https://github.com/weaviate-tutorials/multi2vec-bind-js
https://github.com/weaviate-tutorials/multi2vec-bind-js
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/weaviate-tutorials/multi2vec-bind-js
- Owner: weaviate-tutorials
- Created: 2023-08-31T07:45:20.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-19T13:32:32.000Z (over 2 years ago)
- Last Synced: 2025-03-25T05:27:30.252Z (about 1 year ago)
- Language: TypeScript
- Size: 27.4 MB
- Stars: 12
- Watchers: 1
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: readme.md
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README
## Docker – Setting up a Weaviate instance
In this step, we need to run Weaviate in a Docker container.
> Note that the first time you run it, Docker will download ~4.8GB `multi2vec-bind` Weaviate module, which contains the `ImageBind` model.
### Start Weaviate container with `docker-compose.yml`.
To start the Weaviate instance, run the following command, which will use the `docker-compose.yml` file.
```
docker compose up
```
> To stop the container, press `CTRL` + `c` (or the relevant kill command in your terminal).
## Node – Loading data into Weaviate
In this step, we will use the node application to populate Weaviate with data (image, audio and video).
> The image, audio and video files are located in the [web-app/src/assets/](./web-app/src/assets/) folder.
> You only need to run the import process once!
### Node Setup
1. Go to the node folder:
```
cd node-app
```
2. Install packages
```
npm install
```
### Run node app
To import the data, run the following command:
```
npm run start
```
## Angular – Search in a web app
In this step, we will run an Angular app, which allows us to search through our data.
### Angular Setup
1. Go to the web project
```
cd web-app
```
2. Install packages
```
npm install
```
3. Install Angular CLI:
```
npm install -g @angular/cli
```
### Run the web app
Run the app with the Angular CLI, which will automatically open the app in your browser.
```
ng serve -o
```