Expired60% confidenceBenchmarkExact time
The HNSW algorithm enables approximate nearest-neighbor search over hundred-million-scale vectors to complete within 10 milliseconds with a recall rate of over 98%, and is the standard index algorithm in mainstream vector databases.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/21/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 10/19/2026(expired)
Sources
Related Claims
UnverifiedHNSW(层级可导航小世界图)算法在千万级向量库中的检索延迟可控制在 10ms 以内83% similarUnverifiedMicrosoft Research Asia's MInference framework matches the most suitable sparse computation method to different attention heads, achieving up to 10x speedup in the prefill stage of million-word long contexts without reducing retrieval accuracy.74% similarUnverified在超过 1 万条内容的库中,Deepmark 搜索响应时间控制在 100 毫秒以内74% similarUnverified通过LocalStorage缓存,相同元素组合的二次查询可以在10毫秒内完成66% similarUnverifiedQuerit's search API response time can be controlled at the level of hundreds of milliseconds.64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/584813API
curl https://kongchang.com/api/v1/knowledge/claims/584813MCP
get_claim(id=584813)