---
title: "Embedding — Glossary | LLMind"
description: "A dense vector representation of text (or other data) where semantic similarity corresponds to vector proximity."
url: https://llmind.org/glossary/embedding/
source_format: html
---
[← Glossary](https://llmind.org/glossary/)

# Embedding

**A dense vector representation of text (or other data) where semantic similarity corresponds to vector proximity.**

An embedding is a mathematical representation of meaning encoded as a vector in high-dimensional space. An embedding model maps text (a passage, a phrase, a document) to a fixed-length vector, usually 256 to 1536 dimensions. The key insight is that semantically similar texts produce vectors that are close together in this space — the cosine distance or Euclidean distance between vectors reflects semantic similarity.

## How they're made

An embedding model — often a transformer-based neural network — compresses text into a dense vector representation. The model is trained such that similar passages produce similar vectors. Common embedding models include OpenAI's text-embedding-3 (1536 dimensions), Cohere Embed, and open-source models like all-MiniLM-L6-v2 (384 dimensions) and bge-large-en (1024 dimensions).

## Common models

Embedding quality and cost vary widely. Fast models like all-MiniLM are suitable for large-scale indexing on a budget. Larger, more expensive models like OpenAI's text-embedding-3-large produce richer vectors. Most RAG systems use embeddings to power similarity search in vector databases.

## Where they fit

Embeddings power similarity search in vector DBs and are central to RAG. LLMind does not require embeddings — file-level semantic metadata is structured (human-readable, typed) rather than vectorized, enabling AI agents to read enriched files directly without similarity search.

## Related terms

-   [Vector database](https://llmind.org/glossary/vector-database/)
-   [Chunking](https://llmind.org/glossary/chunking/)
-   [RAG](https://llmind.org/glossary/rag/)

## See also

-   [Agent file access patterns](https://llmind.org/learn/agent-file-access-patterns/)
-   [Spec](https://llmind.org/spec/)