Building AI-Powered Workflows with Data Flow Platform

Learn how to create automated workflows that combine AI models, application integrations, and ETL processes using the Data Flow Platform. This tutorial demonstrates how to build and deploy intelligent workflows as standalone AI agents accessible via API or web interface.

Understanding Data Flow Platform Components

Before diving into workflow creation, let's understand the key components:

1. Workflow Management: Create, manage, and monitor workflows.
2. Workflow Execution: Runtime environment for processing workflow. Based on Directed Acyclic Graphs (DAGs), similar to Apache Airflow.
3. Data Converters: Handlers for data transformation between different formats.
4. Data Sources: Connectors for data sources.
5. Data Operations: Extract, transform, and load data.

Creating Your First Workflow

Design the Workflow Structure

The first step is to design the workflow structure.

The workflow structure is a directed acyclic graph (DAG) that defines the workflow's nodes and their connections. We will start with a simple workflow that is sequence of tasks executed one after the other.

For this example, we will create a workflow that Converts Text to Speech (TTS).

Implementing the Text-to-Speech Workflow

Let's create a workflow that converts text to speech using OpenAI's API. Here's the workflow definition:

{
  "title": "Text to Speech Workflow",
  "description": "Converts input text to speech using OpenAI's Text-to-Speech (TTS) model",
  "inputs": [
    {
      "name": "text",
      "type": "text",
      "description": "The text to convert to speech"
    },
    {
      "name": "voice",
      "type": "text",
      "description": "The voice to use (alloy, echo, fable, onyx, nova, or shimmer)",
      "default": "alloy"
    }
  ],
  "tasks": [
    {
      "name": "text_to_speech",
      "type": "openai.audio.text_to_speech",
      "inputs": {
        "text": "{{inputs.text}}",
        "voice": "{{inputs.voice}}",
        "model": "tts-1"
      }
    }
  ],
  "outputs": [
    {
      "name": "audio_file",
      "type": "file",
      "value": "{{tasks.text_to_speech.outputs.audio}}",
      "description": "The generated audio file"
    }
  ]
}

Executing the Workflow

Once you've created the workflow, you can execute it using the API:

# First, create the workflow
curl -X POST https://api.dataflow.wiki/workflows \
  -H "Content-Type: application/json" \
  -d @workflow.json

# Execute the workflow with input
curl -X POST https://api.dataflow.wiki/workflows/wf-123/executions \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": {
      "text": "Hello world! This is a test of the text to speech system.",
      "voice": "nova"
    }
  }'

Monitoring and Error Handling

Setting Up Webhooks

To monitor workflow execution, you can configure webhooks:

{
  "monitoring": {
    "webhook_url": "https://your-server.com/webhook"
  }
}

Error Handling Strategies

1. Retry Logic: Configure retry attempts for failed tasks:

{
  "name": "api_call",
  "type": "http.request",
  "retry": {
    "attempts": 3,
    "delay": 5
  }
}

2. Fallback Tasks: Define alternative tasks to execute on failure:

{
  "name": "transcribe",
  "type": "openai.audio.transcription",
  "fallback": {
    "task": "whisper.transcribe",
    "inputs": {
      "audio": "{{tasks.extract_audio.outputs.audio}}"
    }
  }
}

Conclusion

The Data Flow Platform provides a powerful framework for building AI-powered workflows. By combining different task types and using features like parallel execution, conditional logic, and error handling, you can create sophisticated automation solutions that integrate various AI services and data processing steps.

Start small with simple workflows and gradually build more complex solutions as you become familiar with the platform's capabilities. Remember to implement proper monitoring and error handling to ensure your workflows run reliably in production.

For more examples and detailed documentation, visit our documentation site.