Learn how text embeddings transform words and sentences into numerical vectors, enabling semantic search and AI-powered information retrieval.
More about Text Embedding
Text Embedding is the process of converting text (words, sentences, or documents) into dense numerical vectors that capture semantic meaning. Similar texts produce similar vectors, enabling semantic search where the AI finds conceptually related content even without exact keyword matches.
Embeddings are fundamental to RAG systems and vector databases. Popular embedding models include OpenAI's text-embedding-ada-002, Cohere's embed, and open-source options like sentence-transformers.
