Edit Mind lets you index your videos including transcription, frame analysis, and multi-model embedding, and you can search your videos or specific video scenes using natural language. The project is designed to help you transcribe, analyze and index your personal video library to help you search for the exact part of the video you're looking for.

Core features include video indexing and processing with a background service that watches for new video files and queues them for AI-powered analysis. The AI-powered video analysis extracts metadata like face recognition, transcription, object & text detection, scene analysis, and more. It provides vector-based semantic search capabilities on video content using ChromaDB and offers dual interfaces accessible through a Web App.

The application runs fully locally using local ML models and a local vector database, ensuring your videos never leave your computer or server with Docker support. It uses AI for rich metadata extraction and semantic search, allowing users to search videos by spoken words, objects, faces, and other criteria.

Benefits include the ability to search videos by spoken words, objects, faces, and more while maintaining complete privacy since everything runs locally. It works on any computer or server with Docker installed and uses AI for rich metadata extraction and semantic search capabilities.

The target users are individuals who want to manage their personal video libraries with advanced search capabilities. The application supports Docker containerization and uses technologies including ChromaDB for vector database functionality, PostgreSQL for relational data, and various AI models for processing.