Unverified50% confidenceFactExact time
The Embedding model is only responsible for the text-to-vector step; which database the vectors are stored in is a relatively independent engineering choice.
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9/25/2026
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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.67% similarUnverifiedThe model adopts N-Gram Embedding, combining multiple embeddings into a local context lookup table, which can significantly increase parameter capacity with almost no increase in per-token computation.67% 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.66% similarUnverifiedEmbedding models are neural networks that map text to a continuous vector space, with the core assumption that semantically similar texts should have vectors that are close in distance.66% similarUnverified不同embedding模型生成的向量在语义空间中不可直接比较，一旦更换模型，向量库中所有历史向量都必须用新模型重新生成65% similar
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