Build a RAG Chatbot with Laravel 13, Gemini AI & pgvector

Learn to build a production-ready AI chatbot in Laravel 13 that answers questions from your own uploaded documents

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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