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@@ -137,6 +174,7 @@ in addition to the response headers, you should get a response body like
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}
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```
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---
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### Cloudflare Workers
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@@ -159,33 +197,10 @@ npm install
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npm run deploy
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```
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For more details, refer to [Cloudflare Workers official](https://developers.cloudflare.com/workers/).
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<br>
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### NodeJS Server
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1. Clone the Repository
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```sh
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git clone https://github.com/portkey-ai/gateway
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```
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2. Install the NPM Dependencies
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```sh
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cd gateway
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npm i
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npm run build
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```
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<br>
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3. Run the Server
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```sh
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node build/start-server.js
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```
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<br>
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---
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### Docker
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@@ -199,6 +214,8 @@ For more information on the Docker image, check [here](https://hub.docker.com/r/
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<br>
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---
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### Docker Compose
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1. Download Compose File from the Repository:
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```
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> The service is now running and listening on port 8787
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For more details, refer to [Docker Compose official](https://docs.docker.com/compose/).
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<br>
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---
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### Replit
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[](https://replit.com/@portkey/AI-Gateway?v=1)
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<br>
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---
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### Zeabur
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[](https://zeabur.com/templates/RU38E3)
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<br>
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---
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### Vercel
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Docs to be written, please help!
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1. Clone the Repository
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```sh
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git clone https://github.com/portkey-ai/gateway
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```
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2. Set up a new project in Vercel by linking the repository.
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3. Configure environment variables in Vercel's dashboard, matching those in the `deployment.yaml` file (e.g., `PORTKEY_PROVIDER`, `PORTKEY_PROVIDER_AUTH_TOKEN`).
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4. Adjust the project to use Vercel’s serverless function framework if needed.
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5. Deploy your application.
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For more details, refer to [Vercel's documentation](https://vercel.com/docs/deployments/git/vercel-for-github).
4. Create a new Fastly service and link it to your cloned Gateway repo.
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5. Modify the `gateway` code to fit into Fastly's Compute@Edge runtime, considering its unique environment for handling edge requests.
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6. Deploy the service using Fastly CLI:
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```sh
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fastly compute publish
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```
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For more details, refer to [Fastly’s official](https://www.fastly.com/).
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<br>
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---
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### AWS Lambda
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Docs to be written, please help!
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1. Clone the Repository:
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```sh
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git clone https://github.com/portkey-ai/gateway
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```
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2. Create a new AWS Lambda function via the AWS Management Console.
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3. Use AWS Lambda Layers to include the necessary Node.js/Bun dependencies (e.g., `npm`).
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4. Zip the Gateway project (excluding `node_modules`) and upload it to your Lambda function.
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5. Configure environment variables for your Lambda function.
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6. Set up an API Gateway for HTTP requests and route them to your Lambda function.
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For more detailed steps, refer to the AWS Lambda [deployment documentation](https://docs.aws.amazon.com/lambda/).
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<br>
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### Lambda@edge
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---
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### Lambda edge
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1. Clone the Repository:
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```sh
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git clone https://github.com/portkey-ai/gateway
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```
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2. Prepare the project for Lambda@Edge deployment by ensuring that request handling works within CloudFront constraints.
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3. Set up an AWS CloudFront distribution.
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Docs to be written, please help!
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4. Deploy the Lambda function to the edge using the `aws` CLI or AWS Console.
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5. Attach the Lambda@Edge function to the CloudFront distribution.
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More information is available in AWS’s official [Lambda@Edge documentation](https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-at-the-edge.html).
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<br>
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---
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### Supabase Functions
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Docs to be written, please help!
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1. Clone the Repository:
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```sh
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git clone https://github.com/portkey-ai/gateway
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```
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2. Set up a Supabase project and create a new function.
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3. Modify the project to use Supabase’s environment.
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4. Deploy using the Supabase CLI:
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```sh
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supabase functions deploy your-function-name
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```
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Refer to [Supabase's official documentation](https://supabase.com/docs) for more details.
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<br>
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---
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## Enterprise Deployment
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Make your AI app more <ins>reliable</ins> and <ins>forward compatible</ins>, while ensuring complete <ins>data security</ins> and <ins>privacy</ins>.
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