OneKE currently offers the following features:
- [x] Various IE Tasks Support
- [x] Various Data Sources Support
- [x] Various LLMs Support
- [x] Various Extraction Method Support
- [x] User-Configurable Knowledge Base
## 🚀Quick Start
We have developed a webpage demo for OneKE with Gradio, click [here](http://120.27.214.45:7876/) try information extraction in an intuitive way.
> Note: The demo only displays OneKE's basic capabilities for efficiency. Consider the local deployment steps below for further features.
### Step1: Environment Setup
OneKE supports both manual and docker image environment configuration, choose your preferred method to build.
#### 🔩Manual Environment Configuration
Conda virtual environments offer a light and flexible setup.
**Prerequisites**
- Anaconda Installation
- GPU support (recommended CUDA version: 12.4)
**Configure Steps**
1. Clone the repository:
```bash
git clone https://github.com/zjunlp/OneKE.git
```
2. Enter the working directory, and all subsequent commands should be executed in this directory.
```bash
cd OneKE
```
3. Create a virtual environment using `Anaconda`.
```bash
conda create -n oneke python=3.9
conda activate oneke
```
4. Install all required Python packages.
```bash
pip install -r requirements.txt
# If you encounter network issues, consider setting up a domestic mirror for pip.
```
#### 🐳Building With Docker Image
Docker image provides greater reliability and stability.
**Prerequisites**
- Docker Installation
- NVIDIA Container Toolkit
- GPU support (recommended CUDA version: 12.4)
**Configure Steps**
1. Clone the repository:
```bash
git clone https://github.com/zjunlp/OneKE.git
```
2. Pull the docker image from the mirror repository.
```bash
docker pull zjunlp/oneke:v4
# If you encounter network issues, consider setting up domestic registry mirrors for docker.
```
3. Launch a container from the image.
```bash
docker run --gpus all \
-v ./OneKE:/app/OneKE \
-it oneke:v4 /bin/bash
```
If using locally deployed models, ensure the local model path is mapped to the container:
```bash
docker run --gpus all \
-v ./OneKE:/app/OneKE \
-v your_local_model_path:/app/model/your_model_name \
-it oneke:v4 /bin/bash
```
Map any **necessary local files** to the container paths as shown above, and use **container paths** in your code and execution.
Upon starting, the container will enter the `/app/OneKE` directory as its working directory. Just modify the code locally as needed, and the changes will sync to the container through mapping.
### Step2: Start with Examples
We offer two quick-start options. Choose your preferred method to swiftly explore OneKE with predefined examples.
> Note:
> - **Ensure** that your working directory is set to the **`OneKE`** folder, whether in a virtual environment or a docker container.
> - Refer to [here](#network-issue-solutions) to resolve the **network issues**. If you have more questions, feel free to open an issue with us.
#### 🖊️Start with CLI
**Step1: Prepare the configuration file**
Several YAML configuration files are available in the `examples/config`. These extraction scenarios cover different extraction data, methods, and models, allowing you to easily explore all the features of OneKE.
***Web News Extraction:***
Here is the example for the web news knowledge extraction scenario, with the source extraction text in `HTML` format:
```yaml
# model configuration
model:
category: DeepSeek # model category, chosen from ChatGPT, DeepSeek, LLaMA, Qwen, ChatGLM, MiniCPM, OneKE.
model_name_or_path: deepseek-chat # model name, chosen from deepseek-chat and deepseek-reasoner. Choose deepseek-chat to use DeepSeek-V3 or choose deepseek-reasoner to use DeepSeek-R1.
api_key: your_api_key # your API key for the model with API service. No need for open-source models.
base_url: https://api.deepseek.com # base URL for the API service. No need for open-source models.
# extraction configuration
extraction:
task: Base # task type, chosen from Base, NER, RE, EE.
instruction: Extract key information from the given text. # description for the task. No need for NER, RE, EE task.
use_file: true # whether to use a file for the input text. Default set to false.
file_path: ./data/input_files/Tulsi_Gabbard_News.html # path to the input file. No need if use_file is set to false.
output_schema: NewsReport # output schema for the extraction task. Selected the from schema repository.
mode: customized # extraction mode, chosen from quick, detailed, customized. Default set to quick. See src/config.yaml for more details.
update_case: false # whether to update the case repository. Default set to false.
show_trajectory: false # whether to display the extracted intermediate steps
```
The `model` section contains information about the extraction model, while the `extraction` section configures the settings for the extraction process.
You can choose an existing configuration file or customize the extraction settings as you wish. Note that when using an API service like ChatGPT and DeepSeek, please **set your API key**.
**Step2: Run the shell script**
Specify the configuration file path and run the code to start the extraction process.
```bash
config_file=your_yaml_file_path # configuration file path, use the container path if inside a container
python src/run.py --config $config_file # start extraction, executed in the OneKE directory
```
If you want to deploy the local models using vllm, run the following code:
```bash
config_file=your_yaml_file_path # REMEMBER to set vllm_serve to TRUE!
python src/models/vllm_serve.py --config $config_file # deploy local model via vllm, executed in the OneKE directory
python src/run.py --config $config_file # start extraction, executed in the OneKE directory
```
Refer to [here](https://github.com/zjunlp/OneKE/tree/main/examples/results) to get an overview of the knowledge extraction results.
> Note: You can also try OneKE by directly running the `example.py` file located in the `example` directory. In this way, you can explore more advanced uses flexibly.
#### 🖊️Start with Web UI
> Note: Before starting with the web UI, make sure the package `gradio 4.44.0` is already installed in your [Environment](https://github.com/zjunlp/OneKE/tree/main/requirements.txt).
**Step1: Execute Command**
Execute the following commands in the `OneKE` directory:
```bash
python src/webui.py
```
**Step2: Open your Web Browser**
The front-end is built with Gradio, and the default port of Gradio is 7860. Therefore, please enter the following URL in your browser's address bar to open the web interface:
```
http://127.0.0.1:7860
```
The web service interface is now complete, so you can visually configure tasks and obtain results through it.
## 📟 Web UI Navigation
### Initial Page
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| Text | Event Type | Event Trigger | Argument | Role |
|---|---|---|---|---|
| UConn Health , an academic medical center , says in a media statement that it identified approximately 326,000 potentially impacted individuals whose personal information was contained in the compromised email accounts. | data breach | compromised | email accounts | compromised data |
| 326,000 | number of victim | |||
| individuals | victim | |||
| personal information | compromised data |