Unverified60% confidenceFactExact time
Embedding models are neural networks that map text to a continuous vector space, with the core assumption that semantically similar texts should have vectors that are close in distance.
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60%
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Long-term
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9/11/2026
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UnverifiedIn RAG, unstructured data is converted into high-dimensional numerical vectors by an Embedding Model, and the distance between vectors in mathematical space represents the semantic similarity of the content.74% similarUnverifiedEmbedding models and generative models are two different types of models: the former outputs fixed-dimension vectors, while the latter outputs text.73% similarUnverifiedEmbedding models map raw content into a high-dimensional vector space, where the output vectors geometrically preserve semantic relationships, with content of similar meaning corresponding to vectors that are closer in distance.71% similarUnverifiedSemantic caching converts queries into vectors via an embedding model and performs similarity search in the vector space, returning cached results directly when the similarity exceeds a set threshold.69% similarUnverified领域特化的 Embedding 模型（如代码领域的 CodeBERT）往往比通用模型在垂直场景下具有更高的语义检索精度68% similar
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