Memvid encodes text chunks as QR codes within MP4 frames to build a portable “video memory” for AI systems. This innovative approach uses standard video containers and offers millisecond-level semantic search across large corpora with dramatically less storage than vector DBs. It's self-contained—no DB needed—and supports features like PDF indexing, chat integration, and cloud dashboards.

Features

  • Encodes text as QR in video frames
  • Millisecond semantic search without DB
  • Compact storage vs vector DBs
  • Supports importing PDFs
  • Works offline and embeddable
  • Simple Python encoder/retriever API

Project Samples

Project Activity

See All Activity >

Categories

Semantic Web

License

MIT License

Follow Memvid

Memvid Web Site

You Might Also Like
Gen AI apps are built with MongoDB Atlas Icon
Gen AI apps are built with MongoDB Atlas

The database for AI-powered applications.

MongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
Start Free
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of Memvid!

Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

Python

Related Categories

Python Semantic Web Software

Registered

2025-07-03