企业 AI 团队终于有了兼顾速度与权限的检索方案——Qdrant + Neo4j 的架构直接解决了「谁可以看什么」的治理难题，做企业级 RAG 或智能体系统的开发者值得关注。
Qdrant 在 Vector Space Day 上联合 Adobe 展示了一种结合向量搜索与图治理层的企业级检索架构。该方案通过 Qdrant 实现快速向量检索，同时利用 Neo4j 的图数据库确保检索结果符合用户身份、权限和策略。现场演示显示，同一查询会根据不同用户的治理规则返回不同结果，而不仅仅是基于相关性排序。这一架构解决了企业 AI 中检索速度与安全合规之间的核心矛盾。
Fast retrieval is table stakes. Retrieval that respects who's asking, what they're allowed to see, a...
Fast retrieval is table stakes. Retrieval that respects who's asking, what they're allowed to see, and what policies apply is a much harder problem. Murthy Chandrapaty and Ankush Gumber from @Adobe are coming to Vector Space Day to show a concrete architecture that solves it: Qdrant's vector search combined with a @neo4j graph governance layer, so agents retrieve fast and stay policy-compliant. They'll demo it live, showing how the same query returns different results for different users based on governance, not just relevance. If you're building enterprise AI, this is the architecture talk you've been waiting for. Get your ticket at luma.com/vsd-sf 💬 4 🔄 1 ❤️ 4 👀 68 📊 5 ⚡