What would you like to see?

Summary:

This feature proposes enhancing Agent Mode to directly process YouTube video URLs, automatically retrieving and injecting the transcript into the agent's context for immediate, ephemeral interaction. This aims to significantly reduce friction and improve the user experience for quick summarization and question-answering on video content, bypassing the current multi-step document creation and embedding process.

Problem:

Currently, analyzing a YouTube video transcript within AnythingLLM requires a multi-step process that is time-consuming and less intuitive than ideal for common use cases. The existing workflow involves:

1. Navigating to the document creation modal.

2. Selecting the YouTube data connector.

3. Providing the URL and creating a document.

4. Manually selecting the transcript for embedding.

5. Waiting for the embedding process to complete, which can take several minutes.

This workflow is not optimized for ephemeral needs where users simply want to quickly ask questions or get a summary of a video without the long-term commitment of embedding its transcript into a database. Users often expect a more direct "paste URL and ask" experience, which the current system does not provide.

Furthermore, Using vector embeddings for YouTube transcript interactions doesn’t align well with general requests like summarization. Vector search is built to find pieces of information that are semantically similar to a given query, which assumes the user already knows roughly what they’re looking for. But when someone asks for a summary (“Summarize this video”), they’re saying the opposite: they don’t yet know the key points and want the system to figure them out for them.

Example of Usage

@agent Extract the key points from this video: https://www.youtube.com/watch?v=w4gqOWUw230