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Visual Editor for Natural Language Processing pipelines

nlp-editor 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Visual Editor for NLP rules

Visual Editor for creating NLP rules.

![Visual editor interface](https://user-images.githubusercontent.com/81634386/158040876-7bb94cbd-7c4a-4b2c-b50f-7524985801c0.png)

## How to use

1. Watch a live demo of the NLP editor, and learn more about our future plans in our recent [IBM Data Science Community presentation](https://community.ibm.com/community/user/ai-datascience/blogs/tim-bonnemann1/2022/06/30/replay-available-learn-about-elyra-visual-nlp-edit).

2. Walk through our [Tutorial](tutorial.md).

3. Try the editor by following the instructions below.

## Try the editor

1. Clone the repository

```
   git clone git@github.com:CODAIT/nlp-editor.git
   ```

1. Navigate to the source code
   ```
   cd nlp-editor
   ```

The application users a NodeJS server file as proxy, this makes it easy to replace and embed the UI with any other server - Websphere, Nginx, etc.

### Prerequisites

On a Terminal window, install the Node Version Manager (nvm) as follows:

```
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.0/install.sh | bash
```
> Reference: https://github.com/nvm-sh/nvm#installing-and-updating

Verify nvm installed properly

```
> nvm -v
0.39.2
```
Next, install the required NodeJS version; currently at 18.12.0
```
nvm install v18.12.0
```
Verify node and npm installed properly
```
> node -v
v18.12.0
> npm -v
8.19.2
```

### Run the editor locally (without a backend runtime)

1. Install the dependencies
   ```
   npm install
   ```

2. Build the app
   ```
   npm run build
   ```

3. Run the app
   ```
   npm run serve
   ```

4. Open http://localhost:8080 in a web browser.

### Run the editor locally using the IBM Watson Discovery Backend container

**Additional Prerequsites**:
 - Docker
 - IBM Watson Discovery Backend container `01-ibm_watson_discovery_web_nlp_tool_backend-<date>.tar.gz` supplied to you

1. Follow steps above to **Run the editor locally**

<!-- Comment out this step as we've added these directories to git
2. Add the following folders to the nlp-editor; `Seer-Code` folder at root level, then add `aql-processor` folder underneath, and finally two more folders `user-data-in` and `run-aql-result`. The folder structure should reflect the following:

* 
  * nlp-editor/Seer-Code/aql-processor/user-data-in
* 
  * nlp-editor/Seer-Code/aql-processor/run-aql-result
-->

2. Extract `01-ibm_watson_discovery_web_nlp_tool_backend-<date>.tar.gz` into a folder of your choice, say `watson_nlp_web_tool`

3. Build the container image
   ```
   cd watson_nlp_web_tool
   docker build -t watson_nlp_web_tool:1.0 .
   ```

4. Run the container image with volumes mapped. Note that `/path/to/nlp-editor` is the absolute path to the `nlp-editor` repository (from Step 1).

```
   docker run -d -v /path/to/nlp-editor/Seer-Core/aql-processor/user-data-in:/app/Seer-Core/aql-processor/user-data-in -v /path/to/nlp-editor/Seer-Core/aql-processor/run-aql-result:/app/Seer-Core/aql-processor/run-aql-result --name watson_nlp_web_tool watson_nlp_web_tool:1.0
   ```

5. Open http://localhost:8080 in a web browser, or use reuse session from Step 1.

6. Create your NLP model. Use the [Tutorial](./tutorial.md) for guidance.

7. When you are satisfied with your model, click **Export**. A `.zip` file is generated on your local file system.

8. In Watson Discovery on CloudPak for Data, apply the model by following the steps in [Advanced Rules Models](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-domain-advanced-rules).

## Getting Help

We welcome your questions, ideas, and feedback. Please create an [issue](https://github.com/CODAIT/nlp-editor/issues) or a [discussion thread](https://github.com/CODAIT/nlp-editor/discussions).

## Contributing to the NLP editor
If you are interested in helping make the NLP editor  better, we encourage you to take a look at our 
[Contributing](CONTRIBUTING.md) page.

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