Image embeddings convert visual content into dense vector representations that capture semantic and visual features, enabling similarity search, classification, and retrieval. Core Idea Image embeddings map images to fixed-size vectors in a high-dimensional space where semantically similar images are close together. …

Retrieval-Augmented Generation techniques for enhancing LLM responses with external knowledge. Core Idea RAG combines retrieval from external knowledge bases with LLM generation to produce accurate, up-to-date responses without retraining the model. Mathematical Foundation The core retrieval mechanism uses cosine …

Audio embeddings convert sound signals (speech, music, environmental sounds) into dense vector representations that capture acoustic and semantic features, enabling similarity search, classification, and retrieval. Core Idea Audio embeddings map audio waveforms or spectrograms to fixed-size vectors in a …

Text embeddings convert textual content into dense vector representations that capture semantic meaning, enabling similarity search, classification, and retrieval in natural language processing. Core Idea Text embeddings map text sequences (words, sentences, documents) to fixed-size vectors in a high-dimensional space …