Embeddings
Embeddings are numeric vector representations of text (or other data) that capture meaning, so that similar content sits close together in vector space. They are what makes semantic search and retrieval possible.
An embedding turns a piece of text into a list of numbers positioned so that things with similar meaning are near each other. That lets software find content by meaning rather than exact keywords: a search for 'refund policy' can match a passage that never uses those words.
Embeddings are the engine behind retrieval. To answer from your documents, a system embeds them, stores the vectors, and at query time finds the closest passages to feed the model, which is the retrieval half of RAG.
Spojit's knowledge base uses embeddings under the hood to retrieve the right passages from your content before an agent answers.
See it in a real workflow
Spojit puts these ideas to work: describe what you want and Miraxa builds the workflow. Start free, no credit card required.