Writing
gptauthor
GPTAuthor is an AI utility for writing long form
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View guide →Rag.computer — self-hostable rag platform - document ingestion, embedd, open-source.
rag.computer is an open-source rag & knowledge project by bigint (bigint/rag.computer on GitHub). It has 100 GitHub stars and 11 forks, is released under the MIT license, and was last updated on GitHub on 4 October 2026.
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 embeddings for a focused workflow.
Built around rag for a focused workflow.
Built around rag pipeline 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/bigint/rag.computer.gitSet up Python dependencies.
cd rag.computer && pip install -r requirements.txtLaunch the application.
cd rag.computer && python main.pyIt self-hostable RAG platform - document ingestion, embedding, and vector search behind a simple REST API, 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.
GPTAuthor is an AI utility for writing long form
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