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Semantic retrieval and keyword retrieval are often used together in mature RAG systems (hybrid retrieval), then results are optimized through a reranking model, to balance recall and precision.
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10/14/2026
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Unverified混合检索通过RRF等算法融合向量嵌入与关键词检索两路结果，是当前工业界RAG系统的最佳实践79% similarVerifiedReranking models such as Cohere Rerank and BGE-Reranker use a Cross-Encoder architecture to perform fine-grained relevance scoring between queries and documents, and are a key component in optimizing RAG precision.78% similarUnverified混合检索（向量检索+BM25关键词检索）和Self-RAG（模型自评估是否需要检索）是RAG的进阶技术方向76% similarUnverifiedIndustrial-grade RAG typically combines vector similarity retrieval (ANN algorithms such as HNSW) with BM25 keyword retrieval, and then uses a Rerank model (Cross-Encoder architecture) to re-rank the retrieved results.75% similarUnverifiedCommon optimization techniques for vector retrieval accuracy include adjusting chunking strategies, using reranking models (Reranker), and hybrid retrieval (keyword + semantic).74% similar
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