YouTube RAG Search with Web Frontend (Apify, Qdrant, AI) - n8n Workflow | Neura Market
YouTube RAG Search with Web Frontend (Apify, Qdrant, AI)
Build a powerful YouTube video search engine using RAG with Apify for scraping transcripts, Qdrant for vector storage, and AI for intelligent querying via a simple web frontend.
This n8n workflow enables you to create a Retrieval-Augmented Generation (RAG) search system over YouTube videos. It scrapes video transcripts from specified channels using Apify, breaks them into chunks, generates embeddings with OpenAI, and stores them in Qdrant vector database for efficient similarity search. A web frontend powered by a webhook allows users to query videos naturally, retrieve relevant results, and play embedded videos directly.
The workflow operates in two stages: first, pop
Platform
n8n
Category
AI & Machine Learning
Price
$24.99
Creator
Jordi Faber
YouTube
RAG
Apify
Qdrant
Vector Database
AI Search
Embeddings
OpenAI
Web Frontend
Transcript Analysis
How to import this workflow into n8n
1Purchase or download the workflow to get the n8n workflow JSON file.
2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
3Open each node marked with a credential warning and connect your own accounts and API keys.
4Run the workflow once manually to verify the data flow, then toggle it to Active.