通过Python构建端到端生成式AI项目的实战课程

这是一门通过Python构建端到端生成式AI项目的实战课程，专注于开发一个智能AI旅行助手。学员将从零开始，学习如何搭建Streamlit前端界面和FastAPI后端服务，连接Qdrant向量数据库存储PDF文档嵌入，并集成OpenAI或Hugging Face大语言模型，最终实现一个能读取旅行指南PDF、通过检索增强生成技术智能回答用户问题的完整RAG流程。课程涵盖文档摄取、嵌入生成、向量检索等关键技术，指导学员完成从项目构建到云端部署的全过程。通过本课程，您将掌握开发现代GenAI应用的核心技能，并拥有一个可实际部署的AI助手项目。

由 Sharath Raju MP4 创建
| 视频：h264, 1280×720 | 音频：AAC, 44.1 KHz, 2 Ch
级别：初学者 | 类型：电子学习 | 语言：英语 | 时长：13 讲（1 小时 23 分钟）| 大小：666 MB

Build End-to-End GenAI Project: AI Travel Agent with Python。Master GenAI by building an AI Travel Agent – from data ingestion to RAG pipeline and deployment, all in one course. Do you want to build and deploy a real-world GenAI application from scratch?In this hands-on course, you’ll learn how to create your very own AI Travel Agent – an intelligent assistant that can read PDF guides, store them as embeddings, and answer user queries using Retrieval-Augmented Generation (RAG) techniques.This course walks you through every stage of development, starting from project setup, building the Streamlit frontend, developing a FastAPI backend, connecting to a vector database (Qdrant), and integrating OpenAI or Hugging Face LLMs. By the end, you’ll not only understand how modern GenAI apps work – you’ll have your own deployed AI assistant ready to use and extend.What You’ll BuildA working AI Travel Assistant that can ingest PDFs and answer travel-related questions intelligently.A clean and modular Python project structure suitable for real-world deployments.A RAG pipeline that connects ingestion, embeddings, retrieval, and LLM generation seamlessly.Fully deployed frontend and backend on cloud platforms such as Railway and Streamlit Cloud.What You’ll LearnHow to set up and structure GenAI projects like a pro.Building beautiful Streamlit UIs with file upload and query blocks.Creating backend APIs using FastAPI with /upload and /ask endpoints.Understanding document ingestion, embeddings, and vector databases.Connecting to Qdrant to store and retrieve embeddings efficiently.Implementing RAG techniques to combine retrieval and generation for smarter answers.Integrating OpenAI and Hugging Face models with proper key management.Deploying your application end-to-end to the cloud.By the end of this course, you’ll have hands-on experience with the entire GenAI development lifecycle – from idea to a fully deployed product。What you’ll learn
Build a complete end-to-end GenAI application from scratch using Python.
Understand and implement document ingestion, text chunking, and embeddings generation
mplement Retrieval-Augmented Generation (RAG) pipelines using OpenAI or Hugging Face models.
Learn how to manage environment variables, configurations, and structure production-ready GenAI projects.

Requirements
A computer with internet access and permission to install Python packages.
Curiosity to build and deploy AI-powered applications from scratch.