YouTube Video Summarizer and Q&A Platform

This web application uses React, TypeScript, Tailwind CSS, and ChatGPT API to summarize YouTube videos and answer related questions. Users can provide a YouTube video link, get a summary, and ask questions about the video content. The platform will also provide timestamps related to the answers.

Features

• YouTube URL Input: Users input a YouTube video link.
• Video Summarization: The platform uses ChatGPT to summarize the video content.
• Q&A Functionality: Users can ask questions related to the video, and ChatGPT will provide answers based on the video summary.
• Timestamping: Each answer will be paired with timestamps linked to specific sections of the video.
• Responsive Design: The website is fully responsive and styled with Tailwind CSS.

Tech Stack

• Frontend: React (with TypeScript), Tailwind CSS
• Backend: OpenAI ChatGPT API
• Libraries: Axios, React Router

How It Works

1. YouTube URL Input

Users input a YouTube video URL into the input field on the homepage. Upon submission, the platform retrieves the video’s transcript (using the YouTube API or an external service) to extract the spoken content of the video.

2. Video Transcript Extraction and Vectorization

The transcript extracted from the video is then processed and vectorized using a vector database. This involves converting the textual transcript into vector representations that capture the semantic meaning of the content. This step enables efficient search and retrieval of relevant segments of the transcript for answering specific questions.

• The transcript is stored in the vector database for fast retrieval.
• The vector database enables similarity searches, helping the system retrieve the most relevant parts of the video transcript based on user queries.

3. Q&A with Retrieval-Augmented Generation (RAG)

Once the transcript is stored and vectorized, users can ask specific questions about the video, such as:

• "What is the main topic of the video?"
• "What is discussed at [timestamp]?"
• "Can you summarize the key points at [timestamp]?"

The system retrieves relevant segments from the transcript by performing a similarity search using the user’s query against the stored vectors. The retrieved segments are then passed to ChatGPT for generating a contextualized answer based on the most relevant parts of the video transcript.

4. Timestamping and Contextual Answer Generation

After retrieving the relevant segments from the vector database, the system generates the answer while also identifying the timestamps corresponding to the most relevant portions of the transcript. The timestamps are included in the response to provide users with direct access to the specific points in the video related to the answer.

• The timestamped answers are generated based on the context of the retrieved transcript segments, ensuring that the responses are both accurate and contextually aligned with the video content.

5. Answer Display with Timestamps

The answers, along with the corresponding timestamps, are displayed on the user interface. Users can click on these timestamps to navigate directly to the relevant point in the video for a deeper dive into the content.

• The user interface is designed to allow seamless interaction with the answers and video, enabling users to watch the video at the specific timestamp mentioned in the answer.

6. Continuous Improvement with Feedback

As users interact with the platform, feedback can be used to improve the retrieval and response generation. The system can adapt by refining the way it stores transcripts and the vectorization process, leading to better and more accurate answers over time.

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This system ensures that video content is not only summarized but also made searchable and answerable with a high level of accuracy and context, thanks to the combination of vector databases and the ChatGPT API for contextual response generation.