Verified80% confidenceSolutionExact time
The RAG workflow is divided into offline and online phases: offline, documents are chunked and converted into vectors stored in a vector database; online, questions are converted into vectors and the most similar text segments are retrieved via ANN algorithms and concatenated into the context.
6
Sources
80%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
零基础学AI大模型:避开三大误区的完整学习路径
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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80% similarVerifiedRAG addresses the limitation of large models' knowledge cutoff dates by splitting private documents into text chunks, converting them into vector representations stored in a vector database, and injecting retrieved relevant content into the Prompt.80% 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80% similarUnverifiedThe complete RAG pipeline includes an offline phase (document parsing → text chunking → Embedding vectorization → writing to a vector database) and an online phase (user query → query vectorization → vector retrieval → recalled document reranking → Prompt construction → LLM response generation).80% similar
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