Embedding

Embeddings are numerical representations of data (text, images, audio, or other modalities) as vectors — arrays of floating-point numbers — in a high-dimensional space where semantically similar items are positioned close together. An embedding model converts a word, sentence, paragraph, or document into a vector of typically 768 to 4096 dimensions. The remarkable property is that these vectors capture semantic relationships: the vector for "king" minus the vector for "man" plus the vector for "woman" is close to the vector for "queen" — meaning is encoded in the geometry of the vector space. This enables semantic similarity search: finding documents that are conceptually related to a query even when they share no common keywords.

Embeddings are the technical foundation of most AI retrieval systems. In a RAG pipeline, documents are broken into chunks, each chunk is converted to an embedding vector and stored in a vector database. When a query arrives, it's embedded using the same model, and the vector database finds the stored chunks whose vectors are most similar to the query vector (measured by cosine similarity or dot product). This allows the system to retrieve documents that are semantically relevant to the query — "what are our refund policies?" retrieves the returns policy document even if the document never uses the word "refund" because the semantic meaning is captured in the embedding space. The quality of the embedding model determines how well this semantic matching works.

For B2B teams building knowledge management, search, or AI assistant products, embeddings are the capability that enables intelligent content retrieval at scale. A support knowledge base powered by embedding search can surface relevant articles for any customer question, even when the question uses different terminology than the articles. A sales intelligence platform that embeds call transcripts can find all conversations about a specific customer pain point across thousands of hours of recordings. Content discovery tools that embed a company's entire content library can suggest relevant existing assets when a team member starts drafting new content — preventing duplication and surfacing reusable material. The infrastructure cost is low (embedding APIs are inexpensive), but the value in large content libraries is high.