株式会社オブライト

AI2026-05-17

Embedding

Also known as: Embedding / 埋め込み表現 / ベクトル埋め込み

The transformation of text or other data into high-dimensional vectors where semantic proximity is preserved — the core representation technique underlying RAG, semantic search, and recommendation systems.

Overview

Embedding converts words, sentences, documents, or images into numeric vectors where semantically similar items cluster together. Cosine similarity or dot-product distance then enables nearest-neighbor lookup. Popular models include OpenAI text-embedding-3, BGE, and E5.

Applications

RAG document retrieval, semantic search, duplicate detection, document clustering, and recommendation engines all depend on embeddings. Storing vectors in a purpose-built vector database makes sub-second similarity search feasible at scale.

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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.

Network & Infrastructure

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

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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

Vector Database Semantic Search RAG (Retrieval-Augmented Generation)Tokenization GraphRAG

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