Unverified50% confidenceSolutionExact time
Setting differentiated weights for documents from different sources in a RAG system can be achieved through two approaches: adjusting score weights during the retrieval stage (Reranking) and explicitly specifying conflict priorities via System Prompt during the generation stage, and these two approaches can be used in combination.
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10/6/2026
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UnverifiedRAG通过检索+生成两阶段架构,先将问题转化为向量表示在向量数据库(如Faiss、Milvus、Chroma)中进行语义检索,弥补大模型知识固化和无法感知私有信息的缺陷79% 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78% similarUnverifiedThe core engineering pipeline of a RAG system includes document chunking strategy, embedding model selection and vector database deployment, hybrid retrieval (semantic retrieval + BM25 keyword retrieval), and Reranker reranking.77% similarUnverifiedMainstream RAG systems typically employ a hybrid retrieval strategy that combines keyword retrieval with vector retrieval and applies a reranking model for secondary filtering.77% similar
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