resolved merge

milo157 · milo157 · commit eb73e8247f85 · 2025-09-11T13:49:20.000-04:00
diff --git a/.vscode/settings.json b/.vscode/settings.json
@@ -1,8 +1,6 @@
 {
-    "cSpell.words": [
-        "Cerebrium"
-    ],
-    "yaml.schemas": {
-        "https://www.artillery.io/schema.json": []
-    }
-}
+  "cSpell.words": ["Cerebrium"],
+  "yaml.schemas": {
+    "https://www.artillery.io/schema.json": []
+  }
+}
diff --git a/cerebrium/container-images/custom-web-servers.mdx b/cerebrium/container-images/custom-web-servers.mdx
@@ -53,7 +53,7 @@ The configuration requires three key parameters:
 <Info>
   For ASGI applications like FastAPI, include the appropriate server package
   (like `uvicorn`) in your dependencies. After deployment, your endpoints become
-  available at `https://api.cortex.cerebrium.ai/v4/{project - id}/{app - name}
+  available at `https://api.aws.us-east-1.cerebrium.ai/v4/{project - id}/{app - name}
   /your/endpoint`.
 </Info>
 
diff --git a/cerebrium/container-images/defining-container-images.mdx b/cerebrium/container-images/defining-container-images.mdx
@@ -282,7 +282,7 @@ vllm = "latest"
 - Code is mounted in `/cortex`—adjust paths accordingly.
 - The port in your entrypoint must match the `port` parameter.
 - Install any required server packages (uvicorn, gunicorn, etc.) via pip dependencies.
-- All endpoints will be available at `https://api.cortex.cerebrium.ai/v4/{project-id}/{app-name}/your/endpoint`.
+- All endpoints will be available at `https://api.aws.us-east-1.cerebrium.ai/v4/{project-id}/{app-name}/your/endpoint`.
 
 Deploy as normal with `cerebrium deploy -y`—the system automatically detects and handles custom runtime configuration.
 
diff --git a/cerebrium/endpoints/async.mdx b/cerebrium/endpoints/async.mdx
@@ -10,7 +10,7 @@ responsibility, while you as the developer are responsible for ensuring that dat
 You can enable your function to execute asynchronously by adding the `async` query parameter to your request and setting it to `true`. This would look something like this:
 
 ```bash
-curl -X POST https://api.cortex.cerebrium.ai/v4/<YOUR-PROJECT-ID>/<YOUR-APP>/run?async=true'\
+curl -X POST https://api.aws.us-east-1.cerebrium.ai/v4/<YOUR-PROJECT-ID>/<YOUR-APP>/run?async=true'\
        -H 'Content-Type: application/json'\
        -H 'Authorization: Bearer <YOUR-JWT-TOKEN>\
        --data '{"param": "hello world"}'
@@ -47,7 +47,7 @@ async execution with a specified `webhookEndpoint`, to have Cerebrium automatica
 the function response once it has returned:
 
 ```bash
-curl -X POST <https://api.cortex.cerebrium.ai/v4/><YOUR-PROJECT-ID>/<YOUR-APP>/run?async=true&webhookEndpoint=https%3A%2F%2Fwebhook.site%2F'\
+curl -X POST <https://api.aws.us-east-1.cerebrium.ai/v4/><YOUR-PROJECT-ID>/<YOUR-APP>/run?async=true&webhookEndpoint=https%3A%2F%2Fwebhook.site%2F'\
  -H 'Content-Type: application/json'\
  -H 'Authorization: Bearer <YOUR-JWT-TOKEN>\
  --data '{"param": "hello world"}'
diff --git a/cerebrium/endpoints/inference-api.mdx b/cerebrium/endpoints/inference-api.mdx
@@ -8,7 +8,7 @@ By default, all functions on Cerebrium are accessible via authenticated POST req
 The POST request follows the structure below, where `{function}` is the name of the function you want to invoke. For example, in this case, the function `predict()` from `main.py` is being called.
 
 ```bash
-curl --location --request POST 'https://api.cortex.cerebrium.ai/v4/p-xxxxx/{app-name}/{function}' \
+curl --location --request POST 'https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxx/{app-name}/{function}' \
 --header 'Authorization: Bearer <JWT_TOKEN>' \
 --header 'Content-Type: application/json' \
 --data '{
diff --git a/cerebrium/endpoints/openai-compatible-endpoints.mdx b/cerebrium/endpoints/openai-compatible-endpoints.mdx
@@ -51,7 +51,7 @@ from openai import OpenAI
 
 client = OpenAI(
     # This is the default and can be omitted
-    base_url="https://api.cortex.cerebrium.ai/v4/p-xxxxx/1-openai-compatible-endpoint/run", ##This is the name of the function you are calling
+    base_url="https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxx/1-openai-compatible-endpoint/run", ##This is the name of the function you are calling
     api_key="<CEREBRIUM_JWT_TOKEN>",
 )
 
diff --git a/cerebrium/endpoints/streaming.mdx b/cerebrium/endpoints/streaming.mdx
@@ -24,7 +24,7 @@ Once you deploy this code snippet and hit the stream endpoint, you will see the
 You can do this as follows:
 
 ```bash
-curl -X POST https://api.cortex.cerebrium.ai/v4/<YOUR-PROJECT-ID>/2-streaming-endpoint/run \
+curl -X POST https://api.aws.us-east-1.cerebrium.ai/v4/<YOUR-PROJECT-ID>/2-streaming-endpoint/run \
        -H 'Content-Type: application/json'\
        -H 'Accept: text/event-stream\
        -H 'Authorization: Bearer <YOUR-JWT-TOKEN>\
diff --git a/cerebrium/endpoints/webhook.mdx b/cerebrium/endpoints/webhook.mdx
@@ -8,7 +8,7 @@ This allows you to use webhooks in your architecture. To achieve this, we can si
 query parameter to any API call:
 
 ```bash
-curl -X POST https://api.cortex.cerebrium.ai/v4/<YOUR-PROJECT-ID>/<YOUR-APP>/run?webhookEndpoint=https%3A%2F%2Fwebhook.site%2F'\
+curl -X POST https://api.aws.us-east-1.cerebrium.ai/v4/<YOUR-PROJECT-ID>/<YOUR-APP>/run?webhookEndpoint=https%3A%2F%2Fwebhook.site%2F'\
        -H 'Content-Type: application/json'\
        -H 'Authorization: Bearer <YOUR-JWT-TOKEN>\
        --data '{"param": "hello world"}'
diff --git a/cerebrium/endpoints/websockets.mdx b/cerebrium/endpoints/websockets.mdx
@@ -35,7 +35,7 @@ Explanation:
 You can test your WebSocket endpoint using websocat, a command-line utility for connecting to WebSocket servers:
 
 ```bash
-websocat wss://api.cortex.cerebrium.ai/v4/<your-project-id>/<your-app-name>/<your-websocket-function-name>
+websocat wss://api.aws.us-east-1.cerebrium.ai/v4/<your-project-id>/<your-app-name>/<your-websocket-function-name>
 ```
 
 ## Implementing the WebSocket Endpoint
@@ -62,7 +62,7 @@ Client-side Implementation: When connecting from a client app, ensure you handle
 ```javascript
 // Example using JavaScript in a browser
 const socket = new WebSocket(
-  "wss://api.cortex.cerebrium.ai/v4/<your-project-id>/<your-app-name>/<your-websocket-function-name>",
+  "wss://api.aws.us-east-1.cerebrium.ai/v4/<your-project-id>/<your-app-name>/<your-websocket-function-name>",
 );
 
 socket.onopen = function (event) {
diff --git a/cerebrium/getting-started/introduction.mdx b/cerebrium/getting-started/introduction.mdx
@@ -62,7 +62,7 @@ cerebrium deploy
 
 This will turn the function into a callable endpoint that accepts json parameters (prompt) and can scale to 1000s of requests automatically!
 
-Once deployed, an app becomes callable through a POST endpoint `https://api.cortex.cerebrium.ai/v4/{project-id}/{app-name}/{function-name}` and takes a json parameter, prompt
+Once deployed, an app becomes callable through a POST endpoint `https://api.aws.us-east-1.cerebrium.ai/v4/{project-id}/{app-name}/{function-name}` and takes a json parameter, prompt
 
 Great! You made it! Join our Community [Discord](https://discord.gg/ATj6USmeE2) for support and updates.
 
diff --git a/cerebrium/integrations/vercel.mdx b/cerebrium/integrations/vercel.mdx
@@ -43,7 +43,7 @@ Once you have followed the example and deployed the app, you should have an outp
 
 ```javascript
 fetch(
-  "https://api.cortex.cerebrium.ai/v4/p-<YOUR PROJECT ID>/mistral-vllm/predict",
+  "https://api.aws.us-east-1.cerebrium.ai/v4/p-<YOUR PROJECT ID>/mistral-vllm/predict",
   {
     method: "POST",
     headers: {
diff --git a/cerebrium/partner-services/deepgram.mdx b/cerebrium/partner-services/deepgram.mdx
@@ -284,7 +284,7 @@ wget https://dpgr.am/bueller.wav
 9. Access the Deepgram service by calling the endpoint with appropriate parameters such as:
 
 ```curl
-curl -X POST --data-binary @bueller.wav "https://api.cortex.cerebrium.ai/v4/p-xxxxxx/deepgram/v1/listen?model=nova-3&smart_format=true"
+curl -X POST --data-binary @bueller.wav "https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxxx/deepgram/v1/listen?model=nova-3&smart_format=true"
 ```
 
 Parameters accepted by the Deepgram service can be found in the [speech-to-text API reference](https://developers.deepgram.com/reference/speech-to-text-api/listen-streaming).
diff --git a/cerebrium/partner-services/rime.mdx b/cerebrium/partner-services/rime.mdx
@@ -59,7 +59,7 @@ App Dashboard: https://dashboard.cerebrium.ai/projects/p-xxxxxxxx/apps/p-xxxxxxx
 5. Use the Deployment url from the output to send requests to the <b>HTTP</b> Rime service via curl request:
 
 ```
-curl --location 'https://api.cortex.cerebrium.ai/v4/p-xxxxxxxx/rime' \
+curl --location 'https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxxxxx/rime' \
 --header 'Authorization: Bearer <RIME_API_KEY>' \
 --header 'Content-Type: application/json' \
 --header 'Accept: audio/pcm' \
diff --git a/docs.json b/docs.json
@@ -226,6 +226,7 @@
       "keywords": "Cerebrium, serverless GPUs, AI infrastructure, hosting Large Language Models, AI voice agents, Real-time AI",
       "language": "en",
       "copyright": "Copyright 2025"
+    }
   },
   "search": {
     "prompt": "Search Cerebrium docs..."
diff --git a/migrations/hugging-face.mdx b/migrations/hugging-face.mdx
@@ -186,7 +186,7 @@ Once deployed, you can use your model as follows:
 import requests
 import json
 
-url = "https://api.cortex.cerebrium.ai/v4/[PROJECT_NAME]/llama-8b-vllm/run"
+url = "https://api.aws.us-east-1.cerebrium.ai/v4/[PROJECT_NAME]/llama-8b-vllm/run"
 
 payload = json.dumps({"prompt": "tell me about yourself"})
 
diff --git a/migrations/mystic.mdx b/migrations/mystic.mdx
@@ -273,7 +273,7 @@ cerebrium deploy
 Once your app is deployed, you can make requests to your model using the example cURL request below:
 
 ```bash
-curl --location 'https://api.cortex.cerebrium.ai/v4/p-<YOUR PROJECT ID>/stable-diffusion/predict' \
+curl --location 'https://api.aws.us-east-1.cerebrium.ai/v4/p-<YOUR PROJECT ID>/stable-diffusion/predict' \
 --header 'Content-Type: application/json' \
 --header 'Authorization: Bearer <YOUR TOKEN HERE>' \
 --data '{
diff --git a/toml-reference/toml-reference.mdx b/toml-reference/toml-reference.mdx
@@ -1,6 +1,6 @@
 ---
 title: TOML Reference
-description: Complete reference for all parameters available in Cerebrium's default`cerebrium.toml` configuration file.
+description: Complete reference for all parameters available in Cerebrium's default `cerebrium.toml` configuration file.
 ---
 
 The configuration is organized into the following main sections:
@@ -25,12 +25,45 @@ The `[cerebrium.deployment]` section defines core deployment settings.
 | shell_commands        | string[] | []                     | Commands to run at the end of the build                     |
 | pre_build_commands    | string[] | []                     | Commands to run before dependencies install                 |
 | docker_base_image_url | string   | "debian:bookworm-slim" | Base Docker image                                           |
+| use_uv                | boolean  | false                  | Use UV for faster Python package installation               |
 
 <Info>
   Changes to python_version or docker_base_image_url trigger full rebuilds since
   they affect the base environment.
 </Info>
 
+### UV Package Manager
+
+UV is a fast Python package installer written in Rust that can significantly speed up deployment times. When enabled, UV will be used instead of pip for installing Python dependencies.
+
+<Info>
+UV typically installs packages 10-100x faster than pip, especially beneficial for:
+
+- Large dependency trees
+- Multiple packages
+- Clean builds without cache
+
+</Info>
+
+**Example with UV enabled:**
+
+```toml
+[cerebrium.build]
+use_uv = true
+```
+
+### Monitoring UV Usage
+
+Check your build logs for these indicators:
+
+- **UV_PIP_INSTALL_STARTED** - UV is successfully being used
+- **PIP_INSTALL_STARTED** - Standard pip installation (when `use_uv=false`)
+
+<Warning>
+  While UV is compatible with most packages, some edge cases may cause build
+  failures, such as legacy packages with non-standard metadata.
+</Warning>
+
 ## Runtime Configuration
 
 The `[cerebrium.runtime.custom]` section configures custom web servers and runtime behavior.
@@ -44,7 +77,7 @@ The `[cerebrium.runtime.custom]` section configures custom web servers and runti
 
 <Info>
   The port specified in entrypoint must match the port parameter. All endpoints
-  will be available at `https://api.cortex.cerebrium.ai/v4/{project - id}/
+  will be available at `https://api.aws.us-east-1.cerebrium.ai/v4/{project - id}/
   {app - name}/your/endpoint`
 </Info>
 
@@ -158,6 +191,8 @@ exclude = [".*"]
 shell_commands = []
 pre_build_commands = []
 docker_base_image_url = "debian:bookworm-slim"
+use_uv = true
+# Enable fast package installation with UV (omit or set to false if you want to use pip)
 
 [cerebrium.runtime.custom]
 port = 8000
diff --git a/v4/examples/aiVoiceAgents.mdx b/v4/examples/aiVoiceAgents.mdx
@@ -493,7 +493,7 @@ We created a public fork of the PipeCat frontend to show you a nice demo of this
 Follow the instructions in the README.md and then populate the following variables in your .env.development.local
 
 ```
-VITE_SERVER_URL=https://api.cortex.cerebrium.ai/v4/p-xxxxx/<APP_NAME> #This is the base url. Do not include the function names
+VITE_SERVER_URL=https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxx/<APP_NAME> #This is the base url. Do not include the function names
 VITE_SERVER_AUTH= #This is the JWT token you can get from the API Keys section of your Cerebrium Dashboard.
 ```
 
@@ -503,4 +503,4 @@ You can now run yarn dev and go to the URL: http://localhost:5173/ to test your
 
 This tutorial provides a foundation for implementing voice in your app and extending into image and vision capabilities. PipeCat is an extensible, open-source framework for building voice-enabled apps, while Cerebrium provides seamless deployment and autoscaling with pay-as-you-go compute.
 
-Tag us as **@cerebriumai** to showcase your work and join our [Slack](https://join.slack.com/t/cerebriumworkspace/shared_invite/zt-1qojg3eac-q4xyu5O~MeniNIg2jNeadg) or [Discord](https://discord.gg/ATj6USmeE2) communities for questions and feedback.
+Tag us as **@cerebriumai** to showcase your work and join our [Discord](https://discord.gg/ATj6USmeE2) community for questions and feedback.
diff --git a/v4/examples/asgi-gradio-interface.mdx b/v4/examples/asgi-gradio-interface.mdx
@@ -210,7 +210,7 @@ class GradioServer:
         interface.launch(
             server_name=self.host,
             server_port=self.port,
-            root_path=f"https://api.cortex.cerebrium.ai/v4/{os.getenv('PROJECT_ID')}/{os.getenv('APP_NAME')}/",
+            root_path=f"https://api.aws.us-east-1.cerebrium.ai/v4/{os.getenv('PROJECT_ID')}/{os.getenv('APP_NAME')}/",
             quiet=True
         )
 
@@ -350,7 +350,7 @@ class GradioServer:
         interface.launch(
             server_name=self.host,
             server_port=self.port,
-            root_path=f"https://api.cortex.cerebrium.ai/v4/{os.getenv('PROJECT_ID')}/{os.getenv('APP_NAME')}/",
+            root_path=f"https://api.aws.us-east-1.cerebrium.ai/v4/{os.getenv('PROJECT_ID')}/{os.getenv('APP_NAME')}/",
             quiet=True
         )
 
@@ -445,7 +445,7 @@ cerebrium deploy -y
 Once deployed, navigate to the following URL in your browser:
 
 ```
-https://api.cortex.cerebrium.ai/v4/p-<YOUR_PROJECT_ID>/2-gradio-interface/
+https://api.aws.us-east-1.cerebrium.ai/v4/p-<YOUR_PROJECT_ID>/2-gradio-interface/
 ```
 
 You should see the Gradio chat interface.
diff --git a/v4/examples/comfyUI.mdx b/v4/examples/comfyUI.mdx
@@ -343,7 +343,7 @@ Now you can now deploy your application by running: `cerebrium deploy`
 Once your ComfyUI application has been deployed successfully, you should be able to make a request to the endpoint using the following JSON payload:
 
 ```curl
-curl --location 'https://api.cortex.cerebrium.ai/v4/p-xxxx/1-comfyui/run' \
+curl --location 'https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxx/1-comfyui/run' \
 --header 'Content-Type: application/json' \
 --header 'Authorization: Bearer <YOUR TOKEN HERE>' \
 --data '{"workflow_values": {
diff --git a/v4/examples/langchain.mdx b/v4/examples/langchain.mdx
@@ -198,7 +198,7 @@ cerebrium deploy
 After deployment, make this request:
 
 ```curl
-curl --location --request POST 'https://api.cortex.cerebrium.ai/v4/p-xxxxxx/1-langchain-QA/predict' \
+curl --location --request POST 'https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxxx/1-langchain-QA/predict' \
 --header 'Authorization: Bearer <YOUR TOKEN HERE>' \
 --header 'Content-Type: application/json' \
 --data-raw '{
diff --git a/v4/examples/livekit-outbound-agent.mdx b/v4/examples/livekit-outbound-agent.mdx
@@ -384,8 +384,7 @@ EXPOSE 8600
 CMD ["python3", "main.py", "start"]
 ```
 
-
- In our `cerebrium.toml`, edit the values with the following contents:
+In our `cerebrium.toml`, edit the values with the following contents:
 
 ```
 [cerebrium.deployment]
diff --git a/v4/examples/mistral-vllm.mdx b/v4/examples/mistral-vllm.mdx
@@ -158,7 +158,7 @@ cerebrium deploy
 After deployment, make this request:
 
 ```curl
-curl --location --request POST 'https://api.cortex.cerebrium.ai/v4/p-<YOUR PROJECT ID>/1-faster-inference-with-vllm/predict' \
+curl --location --request POST 'https://api.aws.us-east-1.cerebrium.ai/v4/p-<YOUR PROJECT ID>/1-faster-inference-with-vllm/predict' \
 --header 'Authorization: Bearer <YOUR TOKEN HERE>' \
 --header 'Content-Type: application/json' \
 --data-raw '{
diff --git a/v4/examples/openai-compatible-endpoint-vllm.mdx b/v4/examples/openai-compatible-endpoint-vllm.mdx
@@ -117,7 +117,7 @@ cerebrium deploy
 After deployment, you'll see a curl command like this:
 
 ```curl
-curl --location 'https://api.cortex.cerebrium.ai/v4/p-<YOUR PROJECT ID>/5-openai-compatible-endpoint/{function}' \
+curl --location 'https://api.aws.us-east-1.cerebrium.ai/v4/p-<YOUR PROJECT ID>/5-openai-compatible-endpoint/{function}' \
 --header 'Content-Type: application/json' \
 --header 'Authorization: Bearer <YOUR TOKEN HERE>' \
 --data '{"..."}'
@@ -130,7 +130,7 @@ import os
 from openai import OpenAI
 
 client = OpenAI(
-    base_url="https://api.cortex.cerebrium.ai/v4/p-xxxxxxx/5-openai-compatible-endpoint/run",
+    base_url="https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxxxx/5-openai-compatible-endpoint/run",
     api_key="<CEREBRIUM_JWT_TOKEN>",
 )
 
diff --git a/v4/examples/realtime-voice-agents.mdx b/v4/examples/realtime-voice-agents.mdx
@@ -470,7 +470,7 @@ We created a public fork of the PipeCat frontend to show you a nice demo of this
 Follow the instructions in the README.md and then populate the following variables in your .env.development.local
 
 ```
-VITE_SERVER_URL=https://api.cortex.cerebrium.ai/v4/p-xxxxx/<APP_NAME> #This is the base url of your pipecat-agent. Do not include the function names
+VITE_SERVER_URL=https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxx/<APP_NAME> #This is the base url of your pipecat-agent. Do not include the function names
 VITE_SERVER_AUTH= #This is the JWT token you can get from the API Keys section of your Cerebrium Dashboard.
 ```
 
@@ -480,4 +480,4 @@ You can now run yarn dev and go to the URL: http://localhost:5173/ to test your
 
 This tutorial provides a foundation for implementing voice in your app and extending into image and vision capabilities. PipeCat is an extensible, open-source framework for building voice-enabled apps, while Cerebrium provides seamless deployment and autoscaling with pay-as-you-go compute.
 
-Tag us as **@cerebriumai** to showcase your work and join our [Slack](https://join.slack.com/t/cerebriumworkspace/shared_invite/zt-1qojg3eac-q4xyu5O~MeniNIg2jNeadg) or [Discord](https://discord.gg/ATj6USmeE2) communities for questions and feedback.
+Tag us as **@cerebriumai** to showcase your work and join our [Discord](https://discord.gg/ATj6USmeE2) community for questions and feedback.
diff --git a/v4/examples/sdxl.mdx b/v4/examples/sdxl.mdx
@@ -149,7 +149,7 @@ cerebrium deploy
 After deployment, make this request:
 
 ```curl
-curl --location 'https://api.cortex.cerebrium.ai/v4/p-<YOUR PROJECT ID>/3-sdxl-refiner/predict' \
+curl --location 'https://api.aws.us-east-1.cerebrium.ai/v4/p-<YOUR PROJECT ID>/3-sdxl-refiner/predict' \
 --header 'Content-Type: application/json' \
 --header 'Authorization: Bearer <YOUR TOKEN HERE>' \
 --data '{
diff --git a/v4/examples/streaming-falcon-7B.mdx b/v4/examples/streaming-falcon-7B.mdx
@@ -186,7 +186,7 @@ After deployment, make this request:
 </Note>
 
 ```curl
-curl --location --request POST 'https://api.cortex.cerebrium.ai/v4/p-<YOUR PROJECT ID>/5-streaming-endpoint/stream' \
+curl --location --request POST 'https://api.aws.us-east-1.cerebrium.ai/v4/p-<YOUR PROJECT ID>/5-streaming-endpoint/stream' \
 --header 'Authorization: Bearer <YOUR TOKEN HERE>' \
 --header 'Content-Type: application/json' \
 --data-raw '{
diff --git a/v4/examples/tensorRT.mdx b/v4/examples/tensorRT.mdx
@@ -320,7 +320,7 @@ Now that our code is ready, deploy the app with the command: `cerebrium deploy`.
 Initial deployment takes about 15-20 minutes to install packages, download the model, and convert it to the TensorRT-LLM format. Once completed, it outputs a curl command you can use to test your inference endpoint.
 
 ```
-curl --location 'https://api.cortex.cerebrium.ai/v4/p-xxxxxx/predict' \\
+curl --location 'https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxxx/predict' \\
 --header 'Content-Type: application/json' \\
 --header 'Authorization: Bearer <YOUR TOKEN HERE>' \\
 --data '{"prompt": "Tell me about yourself?"}'
diff --git a/v4/examples/transcribe-whisper.mdx b/v4/examples/transcribe-whisper.mdx
@@ -148,7 +148,7 @@ cerebrium deploy
 After deployment, make this request:
 
 ```curl
-curl --location 'https://api.cortex.cerebrium.ai/v4/p-<YOUR PROJECT ID>/1-whisper-transcription/predict' \
+curl --location 'https://api.aws.us-east-1.cerebrium.ai/v4/p-<YOUR PROJECT ID>/1-whisper-transcription/predict' \
 --header 'Content-Type: application/json' \
 --header 'Authorization: Bearer <YOUR TOKEN HERE>' \
 --data '{"file_url": "https://your-public-url.com/test.mp3"}''
diff --git a/v4/examples/twilio-voice-agent.mdx b/v4/examples/twilio-voice-agent.mdx
diff --git a/v4/examples/wandb-sweep.mdx b/v4/examples/wandb-sweep.mdx

Original file line number	Diff line number	Diff line change
`@@ -51,7 +51,7 @@ from openai import OpenAI`
`51`	`51`
`52`	`52`	`client = OpenAI(`
`53`	`53`	`# This is the default and can be omitted`
`54`		`- base_url="https://api.cortex.cerebrium.ai/v4/p-xxxxx/1-openai-compatible-endpoint/run", ##This is the name of the function you are calling`
	`54`	`+ base_url="https://api.aws.us-east-1.cerebrium.ai/v4/p-xxxxx/1-openai-compatible-endpoint/run", ##This is the name of the function you are calling`
`55`	`55`	`api_key="<CEREBRIUM_JWT_TOKEN>",`
`56`	`56`	`)`
`57`	`57`