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Version: 0.8

Read Online Features for Inference using the Python Client

Tecton provides an open-source client library in Python for reading features from the online store. The Python Client API Reference can be found here.

Installing the Python Client library

The latest version of the Tecton Python Client can be found on PyPi.

For any project, install the client library using pip:

pip install tecton-client

Creating an API key to authenticate to the HTTP API

To authenticate your requests to the HTTP API, you will need to create a Service Account to obtain an API key, and grant that Service Account the Consumer role for your workspace:

  1. Create a Service Account to obtain your API key.
tecton service-account create \
--name "sample-service-acount" \
--description "An online inference sample"


Save this API Key - you will not be able to get it again.
API Key: <Your-api-key>
Service Account ID: <Your-Service-Account-Id>
  1. Assign the Consumer role to the Service Account.
tecton access-control assign-role --role consumer \
--workspace <Your-workspace> \
--service-account <Your-Service-Account-Id>


Successfully updated role.

Save this API key for the next step.

Creating a TectonClient Object

Create a TectonClient object with your Tecton instance base URL (https://<your Tecton instance prefix> and your API key:

tecton_client = TectonClient(url, api_key)

Calling the get-features Endpoint

To call the get-features endpoint using the client, create objects from the GetFeaturesRequestData and GetFeaturesRequest classes.

get_features_request_data = GetFeaturesRequestData(
join_key_map={"user_id": "123", "merchant": "xyz"}, request_context_map={"amt": 500.00}

get_features_request = GetFeaturesRequest(

Now, call the get_features() method in the tecton_client and pass the get_features_request object as a parameter.

This method:

  • Creates an HTTP request using the URL, api_key and the get_features_request object
  • Calls the FeatureService API
  • Receives the response from the API and deserializes it into the get_features_response object
get_features_response = tecton_client.get_features(get_features_request)

The feature vector can be accessed as a dictionary of feature names and their corresponding values using the property feature_values from the get_features_response.

for name, feature in get_features_response.feature_values.items():
print("Feature Name: ", name)
print("Feature Namespace: ", feature.feature_namespace)
print("Feature Name: ", feature.feature_name)
print("Feature Value: ", feature.feature_value)

# Data type of the feature value
print("Feature Value Type: ", feature.data_type)

# These two fields are only available if requested in the metadata options of the test_request
print("Feature Status: ", feature.feature_status)
print("Feature Effective Time: ", feature.effective_time)

For more information on using the client library and other functions like get-features-batch/ refer to the official documentation.

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