This is a LlamaIndex project using Next.js with Vercel AI RSC.

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 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 to automatically optimize and load Inter, a custom Google Font.

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>

To learn more about LlamaIndex, take a look at the following resources:

• LlamaIndex Documentation - learn about LlamaIndex (Python features).
• LlamaIndexTS Documentation - learn about LlamaIndex (Typescript features).

You can check out the LlamaIndexTS GitHub repository - your feedback and contributions are welcome!