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You can start the automated model building process on the Plexe Platform by making a POST request to the model creation endpoint. Base URL: https://api.plexe.ai

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Prerequisites

  • You have a Plexe Platform account and a valid API Key.
  • (Optional but Recommended) You have uploaded your data and have the resulting upload_id(s).

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Authentication

Include your API key in the x-api-key header. 
import requests
import os
import json

api_key = os.getenv("PLEXE_API_KEY")
if not api_key:
    raise ValueError("Please set the PLEXE_API_KEY environment variable.")

base_url = "https://api.plexe.ai"
headers = {
    "x-api-key": api_key,
    "Content-Type": "application/json"
}

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Starting a Build Job

Make a POST request to the endpoint for creating models, typically including the desired model name in the path (e.g., /models/{model_name}). The request body contains the core configuration for the build:
  • goal: (Required) Natural language description of the model’s goal.
  • upload_id: (Required if not using purely synthetic generation based on goal/schema alone) Reference to your data. This could be:
    • An ID obtained from the data upload process.
    • A publicly accessible URL to a dataset (CSV, JSON, etc. - check API reference for supported URL types).
  • input_schema: (Optional) Dictionary defining the input features and types (e.g., {"feature1": "float", "feature2": "str"}). Plexe will try to infer if omitted and upload_id is provided.
  • output_schema: (Optional) Dictionary defining the output prediction(s) and types (e.g., {"prediction": "int", "probability": "float"}). Plexe will try to infer if omitted.
  • metric: (Optional) Suggest a primary metric to optimize (e.g., "accuracy", "rmse", "f1"). Plexe will select an appropriate default if omitted.
  • max_iterations: (Optional) Maximum number of different modeling approaches the agent system should try (default might be 1 or 3, check API reference). Higher values increase build time and cost but may yield better models.
  • provider: (Optional) Specify the LLM provider/model to use (e.g., "openai/gpt-4o-mini"). Uses the platform default if omitted. See Configure LLM Providers (concepts apply similarly here).
model_name = "customer-churn-predictor-v1" # Choose a unique name for your model
upload_id = "YOUR_UPLOAD_ID" # Replace with the ID from your data upload

build_payload = {
  "goal": "Predict customer churn based on usage patterns like login frequency, support tickets, and subscription duration.",
  "upload_id": upload_id, # Use the ID obtained from data upload
  # Optionally provide schemas if inference is not desired or needs guidance
  # "input_schema": {
  #   "login_frequency_last_30d": "int",
  #   "support_tickets_last_90d": "int",
  #   "subscription_months": "int",
  #   "plan_type": "str"
  # },
  # "output_schema": {
  #   "churn_prediction": "int" # e.g., 1 for churn, 0 for no churn
  # },
  "metric": "f1", # Optimize for F1 score
  "max_iterations": 5 # Try up to 5 approaches
}

print(f"Starting build for model: {model_name}")

try:
    response = requests.post(
        f"{base_url}/models/{model_name}",
        headers=headers,
        json=build_payload
    )
    response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)

    build_result = response.json()
    model_id = build_result.get("model_id") # Typically format: name:version or similar

    print("\nBuild request submitted successfully!")
    print(f"  Model ID: {model_id}")
    print(f"  Initial Status: {build_result.get('status')}")
    print(f"Monitor progress using the status endpoint.")

    # Store model_id for status checking and inference later
    # Example: store model_name and model_version if ID is like "name:version"
    if model_id and ':' in model_id:
        m_name, m_version = model_id.split(':', 1)
        print(f"  Model Name: {m_name}, Model Version: {m_version}")
    else:
        # Handle cases where model_id format might differ
        print("  Could not parse model name/version from model_id.")


except requests.exceptions.HTTPError as http_err:
    print(f"HTTP error occurred: {http_err}")
    print(f"Response Text: {http_err.response.text}")
except requests.exceptions.ConnectionError as conn_err:
    print(f"Connection error occurred: {conn_err}")
except Exception as err:
    print(f"Other error occurred: {err}")

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Checking Build Status

After submitting a build request, you’ll want to monitor its progress. Use the status endpoint to check on your model’s build status: 
def check_build_status(model_name, model_version):
    """Check the status of a model build"""
    try:
        status_url = f"{base_url}/models/{model_name}/{model_version}/status"
        response = requests.get(
            status_url,
            headers=headers
        )
        response.raise_for_status()
        status_data = response.json()
        
        # Print current status information
        print(f"Model: {model_name} (Version: {model_version})")
        print(f"Status: {status_data.get('status')}")
        print(f"Updated: {status_data.get('updated_at')}")
        
        # If there's an error message, display it
        if status_data.get('error_message'):
            print(f"Error: {status_data.get('error_message')}")
            
        return status_data
        
    except requests.exceptions.HTTPError as http_err:
        print(f"HTTP error occurred: {http_err}")
        return None
    except Exception as err:
        print(f"Error checking status: {err}")
        return None

# Example usage after getting model_name and model_version from the build response
if model_id and ':' in model_id:
    m_name, m_version = model_id.split(':', 1)
    status = check_build_status(m_name, m_version)
Once your model’s status is "completed", you can proceed to making inferences with it using the deployed model inference API.
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