Embeddings

Embeddings are numerical representations of text, images, or other data that capture semantic meaning and relationships in a format that machine learning models can process. They transform human-readable content into high-dimensional vectors of numbers, where similar items have similar numerical representations. Text embeddings work by converting words, phrases, or documents into arrays of numbers, typically ranging from 100 to 3,000+ dimensions depending on the embedding model. The key insight is that embeddings capture meaning: words with similar meanings have embeddings that are close together in the vector space, while unrelated words have embeddings far apart. For example, "king" and "queen" would have similar embeddings, as would "Paris" and "France." Embeddings are created by training neural networks on large amounts of text data to predict relationships between words and concepts. Popular embedding models include Word2Vec, GloVe, and more recently, transformer-based models like those from OpenAI and other providers. Modern embedding models can capture nuanced semantic relationships and work across multiple languages. Embeddings have numerous applications: semantic search (finding similar documents), clustering (grouping related items), recommendation systems (finding similar products or content), and as input features for machine learning models. They're particularly useful for retrieval-augmented generation (RAG), where embeddings help find relevant context for language models. By converting text to embeddings, systems can perform mathematical operations to measure similarity, find patterns, and make intelligent connections that would be difficult with raw text alone.