📄 中文摘要
每个 RAG 管道都需要一个向量数据库。大多数代理构建者在原型开发阶段选择一个数据库后便不再重新评估，这可能导致在生产环境中出现实际的摩擦，尤其是当评分差距从 7.5/10 降至 6.5/10 时。主要的向量数据库在 AN 分数上的表现被评估，AN 分数涵盖了 20 个与代理相关的维度，其中执行能力占 70%，访问准备度占 30%。
Pinecone、Qdrant 和 Weaviate 在 AI 代理中的 AN 分数比较
出处: Pinecone vs Qdrant vs Weaviate for AI Agents: AN Score Comparison
发布: 2026年3月30日
📄 English Summary
Pinecone vs Qdrant vs Weaviate for AI Agents: AN Score Comparison
Every RAG pipeline requires a vector database, and most agent builders choose one during the prototype phase without revisiting their choice, which can lead to production friction, especially when the score difference drops from 7.5/10 to 6.5/10. The major vector databases are evaluated based on the AN Score, which encompasses 20 agent-specific dimensions, weighted 70% for execution and 30% for access readiness.