Embeddings

Embeddings are mathematical representations that map discrete data such as words, sentences, documents, or images into continuous high-dimensional vector spaces where semantic similarity corresponds to geometric proximity. Two pieces of content with similar meaning will have embedding vectors that are close together in this space, enabling machines to reason about meaning and relationships in ways that traditional keyword matching cannot.

Text embeddings are generated by specialized encoder models that process input through transformer architectures to produce fixed-length vectors, typically ranging from 384 to 3072 dimensions. Leading embedding models include OpenAI text-embedding-3, Cohere Embed, Google Gecko, and open-source options from the Sentence Transformers library. The choice of embedding model significantly impacts downstream application quality, as different models vary in their handling of domain-specific terminology, multilingual content, and long documents.

The embedding generation pipeline involves preprocessing text into appropriate chunks, running it through the embedding model, and storing the resulting vectors in a vector database for efficient retrieval. Chunking strategy is critical: chunks that are too large dilute semantic specificity while chunks that are too small lose important context. Common approaches include fixed-size chunking with overlap, recursive text splitting at natural boundaries, and semantic chunking that groups thematically related content.

Embeddings serve as the foundation for numerous AI applications including semantic search, where queries are matched to documents by meaning rather than keywords; RAG systems, where relevant context is retrieved to ground language model responses; recommendation systems that surface similar content; clustering and classification tasks; and anomaly detection. Fine-tuning embedding models on domain-specific data can substantially improve retrieval quality for specialized applications, making embedding optimization a high-leverage investment for enterprise AI systems.