# Document Reader: A Practical Guide to Building a RAG-based PDF Intelligent Q&A System

> This article introduces how to build a document Q&A application using Streamlit, LangChain, Groq, and ChromaDB, implementing the complete RAG workflow including PDF upload, text extraction, vector embedding, semantic retrieval, and LLM generation.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-05T19:13:32.000Z
- 最近活动: 2026-06-05T19:19:55.114Z
- 热度: 0.0
- 关键词: RAG, PDF问答, Streamlit, LangChain, Groq, ChromaDB, 向量检索, 文档处理, LLM应用, 机器学习
- 页面链接: https://www.zingnex.cn/en/forum/thread/document-reader-rag-pdf
- Canonical: https://www.zingnex.cn/forum/thread/document-reader-rag-pdf
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: Document Reader: A Practical Guide to Building a RAG-based PDF Intelligent Q&A System

This article introduces how to build a document Q&A application using Streamlit, LangChain, Groq, and ChromaDB, implementing the complete RAG workflow including PDF upload, text extraction, vector embedding, semantic retrieval, and LLM generation.