Unverified60% confidenceFactExact time
Semantic retrieval technology converts Skill description text into high-dimensional vectors via an embedding model and stores them in a vector database, retrieving the best-matching skills through algorithms such as cosine similarity.
2
Sources
60%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
Agent Skills完全指南：原理、结构与实战案例解析
Related Claims
Verified向量数据库将文本内容通过嵌入模型（Embedding Model）转化为高维数值向量，检索时通过余弦相似度等算法找出语义最接近的内容85% similarVerifiedLarge language models, through self-supervised learning on massive text data, are essentially complex statistical inductors that excel at capturing the central tendencies of existing human knowledge78% similarUnverifiedThe core technology behind AI-based natural language search is Embedding (vector embedding), which converts text into high-dimensional vectors and performs similarity searches in a vector database.77% similarVerifiedVector databases achieve semantic retrieval by embedding text as high-dimensional vectors and computing cosine similarity, but lack the ability to express structured relationships.77% similarVerifiedSemantic search converts text into high-dimensional vectors and finds the most relevant conclusions by computing cosine similarity, allowing queries phrased in different ways to match semantically equivalent results.77% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/507108API
curl https://kongchang.com/api/v1/knowledge/claims/507108MCP
get_claim(id=507108)