Embeddings convert complex data into dense vectors (arrays of numbers) that capture semantic meaning. Similar items have similar embeddings in vector space. Text embeddings are generated by models like text-embedding-ada-002 or nomic-embed-text. Used for similarity search, clustering, classification, and RAG systems. Typically 768-1536 dimensions.
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Embedding
Numerical vector representation of text, images, or other data for machine learning.
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</> Related Terms
Vector Database
Specialized database for storing and searching high-dimensional vector embeddings.
pgvector
PostgreSQL extension that adds vector similarity search capabilities for AI and machine learning applications.
RAG (Retrieval-Augmented Generation)
AI technique combining vector search with LLMs to provide contextual answers from custom knowledge bases.
Embedding Server
A specialized server that converts text into vector representations (embeddings) for semantic search and RAG applications.