Unstructured data embedding converts content such as notes, documents, and interaction histories into numerical representations that capture semantic similarity. A retrieval system can compare those representations to find material related to a query or task.
Embedding unstructured customer information can help an artificial intelligence agent retrieve only the account context it needs. The surrounding system should still enforce access controls, relevance thresholds, and limits on how much context is exposed.
Acronyms and aliases
embedded unstructured data variant
Related terms
Frequently asked questions
Why embed unstructured data?
Embeddings make it possible to search text and other unstructured content by semantic similarity instead of relying only on exact keyword matches.
Are embeddings the same as the original data?
No. Embeddings are numerical representations used for comparison and retrieval, while the original content is usually stored separately and fetched when needed.
