Embedding
A numerical vector representation of text, data, or objects that captures semantic meaning for AI processing.
In Depth
An embedding is a dense vector representation of data (text, images, or other objects) in a continuous, low-dimensional space where semantically similar items are positioned closer together. Text embeddings convert words, sentences, or documents into fixed-size numerical vectors that capture semantic meaning. For example, the embeddings for "revenue" and "sales" would be closer together than "revenue" and "temperature." Embeddings are fundamental to many AI applications: semantic search, recommendation systems, clustering, and RAG (Retrieval-Augmented Generation). Popular embedding models include OpenAI's text-embedding-3, Cohere Embed, and sentence-transformers.
How AI for Database Helps
AI for Database uses embeddings to understand the semantic meaning of your queries and match them to the most relevant tables and columns.
Related Terms
Vector Database
A database designed to store and efficiently query high-dimensional vector embeddings for similarity search.
Semantic Search
Search that understands the meaning and intent behind queries rather than just matching keywords.
RAG
Retrieval-Augmented Generation—an AI technique that enhances LLM responses by retrieving relevant context from external data sources.
Natural Language Processing
A branch of AI focused on enabling computers to understand, interpret, and generate human language.
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