AI-Powered YouTube Video Extraction, Summarization, and Recommendation
An AI video intelligence system that extracts YouTube content, summarizes key information, and recommends relevant videos using multimodal models and vector search.
YouTube videos often contain valuable information, but manually watching long videos to find useful points takes time. For users, researchers, and content platforms, it becomes difficult to quickly understand video content, organize metadata, and recommend related materials efficiently.
This project solves that problem by building an AI-powered video extraction, summarization, and recommendation system. It uses BLIP and OpenCV to extract meaningful visual and video information, while LangChain and Milvus vector database help categorize, store, and retrieve relevant content.
A local LLM such as Mistral 7B is used for retrieval, summarization, and recommendation generation. The system also includes metadata handling through LangChain and APIs, making video content easier to manage, search, summarize, and recommend with faster retrieval performance.
Key benefits
- Extracts useful information from YouTube videos
- Summarizes video content using local LLMs
- Categorizes and retrieves content with LangChain and Milvus
- Reduces data retrieval time by 30%
- Supports recommendation generation from video metadata and content
What's included
- YouTube video extraction workflow
- BLIP/OpenCV-based video analysis system
- LangChain-powered categorization and retrieval
- Milvus vector database integration
- Metadata management with REST APIs
Use Cases
- YouTube video summarization
- Content recommendation system
- Educational video analysis
- Video metadata management
- AI-powered media search
Key deliverables
- AI video extraction pipeline
- Video summarization system
- Recommendation engine
- Milvus vector database setup
- Django REST API backend
- PostgreSQL-based metadata handling structure