columns

Network & Infrastructure

Amazon S3 Vectors Complete Guide — Reduce AI/RAG Costs by 90% with Native Vector Search Storage [2026]

Complete guide to Amazon S3 Vectors (GA since December 2025). Covers up to 90% cost reduction vs dedicated vector DBs, 2-billion vectors per index, RAG with Bedrock Knowledge Bases, and Python code examples.

Software Development

Building Internal Knowledge Search with OpenClaw: RAG-Powered AI Agent Guide

Learn how to build a high-accuracy internal knowledge search system using OpenClaw and RAG (Retrieval-Augmented Generation). This guide covers local vector database setup with ChromaDB, Qdrant, and Weaviate, document indexing strategies, and practical deployment for searching across PDFs, Word documents, and internal wikis.

Building Internal Knowledge Search with Qwen3.5-9B & RAG: Enterprise Data AI Guide

A comprehensive guide to building an internal knowledge search system with Qwen3.5-9B and RAG. Covers document ingestion, Japanese-optimized embeddings, vector database selection, chunking strategies, 262K context utilization, citation tracking, and accuracy evaluation methodology.

Building RAG-Enabled Customer Support AI with Ollama and OpenClaw

This article explains how to build a RAG (Retrieval-Augmented Generation) customer support system by combining Ollama's embedding models with OpenClaw agents. Through vector database integration, you can generate accurate answers from FAQ documents and deploy AI support across multiple channels like LINE and Slack.

Related Terms

Embedding RAG (Retrieval-Augmented Generation)Semantic Search GraphRAG Grounding

Feel free to contact us