Verified85% confidenceFactTime unknown
The core logic of RAG is to slice documents and store them as vectors, then retrieve relevant fragments through semantic similarity at query time and inject them into the context.
5
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85%
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Long-term
Relevance
6/1/2026
First Seen
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VerifiedRAG通过将文档切分为语义块后经嵌入模型转化为高维向量存入向量数据库，查询时通过近似最近邻（ANN）搜索找到语义最相近的文档片段82% similarVerifiedRAG (Retrieval-Augmented Generation) introduces external memory during the inference phase to constrain the model's generation boundaries, by chunking documents, embedding them as vectors stored in a vector database, and retrieving relevant chunks to inject into the prompt at inference time.81% similarVerifiedRAG（检索增强生成）技术架构将文档切片、向量化、存入向量数据库，查询时先检索相关片段再交给模型生成回答81% similar
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