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Plain-language AI glossary

Term 12InfrastructureMeaning / context / connections

Infrastructure / Definition

Vector Database

A specialized database optimized for storing and searching high-dimensional vector embeddings, enabling semantic similarity search.

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01

MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

03

UsesWhere the concept becomes useful.

01 / Plain-language context

How Vector Database works.

Vector databases store embeddings and find similar vectors efficiently using algorithms like HNSW or IVF. When you search "how to deploy a Next.js app," the query is converted to an embedding and the database returns documents with similar embeddings, regardless of exact keyword matches. Popular options: Pinecone (managed), Weaviate (open source), pgvector (PostgreSQL extension, works with Supabase), Chroma (local development).

02 / Practical uses

Where it helps.

  1. 01RAG knowledge bases
  2. 02Semantic search engines
  3. 03Recommendation systems
  4. 04Image similarity search
03 / Keep exploring
EmbeddingsNumerical vector representations of text that capture semantic meaning, enabling similarity search and clustering.RAG (Retrieval-Augmented Generation)A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.Semantic SearchA search approach that finds results based on meaning rather than exact keyword matches, using embeddings to understand the intent behind queries.