使用 Python、Streamlit 与 OpenAI 构建您自己的 RAG 系统

这门基于项目的实践课程将引导您使用Python、Streamlit和OpenAI从零开始构建并部署一个功能完整的检索增强生成（RAG）系统，打造能够读取个人文档并准确回答问题的AI应用。课程完整涵盖从文档处理（读取、分块、生成嵌入）、向量搜索到答案生成的RAG全流程，最终通过直观的Streamlit界面集成，支持本地运行或云端部署。学员将通过动手实践掌握Python、OpenAI API、语义搜索及应用部署等技能，无需AI或Python基础即可学习，最终完成一个可个性化扩展、适用于自动化客服或文档智能处理的实战项目，为构建实际AI解决方案奠定坚实基础。

制作：Bluelime Learning Solutions
MP4格式 | 视频：h264，1920×1080 | 音频：AAC，44.1 kHz，2声道
级别：初级 | 类型：在线学习 | 语言：英语 | 时长：35节课（2小时5分钟） | 文件大小：1.14 GB

Build Your Own RAG System with Python, Streamlit & OpenAI. Master Retrieval-Augmented Generation: Build, & Deploy a Complete AI-Powered Document Chat Application from Scratch。Build your own fully working AI system that can read your documents and answer questions with accuracy.In this step-by-step project-based course, you will learn how to use Retrieval-Augmented Generation (RAG) to overcome the limitations of traditional AI models. Instead of relying on the model’s memory, you will connect GPT to your own knowledge sources such as PDFs, policies, reports, and business documentation.You will learn the complete pipeline: document ingestion, chunking, embeddings, vector search, and contextual answer generation. We will combine all of this into a clean, user-friendly Streamlit application that you can run locally or deploy to the cloud.Throughout the course, you will gain hands-on skills in Python, the OpenAI API, semantic search, creating embeddings, designing a chat interface, and deploying applications online.By the end of the course, you will have built and shipped a working RAG system that you can personalize, extend, and showcase in your portfolio. Whether your goal is automating customer support, improving document access, or creating new AI-powered products, this project gives you a strong foundation for building real-world AI solutions.This course is accessible to beginners, while still offering depth for intermediate learners who want to advance their AI engineering skills.Enroll today and start building smarter AI that truly understands your documents.

What you’ll learn
Understand how text embeddings convert human language into numerical vectors that capture semantic meaning, enabling similarity-based search
Describe the complete RAG pipeline including the five key stages.
Explain what Retrieval-Augmented Generation (RAG) is and articulate why it’s superior to fine-tuning for document-based question answering applications
Set up a professional Python development environment with virtual environments to isolate project dependencies
Create and manage a requirements.txt file to document and install project dependencies efficiently
Securely manage sensitive credentials like API keys using environment variables and Streamlit’s secrets management system
Read and extract text content from various document formats such as PDF and TXT.
Chunk large documents into smaller segments suitable for retrieval.
Generate embeddings using the OpenAI API for semantic search.
Store and index embeddings efficiently using a vector database.
Execute similarity searches to retrieve relevant document chunks.
Build core RAG logic that connects retrieval and generation into a working pipeline.
Create an interactive Streamlit application for document chat functionality.
Upload documents and ask questions that return grounded and cited answers
Test the RAG application using real-world documents.
Deploy a working RAG system to Streamlit Cloud for public access.

Requirements
Basic computer literacy (file navigation, copy/paste, typing)
A computer running Windows, macOS, or Linux
Internet access for using the OpenAI API and deployment tools
A free OpenAI account to obtain an API key
Basic programming concepts are beneficial but not mandatory
No prior AI or Python experience is necessary.