学习使用Laravel 13、Livewire和pgvector从零构建全栈RAG AI聊天机器人，集成Gemini API，实现知识库管理与实时聊天，并部署至Laravel Cloud。适合有Laravel基础的开发者。
原始标题：Build a RAG Chatbot with Laravel 13, Gemini AI & pgvector
本课程教授使用 Laravel 13、Livewire v4 和 Laravel AI SDK 从零构建基于 Gemini API 和 pgvector 的全栈 RAG AI 聊天机器人，涵盖知识库管理、实时聊天界面开发及部署至 Laravel Cloud 的完整流程。该课程专注于工业级应用开发，适合希望在 Laravel 生态中集成自定义 AI 功能的开发者。
Published 7/2026
Created by Brian Mweu
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 33 Lectures ( 3h 40m ) | Size: 2 GB
Learn to build a production-ready AI chatbot in Laravel 13 that answers questions from your own uploaded documents
What you’ll learn
⚡ Setting up the environment for creating the RAG app
⚡ Build the knowledge base from scratch that stores the pdf documents
⚡ Build a chat interface for interactivity with Gemini AI
⚡ Testing the app to ensure its working correctly
⚡ Deploy the RAG application to Laravel Cloud
Requirements
❗ Basic knowledge of Laravel and Livewire is important
Description
In this course you will build a fully working RAG (Retrieval-Augmented Generation) chatbot from scratch using Laravel 13, Livewire v4, and the Laravel AI SDK no prior AI experience required.
You will learn how to upload PDF documents, extract and chunk their text, generate vector embeddings using Google Gemini’s free API, and store them in PostgreSQL via pgvector. When a user asks a question, the app searches those vectors for the most relevant context and sends it to Gemini to generate a cited, accurate answer with source highlighting that shows exactly which part of the document was used.
The course follows a developer workflow, including debugging sessions, corrections, and honest explanations of why certain tools were chosen over others. You will not just copy code you will understand every decision made.
What you will build
✨ A Knowledge Base admin panel (upload, index, manage documents)
✨ A real-time chat interface with cited source highlighting
✨ A full RAG pipeline: parse → chunk → embed → store → retrieve → answer
✨ A deployed live application on Laravel Cloud
Who this is for: Laravel developers who want to add practical AI features to their apps without relying on expensive black-box services. Also the course will cater for other developers using other types of technologies.
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