Youtube RAG search with Frontend using Apify, Qdrant and AI

Ever wanted to build your own RAG search over Youtube videos? Well, now you can! This n8n template shows how you can build a very capable Youtube search engine powered by Apify, Qdrant and your LLM of choice to quickly and efficiently browse over many videos for research. I originally started to template to ask questions on the "n8n @ scale office-hours" livestream videos but then extended it to include the latest videos on the official channel. Check out a demo here: https://jimleuk.app.n8n.cloud/webhook/n8nvideos How it works Stage 1 is to collect the Youtube video transcripts and push them into a vector database. For this, I've used Apify to scrape Youtube and Qdrant to store the embeddin

Help me set up the n8n workflow "Youtube RAG search with Frontend using Apify, Qdrant and AI" (https://n8n.io/workflows/3288). It uses: HTTP Request, Redis, HTML, Basic LLM Chain, Embeddings OpenAI, OpenAI Chat Model, Recursive Character Text Splitter, Default Data Loader, Qdrant Vector Store, Information Extractor. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.