Python SDK Quickstart

What you’ll learn

How to call Aparavi APIs
How to manage third-party dependencies using npm
How to install the latest Aparavi DTC Python SDK
How to send your first SDK request

Key Benefits

- Simplified Integration – Streamlined API calls with built-in error handling
- Pipeline Validation – Catch configuration errors before execution
- Real-time Monitoring – Track pipeline status and progress
- Automated Workflows – Build robust data processing automation
- Flexible Data Input – Support for webhook-based file submission
- Resource Management – Proper cleanup and teardown capabilities

Initial setup

First, create an Aparavi account or sign in to get your API credentials.

Install the TestPyPI package

Check your Node.js version:

pip install -i https://test.pypi.org/simple/ aparavi-dtc-sdkpip install -i

Run your first SDK request

Now that you have the SDK installed, you can validate a pipeline configuration and start a data transfer task with a couple API requests.

Create a .env file

Linux/macOS

touch .env

Windows:

type nul > .env

This is what you should have in your .env file:

APARAVI_API_KEY=aparavi-dtc-api-key 
APARAVI_BASE_URL=https://eaas-dev.aparavi.com

Note: This sample uses your API key for your sandbox environment. Only you can see these values.

quick_start.py:

import os

from dotenv import load_dotenv
from aparavi_dtc_sdk import AparaviClient

load_dotenv()

client = AparaviClient(
    base_url=os.getenv("APARAVI_BASE_URL"),
    api_key=os.getenv("APARAVI_API_KEY")
)

result = client.execute_pipeline_workflow(
    pipeline="./pipeline_config.json",
    file_glob="./*.png"
)

print(result)

Save the file as quick_start.py. This will run your pipeline. Navigate to your directory and run:

python quick_start.py

Core Functions

Function	Purpose	Description
`validate_pipeline`	Pipeline Validation	Validates a pipeline structure against Aparavi’s backend to ensure it is correctly formed before execution, helping catch configuration errors early in the development process.
`execute_pipeline`	Pipeline Execution	Submits a pipeline for execution on the Aparavi platform, enabling automated processing of your data workflows with proper error handling and status tracking.
`execute_pipeline_workflow`	End-to-End Workflow Execution	Performs a full end-to-end execution lifecycle for a pipeline, combining multiple operations into a single streamlined call for simplified automation.
`get_pipeline_status`	Status Monitoring	Fetches the current execution status of a pipeline task, allowing you to monitor progress, check for completion, and handle any execution issues that may arise.
`teardown_pipeline`	Pipeline Management	Gracefully ends a running pipeline task, ensuring proper cleanup of resources and allowing for controlled termination of long-running processes.
`send_payload_to_webhook`	Data Submission	Sends file(s) to a running webhook-based pipeline, enabling real-time data processing and integration with external systems and applications.
`get_version`	Version Management	Fetches the current version of the Aparavi API/backend, ensuring compatibility and helping maintain proper version control in your applications.

Pre-Built Pipelines

Available pre-built pipeline configurations:

AUDIO_AND_SUMMARY: Processes audio content and produces both a transcription and a concise summary.
SIMPLE_AUDIO_TRANSCRIBE: Processes audio files and returns transcriptions of spoken content.
SIMPLE_PARSER: Extracts and processes metadata and content from uploaded documents.

import os

from dotenv import load_dotenv
from aparavi_dtc_sdk import AparaviClient, PredefinedPipeline # Import PredefinedPipeline enum

load_dotenv()

client = AparaviClient(
    base_url=os.getenv("APARAVI_BASE_URL"),
    api_key=os.getenv("APARAVI_API_KEY")
)

result = client.execute_pipeline_workflow(
    pipeline=PredefinedPipeline.SIMPLE_AUDIO_TRANSCRIBE, # Specify PredefinedPipeline
    file_glob="./audio/*.mp3"
)

print(result)

Power User Quick Start

If you are comfortable with more control over running your pipeline, consider the following:

import json
import os

from dotenv import load_dotenv
from aparavi_dtc_sdk import AparaviClient

load_dotenv()

client = AparaviClient(
    base_url=os.getenv("APARAVI_BASE_URL"),
    api_key=os.getenv("APARAVI_API_KEY")
)

try:
    validation_result = client.validate_pipeline("pipeline_config.json")
except Exception as e:
    print(f"Validation failed: {e}")

try:
    start_result = client.execute_pipeline(pipeline_config, name="my-task")
    if start_result.status == "OK":
        token = start_result.data["token"]
        task_type = start_result.data["type"]

        status_result = client.get_pipeline_status(token=token, task_type=task_type)

        end_result = client.teardown_pipeline(token=token, task_type=task_type)

except Exception as e:
    print(f"Task operation failed: {e}")

Next steps

Next steps: Explore the full API reference to learn about all available methods and features.