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"

Output:

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>

2. Assign the Consumer role to the Service Account.

tecton access-control assign-role --role consumer \
      --workspace <Your-workspace> \
      --service-account <Your-Service-Account-Id>

Output:

Successfully updated role.

Save this API key for the next step.

TectonClient Example

To call the get-features endpoint, first create a client using the base URL for your cluster (https://<your Tecton instance prefix>.tecton.ai) and the API key from the step above. Then call get_features(). Below is an example calling the fraud_detection_feature_service:v2 service in https://explore.tecton.ai/:

from tecton_client import TectonClient

tecton_client = TectonClient(url="https://explore.tecton.ai/", api_key="your-api-key", default_workspace_name="prod")

resp = client.get_features(
    feature_service_name="fraud_detection_feature_service:v2",
    join_key_map={"user_id": "user_4407104885"},
    request_context_map={"amount": 500.00},
)

print(resp.get_features_dict())

You can add additional metadata for debugging such as the the server response time using the metadata_options argument:

from tecton_client import MetadataOptions

resp = tecton_client.get_features(
    feature_service_name="fraud_detection_feature_service:v2",
    join_key_map={"user_id": "user_4407104885"},
    request_context_map={"amount": 500.00},
    metadata_options=MetadataOptions(include_slo_info=True),
)

print(resp.metadata)

AsyncTectonClient Example

The AsyncTectonClient has the same method signatures as the (synchronous) TectonClient, but is designed to leverage Python’s async/await syntax for non-blocking I/O operations. To use the asynchronous features, you will need some familiarity with Python’s async/await syntax. Replace your synchronous client calls with the async counterparts provided by Tecton, and manage them within an async function or an event loop.

import asyncio
from tecton_client import AsyncTectonClient

async_client = AsyncTectonClient(
    url="https://explore.tecton.ai/",
    api_key="your-api-key",
    default_workspace_name="prod",
)


async def fetch_data():
    resp = client.get_features(
        feature_service_name="fraud_detection_feature_service:v2",
        join_key_map={"user_id": "user_4407104885"},
        request_context_map={"amount": 500.00},
    )
    print(resp.result.features)


asyncio.run(fetch_data())

For more information on using the client library, refer to the official documentation.