The video demonstrates a script that uses AI to find key moments in YouTube videos by searching for specific terms and providing corresponding timestamps, utilizing an embedding model from OpenAI. The presenter explains the technical setup, including requirements and the script’s structure, while showcasing its functionality with examples and promoting future resources available through their Patreon.
In the video, the presenter demonstrates a script that utilizes AI to search through YouTube videos for specific moments, providing timestamps for relevant sections. The approach employs a retriever-like method using embeddings, allowing users to find key moments in videos based on their content. The presenter showcases this functionality using one of their own videos, illustrating how to search for terms like “embeddings” and retrieve the corresponding timestamps where the topic is discussed.
To begin, the presenter explains how to set up the script by selecting an embedding model from OpenAI, specifically recommending the T5 large model for its accuracy. After entering the YouTube URL, the script quickly processes the video, embedding the content for efficient searching. The presenter demonstrates a search for “Project Gutenberg,” showing how the script returns timestamps and snippets of text related to the query, enabling viewers to jump directly to the relevant parts of the video.
The video also covers the technical aspects of the script, including the requirements for running it, such as the OpenAI API key and the YouTube transcript API. The presenter discusses the use of the Rich library for formatting output in a visually appealing way. The script is structured using object-oriented programming, with methods for fetching transcripts, generating embeddings, and calculating cosine similarity to find the most relevant segments of the video based on user queries.
Additionally, the presenter outlines the process of handling video data, including extracting text and timestamps from transcripts, generating embeddings for each segment, and storing this information for similarity searches. The script is designed to return the top five results based on similarity scores, displaying them in a neatly formatted table that includes links to the specific timestamps in the video.
Finally, the presenter mentions plans to create a FastAPI web app version of the script and encourages viewers to check out their Patreon for access to the code files. They also promote their website and a masterclass on coding with AI tools, inviting viewers to explore additional resources and consulting options available through their Patreon. The video concludes with an invitation to engage with the content and stay tuned for future updates.