Vector Database

A vector database stores embeddings and does similarity search, acting as the storage layer behind RAG and semantic search that finds the most relevant pieces.

A vector database stores the embeddings of text, images and more, searching by similarity instead of keywords to match meaning even when wording differs. It underpins semantic search, recommendation and RAG, using embeddings to place questions and documents in one space and pull back the closest passages fast.

What is a vector database? Why it always comes with RAG

Vector databases have become almost a high-frequency word in RAG solutions, and many people think it's a heavy infrastructure. In fact, from a functio...

Admin 4/7/2026

What is Embedding? Why semantic search and knowledge retrieval are inseparable from it

Embedding is a low-level capability used in many AI applications, but it is not as easily perceived by ordinary users as chat models. In simple terms,...

Admin 4/7/2026

What is a vector library? Vector database explained in detail

1. Concept Vector library is a database for "vectors" (embeddings): it stores high-dimensional vectors converted from text, images, audio, and other d...

Admin 8/11/2025

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