Upload any PDF → Ask questions → AI answers using document knowledge with RAG.

In today’s episode of the n8n AI Automation Mastery Course, we build a complete RAG-based AI system where users can upload a PDF and ask questions about it.

Instead of guessing answers, the AI retrieves information directly from the uploaded document and generates accurate responses.
No hallucinations. No fake answers. Only context-aware AI responses.

This is exactly how modern AI document assistants and enterprise knowledge systems are built in 2026.

TIMESTAMPS (The RAG System Architect)
0:00 - The RAG Revolution: Engineering an AI That Never Hallucinates
1:20 - Frontend Architecture: Designing the Document Ingestion Interface
2:43 - Backend Deployment: Running Your Self-Hosted n8n Instance
3:01 - Workflow Engineering: Building the Intelligence Pipeline from Scratch
3:14 - Webhook Integration: Setting Up the Global Data Listener
3:26 - The Handshake: Connecting UI to n8n via Production Webhook URLs
4:38 - Binary Processing: Extracting Raw Content from PDF Uploads
5:26 - Vector Database Setup: Initializing the Local Knowledge Base
6:08 - Semantic Intelligence: Generating OpenAI Embeddings for Vector Math
6:45 - Data Ingestion: Configuring the Document Loader for Knowledge Retrieval
7:06 - The Brain: Deploying the AI Agent for Multi-Tool Reasoning
8:16 - LLM Integration: Configuring OpenAI Chat Models for Response Generation
8:29 - Conversational Persistence: Adding Contextual Memory to the Agent
8:58 - Retrieval Logic: Connecting the Vector Store Tool for Semantic Search
10:08 - Feedback Loop: Mastering the "Respond to Webhook" Architecture
11:17 - Production Ready: Saving & Activating Your AI Masterpiece
11:20 - Stress Testing: Validating Data Flow & System Logic
11:45 - THE BOOM MOMENT! Live PDF Chat Verification & Accurate Results
In this project we build the complete workflow from scratch:
* Frontend web page for PDF upload and query input
* Connecting the frontend to n8n using Webhook
* Extracting data from PDF using Extract From File node
* Splitting text into chunks using Default Data Loader
* Creating embeddings using OpenAI Embedding model
* Storing vectors using Simple Vector Store
* Building the brain of the system using AI Agent node
* Adding Chat Model + Memory + Retrieval Tool
* Returning the final response back to the frontend using Respond to Webhook

When the user uploads a PDF and asks a question, the AI searches the document using vector embeddings and generates an answer only from the document knowledge.

This architecture is used in real-world systems like:
* AI document assistants
* Company knowledge base chatbots
* AI helpdesk systems
* Legal and research document search tools
* Internal company AI assistants

By the end of this video you will understand how modern AI systems combine RAG, Vector Databases, Embeddings, and AI Agents to build powerful knowledge-based assistants.

This video is part of n8n AI Automation Mastery — Zero to Job Ready (2026) only on The Techzeen.

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The Techzeen Website:
https://www.thetechzeen.com/

Source Code:
https://github.com/farzeen-ali/n8n-RAG

AI Automation Concepts:
https://github.com/farzeen-ali/AI-Automation-Concepts

n8n AI Automation Full Course (2026):
https://www.youtube.com/playlist?list=PL5OhSdfH4uDvUeV9hOqg1XEl3IPMyteZu

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