AI Notes

Call Vertex AI endpoint

To call your endpoint using HTTP, you’ll need to construct a POST request with the correct authorization and data format. Here’s a breakdown and an example using curl:

1. Prerequisites

  • Endpoint ID: You’ll need the ID of your Vertex endpoint. You can find this in the Google Cloud Console or by using the Vertex AI SDK (as shown in the previous response).
  • Google Cloud Credentials: You’ll need credentials to authorize the request. The easiest way to do this from your local machine is to have the Google Cloud SDK (gcloud) installed and configured.
  • Project ID and Region: You will need your Google Cloud Project ID and the region where you deployed the endpoint.

2. Authorization

Vertex AI requests require an authorization header with a valid access token. If you have the Google Cloud SDK installed, you can obtain an access token using the following command:

gcloud auth print-access-token

3. Construct the HTTP Request

You’ll make a POST request to the Vertex AI API endpoint. The URL will look like this:

https://{region}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{region}/endpoints/{endpoint_id}:predict
  • {project_id}: Your Google Cloud Project ID.
  • {region}: The region where your endpoint is deployed (e.g., “us-central1”).
  • {endpoint_id}: The ID of your Vertex AI endpoint.

The request body should be a JSON object with an “instances” key. The value of “instances” is a list of data instances. In your case, each instance represents the features of a house for which you want to predict the price.

4. Example using curl

Here’s an example of how to call your endpoint using curl:

ACCESS_TOKEN=$(gcloud auth print-access-token)
PROJECT_ID="your-project-id"  # Replace with your Project ID
REGION="us-central1"      # Replace with your Region
ENDPOINT_ID="your-endpoint-id"  # Replace with your Endpoint ID

# Sample data (same as in the Python SDK example)
DATA='{
    "instances": [
        {
            "Size_LivingArea_SqFt": 2000,
            "Size_Lot_SqFt": 8000,
            "Size_TotalArea_SqFt": 2800,
            "Rooms_Total": 7,
            "Bedrooms": 3,
            "Bathrooms_Full": 2,
            "Bathrooms_Half": 1,
            "Basement_Area_SqFt": 800,
            "Basement_Finished": 1,
            "Garage_Cars": 2,
            "Fireplaces": 1,
            "Porch_Area_SqFt": 100,
            "Year_Built": 2000,
            "Year_Remodeled": 2010,
            "Condition_Overall": 7,
            "Quality_Overall": 7,
            "Building_Type": "House",
            "House_Style": "Ranch",
            "Foundation_Type": "Slab",
            "Roof_Material": "Composition Shingle",
            "Exterior_Material": "Brick",
            "Heating_Type": "Forced Air",
            "Cooling_Type": "Central AC",
            "Kitchen_Quality": "Good",
            "Bathroom_Quality": "Good",
            "Fireplace_Quality": "Average",
            "Basement_Quality": "Average",
            "Stories": 1,
            "Floor_Material": "Hardwood",
            "Neighborhood": "Bentonville Central",
            "Proximity_Schools_Miles": 0.5,
            "Proximity_Parks_Miles": 1.2,
            "Proximity_PublicTransport_Miles": 0.8,
            "Proximity_Shopping_Miles": 1.5,
            "Proximity_Hospitals_Miles": 2.0,
            "Safety_CrimeRate_Index": 65,
            "Environmental_NoiseLevel_dB": 45,
            "Environmental_AirQuality_Index": 35,
            "Flood_Zone": "No",
            "View": "None",
            "Time_of_Sale": "2024-08",
            "Interest_Rate": 6.2,
            "Inflation_Rate": 3.5,
            "Unemployment_Rate": 4.2,
            "Housing_Inventory": 0.05,
            "Economic_Growth_Rate": 2.5
        }
    ]
}'

# Construct the URL
URL="https://${REGION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${REGION}/endpoints/${ENDPOINT_ID}:predict"

# Make the POST request
curl -X POST \
     -H "Authorization: Bearer ${ACCESS_TOKEN}" \
     -H "Content-Type: application/json" \
     -d "${DATA}" \
     "${URL}"

Explanation:

  • ACCESS_TOKEN=$(gcloud auth print-access-token): Gets your current access token.
  • PROJECT_ID, REGION, ENDPOINT_ID: Replace these with your actual values.
  • DATA: A JSON string containing the input data. Crucially, it’s wrapped in an “instances” list.
  • URL: The Vertex AI API endpoint URL.
  • The curl command:
    • -X POST: Specifies the POST request method.
    • -H "Authorization: Bearer ${ACCESS_TOKEN}": Adds the authorization header.
    • -H "Content-Type: application/json": Sets the content type to JSON.
    • -d "${DATA}": Sends the JSON data in the request body.
    • ${URL}: The URL to send the request to.

5. Response

The response from the Vertex AI endpoint will be a JSON object with a “predictions” key. The value of “predictions” will be a list, where each element corresponds to the prediction for an instance in your input. In this case, you’ll get a list with a single element: the predicted house price.

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