Unverified50% confidenceFactExact time
The RAG workflow can be broken down into three steps: splitting external documents into text chunks and converting them into vectors via an embedding model to store in a vector database; vectorizing the user's question and performing similarity retrieval to extract the Top-K text chunks; and concatenating the text chunks into a prompt for a large language model to generate an answer.
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10/1/2026
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UnverifiedThe RAG pipeline includes text chunking, converting chunks into vectors via an embedding model, and vector retrieval.78% similarVerifiedThe 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.71% similarUnverifiedRAG 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69% similarUnverified在 RAG 系统中，Markdown 的层级结构为文档分块提供了天然的语义边界，相比纯文本按字数切分能显著提升检索召回率和生成质量67% similar
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