tecton

import os from tecton import TectonClient # --- IMPORTANT: Setup for quickstart --- # 1. Set your Tecton API key as an environment variable: # export TECTON_API_KEY="YOUR_API_KEY" # 2. Replace 'your_workspace_name' with an actual workspace you have access to. # (Find in Tecton UI or via `tecton list workspaces` CLI command) # 3. Replace 'your_feature_service_name' and 'your_join_key' with actual values. # (Requires an existing FeatureService in your workspace) # ------------------------------------------- api_key = os.environ.get('TECTON_API_KEY', '') if not api_key: print("Warning: TECTON_API_KEY environment variable not set. This example will likely fail.") # In a real application, you might raise an error or exit. # Initialize the Tecton client try: client = TectonClient( workspace_name='your_workspace_name', # e.g., 'prod' api_key=api_key ) print(f"Successfully connected to Tecton workspace: {client.workspace_name}") # Example: Get online features for a specific entity. # This assumes you have a FeatureService configured in your workspace. # and it expects a specific join key (e.g., 'user_id'). feature_service_name = "your_feature_service_name" # e.g., 'user_transaction_counts_fs' join_keys = {"your_join_key": "entity_id_example"} # e.g., {"user_id": "user_123"} # Fetch features feature_vector = client.get_online_features( feature_service_name=feature_service_name, join_keys=join_keys ) if feature_vector and not feature_vector.empty: print(f"\nSuccessfully retrieved features from {feature_service_name}:") print(feature_vector.to_pandas()) else: print(f"\nNo features retrieved or feature vector is empty for {feature_service_name} with join keys {join_keys}.") print("Please check FeatureService name, join keys, and if features are materialized.") except Exception as e: print(f"\nError during Tecton client initialization or feature retrieval: {e}") print("Ensure your TECTON_API_KEY is valid, workspace_name exists, and network connectivity is present.")