Unverified50% confidenceFactExact time
Reranking models score the query and document together, capturing finer-grained relevance signals, but inference must be run in real time at query time, resulting in higher latency.
1
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50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Building a Precise AI Agent Knowledge Base: Hybrid Retrieval and Reranking in Practice
bilibili双语对照翻译10/19/2026
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UnverifiedIn multi-hop reasoning scenarios, each hop's retrieval should incrementally supplement based on the conclusions of the previous hop, and the decision model can explicitly track the known information set to avoid redundant recall.76% similarUnverifiedThe bi-encoder model encodes queries and documents into independent vectors respectively; document vectors can be pre-computed offline and indexed, enabling fast retrieval but unable to model token-level interactions.76% similarUnverifiedEmbedding models convert queries and candidate documents into vector representations, thereby measuring relevance in a semantic space.75% similarUnverifiedBy having the large model ask follow-up questions, Q&A continuously accumulates in the context, and only when the contextual information is sufficiently rich will the model generate more reliable solutions.75% similarUnverifiedReranker模型（如Cohere Rerank、BGE-Reranker、bce-reranker）同时考虑Query和Document的交互信息，排序精度更高但计算成本也更大74% similar
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