Qdrant教你用语义缓存，让AI智能体不用重复回答问题。实测57.1%命中率，响应快15ms，还省55.7% token，比直接调用LLM强多了。
Tina Sharma使用Qdrant构建语义缓存，将相同语义的不同表述问题识别并复用已有响应。该方案在基准测试中实现了57.1%的缓存命中率。缓存命中时，响应时间约为15毫秒，并减少了55.7%的token消耗。文章比较了单向量与多向量检索在语义缓存中的表现。
What if your AI Agent didn’t need to answer the same question twice? Semantic caching can recognize...
What if your AI Agent didn’t need to answer the same question twice? Semantic caching can recognize when two differently worded questions have the same meaning and reuse an existing response instead of calling the LLM again. In this practical deep dive, Tina Sharma builds a semantic cache with Qdrant, benchmarks it, tunes similarity thresholds, and compares single-vector vs. multi-vector retrieval. The results: 57.1% cache hit rate, 55.7% fewer tokens, and ~15ms response time for cache hits. Read the full article and explore the implementation: ai.gopubby.com/how-to-use-sem… 💬 0 🔄 0 ❤️ 3 👀 100 📊 1 ⚡