RAG y conocimiento
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Local AI-driven document search and editing with first-in-class hybrid retrieval
★ 115 estrellasMIT
Ver la guía →summarize es un proyecto de vídeo de código abierto de martinopiaggi (martinopiaggi/summarize en GitHub). Tiene 225 estrellas en GitHub y 30 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 28 de septiembre de 2026.
Run summarize locally: video ai summarization from multiple sources (youtube.
Fetch the source code onto your machine.
Set up the Python environment the project needs.
Copy the sample config or env file and adjust it.
Start the app from the source directory.
Built around ai for a focused workflow.
Built around cobalt for a focused workflow.
Built around docker for a focused workflow.
Built around downloader for a focused workflow.
Inspect, fork, and modify every line of the code.
Run it on your own machine — no account lock-in.
Get the source on your machine.
git clone https://github.com/martinopiaggi/summarize.gitSet up Python dependencies.
cd summarize && pip install -r requirements.txtLaunch the application.
cd summarize && python main.pyIt video AI summarization from multiple sources (YouTube, X, Instagram, TikTok, Reddit, Facebook, Google Drive, Dropbox, and local files), giving you a working starting point you can run and adapt.
Yes — the code is open-source, so you can use and modify it without a license fee.
Clone the GitHub repository and follow the README; the typical flow is install dependencies, configure, and run.
It is primarily written in Python, so you will need that toolchain to build or extend it.
The GitHub repository is the hub: open issues for bugs and check the README and discussions for guidance.
Local AI-driven document search and editing with first-in-class hybrid retrieval
1 min voice data can also be used to train a good TTS model! (few shot voice cloning)
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Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.