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Youtube RAG search with Frontend using Apify, Qdrant and AI

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Created by: Jimleuk || jimleuk

Jimleuk

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Last update 4 months ago

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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/n8n_videos

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 embeddings.
  • Transcripts are broken down into smaller chunks and carefully tagged with metadata to assist in later search and filtering.
  • Stage 2 is to build a web frontend for the user to query the vectorised transcripts. I'm using a webhook to serve a simple web app and API to dynamically fetch the results.
  • When searching for a video, I've opted to use Qdrant's search groups API which in this use-case, performs better as it returns a wider range of videos results.
  • In the web frontend, when the user clicks on the results, the matching Youtube video plays in an embedded video player.

How to use

  • Once credentials are all set, first run steps 1 - 3 to populate your vector store.
  • Next, set the workflow to active to expose the web frontend. Visit the webhook URL in your browser to use it.
  • If only for personal use, you may want to remove the rate limiting mechanism in step 4.

Requirements

  • Apify for Youtube Channel and Video Scraping
  • Qdrant for Vector store
  • OpenAI for LLM and Embeddings

Customising the template

  • Not interested in official n8n videos? Swap to a different channel - this template will work on many as long as videos are not private or set to prevent embeds.
  • Technically any vector store should work but may not have the same grouping API. Use the simple vector store node and revert back to basic searching instead.