This project leverages advanced semantic search capabilities to navigate through a comprehensive database of YouTube video transcripts from a prominent influencer. By employing Natural Language Processing (NLP) techniques, this search engine intelligently segments transcripts and encodes them into a vector store for efficient retrieval.

• Download and Processing: All video transcripts are downloaded and processed using cutting-edge NLP methods.
• Smart Segmentation: Extensive dialogues are broken down into manageable, meaningful units, enhancing the search engine's ability to understand and categorize content accurately.

• High-Dimensional Vectors: The processed transcripts are transformed into high-dimensional vectors.
• Vector Database: These vectors are stored in a vector database, facilitating semantic search. This allows for nuanced understanding and retrieval of content based on semantic similarity rather than just keyword matching.

• Effective Querying Mechanism: Traditional direct queries resulted in subpar outcomes. The breakthrough was in configuring the system to generate contextually similar responses that the influencer might provide.
• ChatGPT Integration: The system integrates a ChatGPT model to simulate potential answers to user queries before searching the vector store, dramatically improving the relevance and quality of search results.

This search engine enhances the way users interact with video content, offering a unique solution to the often frustrating experience of pinpointing specific information within lengthy videos. By allowing users to find not just any content, but the most contextually relevant advice or discussion points, it provides immense value to:

• Educational Platforms
• Content Creators
• Viewers

The system already shows remarkable performance even without fine-tuning. Future enhancements could include:

• Fine-Tuning the ChatGPT Model: Specific influencer data can further refine answer generation.
• Database Expansion: Including multiple influencers across various domains will scale the system, making it an attractive prospect for investors and partners interested in cutting-edge AI and content discovery platforms.

This project highlights capabilities in AI, NLP, and system architecture design, demonstrating the ability to tackle complex, real-world problems with innovative solutions. It paves the way for future projects in AI-driven content navigation and user interaction technologies, reflecting both technical proficiency and market insight.

1. Clone the Repository:

   git clone https://github.com/yourusername/youtube-search-engine.git
   cd youtube-search-engine

2. Install Dependencies:

   pip install -r requirements.txt

3. Download Transcripts:

   • Use the provided script to download and preprocess YouTube video transcripts.

4. Segment Transcripts:

   • Run the segmentation script to break down transcripts into manageable units.

5. Encode Transcripts:

   • Transform the segmented transcripts into high-dimensional vectors and store them in the vector database.

6. Run the Search Engine:

   • Start the search engine and begin querying. The system will use ChatGPT to generate contextually similar responses and retrieve the most relevant content.

Contributions are welcome! Please fork this repository and submit pull requests.

This project is licensed under the MIT License - see the LICENSE file for details.

By leveraging advanced AI and NLP technologies, this project aims to revolutionize the way users search and interact with video content. Whether for educational purposes, content creation, or simply enhancing viewer experience, this search engine represents a significant step forward in semantic search capabilities.