Description
转自 https://www.youtube.com/playlist?list=PLZoTAELRMXVM8Pf4U67L4UuDRgV4TNX9D
转自 https://www.youtube.com/playlist?list=PLZoTAELRMXVM8Pf4U67L4UuDRgV4TNX9D
转自 https://www.youtube.com/playlist?list=PLZoTAELRMXVM8Pf4U67L4UuDRgV4TNX9D
本视频是"从零构建RAG"系列的第三集（下篇），在上篇完成数据摄入与向量数据库存储的基础上，本节重点实现查询检索管道（Query Retrieval Pipeline）。课程详细演示了如何结合大型语言模型（LLM）对检索到的上下文进行归纳总结，最终输出经过提炼的RAG答案。通过对整个端到端管道的完整实现，帮助学习者深入掌握生产级RAG系统的构建方法。
This is Part 2 (Episode 3) of the "Build RAG From Scratch" series. Building on the data ingestion pipeline from Part 1, this session implements the query retrieval pipeline — integrating an LLM to generate summarized answers from retrieved vector DB context. The tutorial walks through the complete end-to-end RAG pipeline, showing how user queries flow through vector search and LLM inference to produce grounded, coherent responses.
原版视频：https://www.youtube.com/playlist?list=PLZoTAELRMXVM8Pf4U67L4UuDRgV4TNX9D