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Traditional RAG solutions primarily rely on chunk-based retrieval, which splits documents into segments and returns the most relevant passages through vector similarity matching.
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8/31/2026
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UnverifiedIn RAG engineering, retrieved relevant document chunks should be prioritized at the beginning or end of the prompt, rather than stacked in order of similarity scores.78% similarUnverifiedGraph-based RAG can capture multi-hop reasoning relationships and is more precise than traditional RAG, which relies on similarity matching in vector databases.78% similarVerifiedAdvanced RAG has introduced mechanisms such as query rewriting, hybrid retrieval, and reranking to improve retrieval accuracy.77% similarUnverifiedIn the offline phase of the traditional RAG process, documents are chunked and converted into vectors via an embedding model and stored in a vector database; in the online phase, similar document chunks are retrieved using an Approximate Nearest Neighbor (ANN) algorithm.74% 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74% similar
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