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