Embeddings

Embeddings are dense vector representations that map complex data (text, images, audio) into a high-dimensional numerical space. In this space, semantically similar items are positioned close to each other.

Applications of embeddings:

• Semantic Search: Finding documents by meaning, not just keywords
• RAG Systems: Retrieving relevant context for LLM prompts
• Recommendation Systems: Finding similar products or content
• Clustering: Grouping related items automatically
• Classification: Categorizing text or images by similarity

Popular embedding models include OpenAI’s text-embedding-3-small, Google’s Gemini embeddings, and open-source models like BERT and Sentence Transformers. Embeddings are typically stored and searched in vector databases.