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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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llama-index-vector-search-javascript \
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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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llama-index-vector-search-javascript
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```
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## Using the app
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- In Azure: navigate to the Azure app deployed by `azd`. The URL is printed out when `azd` completes (as "Endpoint"), or you can find it in the Azure portal.
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