AI Glossarymodels

Embedding Model

A specialized AI model that converts text, images, or other data into numerical vectors (embeddings) that capture semantic meaning for search and comparison.

How It Works

Embedding models are the unsung heroes of AI applications. They power semantic search, RAG systems, recommendation engines, and clustering. Unlike generative models (which produce text or images), embedding models produce vectors — arrays of numbers that represent meaning. The key property: similar content produces similar vectors. "How do I reset my password?" and "I forgot my login credentials" would have vectors that are close together in vector space, even though they share no words. This enables finding relevant content based on meaning rather than keywords. Popular embedding models: OpenAI's text-embedding-3-small (1536 dimensions, cheap, fast), text-embedding-3-large (3072 dimensions, higher quality), Cohere's embed-v4, and open-source options like BGE and E5. For production: choose dimension size based on your accuracy-vs-storage tradeoff, batch your embedding requests, cache results (embeddings for the same text never change), and use the same model for both indexing and querying.

Common Use Cases

  • 1Powering RAG retrieval
  • 2Semantic search engines
  • 3Document deduplication
  • 4Recommendation systems
  • 5Content clustering and categorization

Related Terms

RAG (Retrieval-Augmented Generation)

A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.

Embeddings

Numerical vector representations of text that capture semantic meaning, enabling similarity search and clustering.

Vector Database

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

Semantic Search

A search approach that finds results based on meaning rather than exact keyword matches, using embeddings to understand the intent behind queries.

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