Unverified50% confidenceFactExact time
Embedding models do not output text; they only convert inputs into vectors, whose meaning lies in their relative relationships to one another (the embedding space).
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50%
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Long-term
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10/8/2026
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Unverified嵌入模型的作用是把文本转换为高维向量，让机器能够以数学方式衡量语义相似度74% similarVerifiedVector embedding is the process of transforming discrete text, code, or experiences into numerical vectors in a continuous high-dimensional space, so that semantically similar content is closer in vector space.71% similarVerifiedEmbedding models convert text into fixed-dimensional floating-point vectors, placing semantically similar content closer together in the vector space, and serve as the core foundation of RAG technology.69% similarUnverified语义缓存通过将用户请求转化为向量嵌入，在向量空间中计算语义相似度，将语义相近的问题映射到同一缓存结果，从而减少模型调用次数和Token消耗69% similarUnverifiedLarge language models encode semantic concepts as specific directional vectors in a high-dimensional representation space, with refusal behavior corresponding to an identifiable direction.68% similar
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