Unverified60% confidenceSolutionExact time
Semantic caching converts queries into vectors via an embedding model and performs similarity search in the vector space, returning cached results directly when the similarity exceeds a set threshold.
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60%
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Long-term
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9/1/2026
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Unverified在特定垂直领域语料上微调的Embedding模型往往能提升同义查询相似度得分，降低阈值误判率76% similarUnverifiedIn RAG, unstructured data is converted into high-dimensional numerical vectors by an Embedding Model, and the distance between vectors in mathematical space represents the semantic similarity of the content.73% similarUnverifiedVeloce-Engine's semantic cache uses an embedding model to encode prompts into vectors and determines functional equivalence via measures such as cosine similarity to hit the cache.73% similarUnverifiedReranking 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.72% similarUnverifiedContrastive learning is the core paradigm for fine-tuning dense retrieval models, pulling a query and its positive document closer in vector space while pushing the query away from other documents in the batch (negatives).72% similar
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