Unverified50% confidenceFactExact time
Semantic Cache uses an Embedding model to convert a query into a vector and performs similarity retrieval in a vector database, commonly using cosine similarity with a threshold typically set between 0.85 and 0.95; if a hit occurs, the inference chain is skipped.
1
Sources
50%
Confidence
Long-term
Relevance
10/31/2026
First Seen
Sources
Backend to AI Agent Engineer: What Are P7 Interviewers Really Testing?
bilibili大模型学习教程10/31/2026
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UnverifiedSemantic 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.80% similarUnverified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70% similarUnverifiedTokens per second (tokens/s) measures throughput in the decoding stage and does not directly equal the model's performance on complex reasoning tasks; a high-speed version may perform extremely well on tasks such as code completion but is not necessarily better than the main model on long-chain reasoning tasks.67% 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.67% similarUnverifiedRecency re-ranking combines timestamps or version numbers as additional signals with semantic similarity scores through weighted merging, with a common formula of final_score = α × semantic_score + (1-α) × recency_score.67% similar
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