Unverified60% confidenceSolutionExact time
Modern RAG systems widely adopt vector retrieval technology, converting text into high-dimensional vectors via embedding models and computing cosine similarity to find relevant documents.
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Long-term
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9/2/2026
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Unverified在RAG中，文档被切分成chunk，由embedding模型将每个chunk转换为高维向量，系统通过计算向量之间的相似度（如余弦相似度）来找到与用户查询语义最接近的片段。79% similarVerifiedProduction-grade RAG systems typically build and fully fuse three retrieval paths: vector retrieval, knowledge graphs, and keyword inverted indexes.79% similarUnverifiedRetrieval-Augmented Generation (RAG) architecture decouples knowledge storage from language generation, evolving from simple vector similarity retrieval to hybrid retrieval combining BM25 and semantic vectors, iterative retrieval, and Graph RAG.78% similarUnverified工程界普遍采用RAG架构：将历史信息切分成语义块，通过嵌入模型转化为向量存入向量数据库（如Faiss、Pinecone、Weaviate），用户提问时计算余弦相似度检索最相关片段78% similarUnverifiedHierarchical RAG employs a coarse-to-fine retrieval approach, first locating relevant documents or sections at a coarse-grained level, then drilling down layer by layer to fine-grained paragraphs or sentences.77% similar
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