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In 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.
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9/17/2026
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UnverifiedEmbedding 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.74% 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.73% similarUnverifiedRAG工作流通常涉及两类模型：嵌入模型（将文本转换为向量用于相似度检索）和对话大模型（综合检索结果生成答案）71% similarUnverifiedIt is recommended to use Tongyi Qianwen's Embedding model in Dify for RAG functions such as knowledge base retrieval.66% similarUnverified多模态技术的底层原理是将不同类型数据通过各自编码器映射到统一向量空间中，使模型能跨模态理解信息64% similar
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