Artificial Intelligence tools and workflows
## How it works - The workflow loads a list of test cases from a Google Sheet (previous results stored from an LLM). - For each test case, we execute a call to an LLM judge in parallel (using HTTP Request + Webhook nodes). - The judge uses the Input, Output, and Reference Answer fields from the spreadsheet to mark each LLM response as Pass/Fail. - The results are logged into a separate sheet in the same Sheets file. ## Set up steps: - Add your credentials for Google Sheets and OpenAI (or replace the OpenAI node with your favorite chat model). - Make a copy of the example Sheet to populate it with your own test data. - Run the workflow with the Execute Workflow button next to the Manual Trigger node.
This workflow automates the retrieval of citations and sources from OpenAI assistant outputs, formatting them for Markdown or HTML. It ensures accurate citation handling and output formatting for enhanced readability.
### 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 this 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](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 vectorized 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 video 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 ## Customizing 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.
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