Memvid offers a novel approach to AI memory management by encoding text data into video files, enabling efficient, offline, and fast semantic search across millions of text chunks. This solution targets developers and researchers seeking to build scalable AI applications with large knowledge bases without the overhead of traditional databases.

How It Works

Memvid leverages video files as a "database," storing text chunks as frames within an MP4 container. Semantic search is achieved by encoding text into embeddings and then efficiently retrieving relevant chunks from the video index. This video-as-database approach offers significant storage compression and eliminates the need for dedicated database servers, making it highly portable and offline-capable.

Quick Start & Requirements

• Install via pip: pip install memvid
• For PDF support: pip install PyPDF2
• Official documentation and examples are available in the repository.

Highlighted Details

• Stores millions of text chunks in a single MP4 file.
• Achieves sub-second semantic search retrieval times.
• Offers up to 10x storage compression compared to traditional databases.
• Operates offline once video files are generated.
• Minimal dependencies and CPU-friendly operation.

Maintenance & Community

The project is maintained by Olow304 and the Memvid community. Contributions are welcomed, with a contributing guide and issue tracker available.

Licensing & Compatibility

Memvid is released under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

While efficient, the video encoding and decoding process might introduce some latency compared to in-memory solutions. The effectiveness of compression and retrieval speed can be influenced by video codec choices and frame size configurations.