Unverified50% confidenceFactExact time
RAG 依赖向量嵌入技术，将文本转化为高维数值向量，使语义相近内容在向量空间中距离更近
1
Sources
50%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
Claude Code为何放弃RAG，选择Grep搜索代码
Related Claims
UnverifiedVector 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.79% similarUnverifiedThe RAG strategy encodes code snippets as high-dimensional semantic vectors through vector embeddings, and during retrieval computes the semantic similarity between query intent and code snippets to recall relevant code.76% similarUnverifiedRAG的向量检索依赖Embedding模型将文本编码为稠密向量，通常为512至4096维，通过余弦相似度或内积计算语义距离74% similarUnverifiedRAG (Retrieval-Augmented Generation) can be used as a strategy for segmented processing of extremely long texts.72% similar
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