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---
title: Frontend
emoji: 🐢
colorFrom: pink
colorTo: blue
sdk: docker
pinned: false
license: mit
app_port: 3000
---
This is a [LlamaIndex](https://www.llamaindex.ai/) project using [Next.js](https://nextjs.org/) bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).
## Getting Started
First, install the dependencies:
```
npm install
```
Second, generate the embeddings of the documents in the `./data` directory (if this folder exists - otherwise, skip this step):
```
npm run generate
```
Third, run the development server:
```
npm run dev
```
Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font.
## Using Docker
1. Build an image for the Next.js app:
```
docker build -t <your_app_image_name> .
```
2. Generate embeddings:
Parse the data and generate the vector embeddings if the `./data` folder exists - otherwise, skip this step:
```
docker run \
--rm \
-v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-v $(pwd)/config:/app/config \
-v $(pwd)/data:/app/data \
-v $(pwd)/cache:/app/cache \ # Use your file system to store the vector database
<your_app_image_name> \
npm run generate
```
3. Start the app:
```
docker run \
--rm \
-v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-v $(pwd)/config:/app/config \
-v $(pwd)/cache:/app/cache \ # Use your file system to store gea vector database
-p 3000:3000 \
<your_app_image_name>
```
## Learn More
To learn more about LlamaIndex, take a look at the following resources:
- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex (Python features).
- [LlamaIndexTS Documentation](https://ts.llamaindex.ai) - learn about LlamaIndex (Typescript features).
You can check out [the LlamaIndexTS GitHub repository](https://github.com/run-llama/LlamaIndexTS) - your feedback and contributions are welcome!
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