Unverified50% confidenceFactExact time
Vector embeddings are typically floating-point arrays of 768 or 1536 dimensions.
1
Sources
50%
Confidence
Long-term
Relevance
8/30/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedComputing the cosine similarity of a 1536-dimensional vector is essentially 1536 multiplications, 1535 additions, and several square root operations.70% similarUnverifiedA Lie algebra is the tangent space of a Lie group at the identity element, allowing multiplication on the group to be approximated by addition in a vector space.58% similarUnverifiedEmbedding models convert text into high-dimensional vectors, typically arrays of 768 or 1536 floating-point numbers.57% similarUnverifiedThe Mersenne Twister algorithm maintains a uniform distribution in 623-dimensional space.55% similarUnverifiedJohnson-Lindenstrauss引理指出在n维空间中可以找到指数级于n的近似正交向量集合55% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/825694API
curl https://kongchang.com/api/v1/knowledge/claims/825694MCP
get_claim(id=825694)