Unverified50% confidenceSolutionExact time
对于事实性、关键词导向的智能体记忆场景,基于关键词的 FTS5 检索完全够用,且响应更快、成本几乎为零
1
Sources
50%
Confidence
Long-term
Relevance
8/26/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedFTS5 依赖字面匹配,当查询词与存储内容用词不同但语义相近时可能漏检,是纯全文方案的固有局限68% similarUnverified语义缓存更适合作为无状态、事实性查询的优化手段,而非对所有请求一刀切地应用66% similarUnverified投机解码主要优化TPOT(每Token生成耗时)指标,在低并发Memory-bound场景下将闲置算力转化为草稿验证的并行计算,单用户速率提升幅度显著65% similarUnverifiedTencent Hunyuan open-sourced an OCR text recognition model that, after full-stack operator optimization, delivers a 6.8x inference speedup without affecting handwriting recognition accuracy.65% similarUnverifiedSome emerging APIs support Neural Search, which uses vector embeddings to represent query semantics and performs better when handling colloquial and long-tail queries.64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/804544API
curl https://kongchang.com/api/v1/knowledge/claims/804544MCP
get_claim(id=804544)