Unverified60% confidenceFactExact time
Embeddings map each token to a dense vector space of fixed dimensionality (e.g., 768 or 4096 dimensions), where semantically similar words are close to each other.
2
Sources
60%
Confidence
Long-term
Relevance
8/22/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedVector embeddings map text fragments to a set of floating-point numbers in a high-dimensional space, where semantically similar content is closer together in that space.78% similarUnverifiedIn vector space, each word, phrase, and abstract concept is mapped to a vector in high-dimensional space, typically ranging from hundreds to thousands of dimensions.75% similarUnverifiedThe embedding layer maps discrete tokens to a continuous vector space.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/786922API
curl https://kongchang.com/api/v1/knowledge/claims/786922MCP
get_claim(id=786922)