RAG From Scratch
RAG From Scratch
https://github.com/langchain-ai/rag-from-scratch
LLMs are trained on a large but fixed corpus of data, limiting their ability to reason about private or recent information. Fine-tuning is one way to mitigate this, but is often not well-suited for facutal recall and can be costly. Retrieval augmented generation (RAG) has emerged as a popular and powerful mechanism to expand an LLM's knowledge base, using documents retrieved from an external data source to ground the LLM generation via in-context learning. These notebooks accompany a video playlist that builds up an understanding of RAG from scratch, starting with the basics of indexing, retrieval, and generation.
【推荐】编程新体验,更懂你的AI,立即体验豆包MarsCode编程助手
【推荐】凌霞软件回馈社区,博客园 & 1Panel & Halo 联合会员上线
【推荐】抖音旗下AI助手豆包,你的智能百科全书,全免费不限次数
【推荐】博客园社区专享云产品让利特惠,阿里云新客6.5折上折
【推荐】轻量又高性能的 SSH 工具 IShell:AI 加持,快人一步
· PowerShell开发游戏 · 打蜜蜂
· 在鹅厂做java开发是什么体验
· 百万级群聊的设计实践
· WPF到Web的无缝过渡:英雄联盟客户端的OpenSilver迁移实战
· 永远不要相信用户的输入:从 SQL 注入攻防看输入验证的重要性
2024-02-25 C - Many Replacement
2019-02-25 express session 和 socketio session关联
2018-02-25 Git 命令解释优秀博文转摘
2015-02-25 Cross-site Scripting (XSS) 阅读笔记