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https://github.com/aida-ugent/SkillGPT
https://github.com/aida-ugent/SkillGPT
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/aida-ugent/SkillGPT
- Owner: aida-ugent
- License: other
- Created: 2023-04-16T13:24:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-25T12:17:35.000Z (6 months ago)
- Last Synced: 2024-05-14T00:17:20.944Z (4 months ago)
- Language: Python
- Size: 23.4 KB
- Stars: 51
- Watchers: 6
- Forks: 12
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model
Nan Li, Bo Kang, Tijl De bie
IDLAB - Department of Electronics and Information Systems (ELIS), Ghent University
### [Paper](https://arxiv.org/abs/2304.11060)
### [Demo Video](http://bokang.io/videos/SkillGPT.mp4)
## Requirements
- Environment
- Python 3.8 or later
- Docker
- Redis
## Installation
1. Make sure you have all the requirements listed above
2. Clone the repository
``` bash
git clone https://github.com/aida-ugent/SkillGPT.git
```
3. Navigate to the directory where the repository was downloaded
``` bash
cd SkillGPT
```
4. Install the required dependencies
``` bash
pip install -r requirements.txt
```
5. Configure SkillGPT
1. Locate the file named .env.template in the main /SkillGPT folder.
2. Create a copy of this file, called .env by removing the template extension.
3. Open the .env file in a text editor.
4. Enter Model server info as well as Redis server info.
5. Save and close the .env file.
## Environment vairable setup
Set the following settings in `.env`
``` bash
API_HOST="127.0.0.1" # the IP or domain to launch the api gateway
API_PORT=21002
REDIS_HOST=localhost # the IP or domain of the running redis instance
REDIS_PORT=6379
MODEL_PATH=models/vicuna_13b # the path to Huggingface AutoModelForCausalLM model
```
## Usage
1. Launch docker service
``` bash
sudo docker run --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest
```
2. Run `api` Python module in your terminal
``` bash
python -m api
```
3. Launch gradio interface
``` bash
python gradio_server.py
```
4. Process via API requests. See examples in `api_request.ipynb`.
5. (Optional) initalize Redis vector DB. See example in the last cell "Initialize ESCO embeddings" in `api_request.ipynb`.
## Access to the embedding files
Please fill in the form https://forms.gle/vKXKxCegWjzni1aM7 .