📄 中文摘要
大型语言模型（LLM）知识库常被误解为仅仅是“向量化文档”。实际上，生产级知识系统是一个需要可追溯、增量和可测量的检索基础设施。该工程指南涵盖了数据清理与规范化、语义分块策略、元数据架构设计、批量嵌入架构以及检索与评估考虑等多个方面，重点在于实际系统中的实施决策，而非理论探讨。
为大型语言模型设计可扩展的知识库
出处: Designing a Scalable Knowledge Base for Large Language Models
发布: 2026年2月11日
📄 English Summary
Designing a Scalable Knowledge Base for Large Language Models
Large Language Model (LLM) knowledge bases are often misconceived as merely 'vectorizing documents.' In reality, a production-grade knowledge system is a retrieval infrastructure that must be traceable, incremental, and measurable. This practical engineering guide covers data cleaning and normalization, semantic chunking strategies, metadata schema design, batch embedding architecture, and retrieval and evaluation considerations. The focus is on implementation decisions that work in real systems rather than theoretical discussions.