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AI Glossary

Vector Database

A database optimized for storing and querying high-dimensional vector embeddings for AI applications.

TL;DR

  • —A database optimized for storing and querying high-dimensional vector embeddings for AI applications.
  • —Understanding Vector Database is critical for effective AI for companies.
  • —Remova helps companies implement this technology safely.

In Depth

Vector databases store embedding vectors and enable fast similarity searches — finding the most relevant documents for a given query. They're essential infrastructure for RAG systems, enabling AI to retrieve relevant organizational knowledge to ground its responses.

Related Terms

Embedding

A numerical vector representation of text that captures semantic meaning for AI processing.

Retrieval-Augmented Generation (RAG)

A technique that grounds AI responses in retrieved documents to improve accuracy and reduce hallucinations.

Knowledge Graph

A structured representation of relationships between entities used to enhance AI reasoning.

Large Language Model (LLM)

A deep learning model trained on vast text datasets that can understand and generate human-like text.

Knowledge Hub

Glossary FAQs

Vector Database is a fundamental concept in the AI for companies landscape because it directly impacts how organizations manage a database optimized for storing and querying high-dimensional vector embeddings for ai applications.. Understanding this is crucial for maintaining AI security and compliance.
Remova's platform is built to natively manage and optimize Vector Database through our integrated governance layer, ensuring that your organization benefits from this technology while mitigating its inherent risks.
You can explore our full AI for companies glossary, which includes detailed definitions for related concepts like Embedding and Retrieval-Augmented Generation (RAG).

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