📄 中文摘要
本教程提供了一种方法，使本地大型语言模型（LLM）能够访问互联网并进行信息检索。通过安装相关库，用户可以执行真实查询，并逐步解析内部处理过程及最终输出。最终，用户将获得一个工作管道，该管道能够将任何本地模型（如 Ollama、LM Studio 等）转变为能够搜索网络、阅读网页、对结果进行排名并生成带有内联引用的结构化提示的工具，类似于自托管的 Perplexity。该项目为希望增强本地 LLM 功能的开发者提供了实用的解决方案。
用 50 行 Python 构建本地 LLM 的 Perplexity 克隆
出处: Building a Perplexity Clone for Local LLMs in 50 Lines of Python
发布: 2026年3月20日
📄 English Summary
Building a Perplexity Clone for Local LLMs in 50 Lines of Python
This tutorial provides a method for enabling local large language models (LLMs) to access the internet and perform information retrieval. By installing a relevant library, users can execute real queries and break down the internal processing steps along with the final output. By the end, users will have a working pipeline that transforms any local model (such as Ollama, LM Studio, etc.) into a tool that searches the web, reads pages, ranks results, and generates structured prompts with inline citations, akin to a self-hosted Perplexity. This project offers a practical solution for developers looking to enhance the capabilities of local LLMs.