Unverified50% confidenceFactExact time
A RAG pipeline typically includes stages such as document chunking, Embedding vectorization, writing to a vector database, similarity retrieval, Top-K recall injection into the Prompt, and LLM answer generation.
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9/28/2026
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UnverifiedRAG systems adopt an "indexing-retrieval-generation" three-stage pipeline, converting document chunks into high-dimensional vectors via an embedding model and storing them in a vector database, then retrieving them via cosine similarity or approximate nearest neighbor algorithms.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77% similarUnverifiedDify 知识库模块基于 RAG 技术构建，文档被切分为 Chunk 并通过 Embedding 模型转化为向量存入向量数据库，检索时使用余弦相似度等算法进行语义匹配77% similarUnverifiedA typical RAG pipeline includes document chunking, vector embedding, vector database storage and retrieval, and final answer generation.76% similarVerifiedRAG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76% similar
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