Private AutoGPT Robot - Your private task assistant with GPT!
Project description
🔥 PⒶutoBot 🔥
Private AutoGPT Robot - Your private task assistant with GPT!
- 🔥 Chat to your offline LLMs on CPU Only. 100% private, no data leaves your execution environment at any point.
- 🔥 Ask questions to your documents without an internet connection. Engine developed based on PrivateGPT.
- 🔥 Automate tasks easily with PAutoBot plugins. Easy for everyone.
- 🔥 Easy coding structure with Next.js and Python. Easy to understand and modify.
- 🔥 Built with LangChain, GPT4All, Chroma, SentenceTransformers, PrivateGPT.
The supported extensions are:
.csv
: CSV,.docx
: Word Document,.doc
: Word Document,.enex
: EverNote,.eml
: Email,.epub
: EPub,.html
: HTML File,.md
: Markdown,.msg
: Outlook Message,.odt
: Open Document Text,.pdf
: Portable Document Format (PDF),.pptx
: PowerPoint Document,.ppt
: PowerPoint Document,.txt
: Text file (UTF-8),
I. Installation and Usage
1. Installation
- Python 3.8 or higher.
- Install PAutoBot:
pip install pautobot
2. Usage
- Run the app:
python -m pautobot.app
or just:
pautobot
- Go to http://localhost:5678/ to see the user interface. You can choose one of the two modes:
- Chat Only
- Documents Q&A
- Upload some documents to the app (see the supported extensions above). You can try docs/python3.11.3_lite.zip for a quick start. This zip file contains 45 files from the Python 3.11.3 documentation.
- Force ingesting documents with Ingest Data button.
You can also run PAutoBot publicly to your network or change the port with parameters. Example:
pautobot --host 0.0.0.0 --port 8080
II. Development
1. Clone the source code
git clone https://github.com/nrl-ai/pautobot
cd pautobot
2. Run your backend
- Python 3.8 or higher.
- To install Pautobot from source, from
pautobot
source code directory, run:
pip install -e .
- Run the app:
python -m pautobot.app
- Go to http://localhost:5678/ to see the user interface.
2. Run your frontend
- Install the dependencies:
cd frontend
npm install
- Run the app:
npm run dev
- Go to http://localhost:3000/ to see the user interface. Use this address to develop the frontend.
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