Embedding

Embeddings are dense numerical vectors (lists of numbers) that represent data in a way that captures its meaning and relationships. When text is converted to an embedding, semantically similar content produces vectors that are close together in the high-dimensional embedding space, while unrelated content produces distant vectors. This mathematical representation of meaning enables AI systems to perform similarity search, clustering, and classification.

Embedding models are trained to map inputs to vector spaces where geometric relationships correspond to semantic relationships. For example, the embedding for "king" minus "man" plus "woman" produces a vector close to "queen." Modern embedding models can capture nuanced semantic relationships across entire paragraphs and documents, not just individual words. Different embedding models are optimized for different tasks such as search, classification, or clustering.

Embeddings are a critical component of RAG systems, recommendation engines, and semantic search. When building a RAG application, you generate embeddings for your knowledge base documents and store them in a vector database. When a user asks a question, the query is embedded and compared against stored document embeddings to find the most relevant information. The quality of your embeddings directly impacts the quality of retrieval and therefore the quality of AI responses.