AI-Powered Knowledge Base & RAG Chatbot
From documents to intelligent answers.
Project Overview
I built an AI-powered Retrieval-Augmented Generation (RAG) knowledge base that allows users to interact with documents using natural language.
The system processes documents, generates embeddings, stores information in a vector database, retrieves relevant information, and uses an AI Agent to provide contextual responses.
The Challenge
Traditional document repositories can make it difficult to quickly find specific information. I wanted to build a system that could allow users to ask questions in natural language and retrieve relevant information from their knowledge base.
The Solution
I designed a Retrieval-Augmented Generation workflow that connects document ingestion, embeddings, vector search, and an AI Agent into an automated knowledge system.
How It Works
- 1Google Drive
- 2n8n
- 3Document Processing
- 4Gemini Embeddings
- 5Supabase Vector Store
- 6User Question
- 7AI Agent
- 8Relevant Information Retrieval
- 9AI Response
Knowledge Base Build
Capturing the knowledge base workflow and the components used to prepare information for retrieval.
RAG Build Successfully Connected
This screenshot captures the successfully connected RAG workflow and demonstrates the working integration between the automation, AI, and knowledge retrieval components.
Watch the Build
Walk through the Knowledge Base and RAG chatbot project and see how the workflow comes together.
Knowledge Base & RAG Chatbot Project Demo
See the RAG Solution in Action
A demonstration of the AI-powered knowledge base and RAG chatbot built with n8n, Google Gemini, and Supabase.
AI Powered Knowledge Base & RAG Chatbot | n8n + Gemini + Supabase
Technologies Used
What I Learned
Building this project reinforced that creating an AI workflow is only one part of the process. Reliable retrieval, correct embedding configuration, vector database setup, API limitations, and troubleshooting all play an important role in building a practical RAG system.
The Goal
Turn static documents into a knowledge system people can interact with naturally.
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