做招聘系统或人才匹配的团队，这个案例展示了如何用多向量+混合搜索突破语义搜索天花板，准确率从 30% 拉到 99.993%，值得直接参考架构。
招聘本质是海量数据中的大海捞针问题，传统语义搜索无法区分产品经理和产品营销经理这类相似但不同的职位，导致候选人接受率停滞在 30% 的行业基线。Perfect_HQ 通过 Qdrant Cloud 的混合搜索和多向量表示，将每个候选人档案拆分为多个独立向量，结合 LLM 编排，实现了匹配准确率从 30% 跃升至 99.993%，客户接受率高达 95-100%。复杂多条件搜索在延迟预算内完成，从招聘者意图到活跃人才库的全流程缩短至 2 分钟以内。
Recruiting is a needle-in-a-haystack problem at massive scale: 200M+ profiles, 1B+ data points, and ...
Recruiting is a needle-in-a-haystack problem at massive scale: 200M+ profiles, 1B+ data points, and a definition of "the right candidate" that no keyword filter can express. @Perfect_HQ builds an AI recruiting workforce that runs sourcing, screening, outreach, and candidate conversations end to end. But pure semantic search hit a ceiling. A product manager and a product marketer sit next to each other in embedding space. They're not the same job. Acceptance rates plateaued at 30%, the recruiting industry baseline. @Perfect_HQ moved to Qdrant Cloud with hybrid search and multivector representations, structuring each candidate profile as a set of distinct vectors rather than a single embedding. Combined with LLM orchestration, the results: - Match accuracy from 30% to 99.993% in internal benchmarks - 95-100% acceptance rates across largest customer cohorts - Complex multi-criteria searches complete end to end within the latency budget - Full cycle from recruiter intent to live pipeline in under 2 minutes Read the full story: qdrant.tech/blog/case-stud… 💬 1 🔄 1 ❤️ 5 👀 320 📊 2 ⚡