ARPIA Docs

Step-by-Step Guide to Creating a Reasoning Flow

Updated October 26, 2026

Creating a Reasoning Flow#

A step-by-step guide to creating your first Reasoning Flow and adding objects.


Prerequisites#

Before starting, ensure you have:


Step 1: Create a Reasoning Flow#

  1. Navigate to Reasoning Flows from the main menu.
Reasoning Flows navigation
Reasoning Flows navigation
  1. Click the + button to create a new Flow.

  2. Fill in the New Flow form:

Field Description
Name A descriptive name for your Flow (e.g., "Sales_Data_Pipeline")
Project Type How the Flow will be executed (see below)
Project Category Organizational label for filtering (see below)
Repository The data repository this Flow will use
Description Optional description of the Flow's purpose

Project Types#

Type Description Best For
Batch Process Runs manually or on a schedule ETL pipelines, model training, reporting
Interactive Process Runs on demand via UI with user parameters Parameterized analytics, experiment triggers
API Calls Exposed as REST endpoint Prediction APIs, webhook-triggered tasks
AI Tools Collection Each child object is an independently-callable tool AI agent/assistant tools, MCP servers, API Collections
WARP Persistent HTTP service (FastAPI), long-running Custom APIs, daemons, persistent microservices

Project Categories#

Category Description
Data Pipeline Ingestion and extraction workflows
Data Preparation & Transform Data cleaning, formatting, preprocessing
AI/ML Workflow Training, deploying, or predicting with ML models
Processes Workflow Orchestrating multiple logic layers or dependencies
Big Data Process Distributed or parallel data tasks
Notifications Automated alert or messaging workflows
Sequential Code Process Chained execution or custom scripts
API Services exposed to external systems
KOEL Orchestrates multi-step workflows
WARP Service Groups WARP projects: persistent HTTP services and daemons (e.g. FastAPI apps) exposed via the /warp/ gateway

Note: Project categories are organizational labels — they don't affect runtime behavior.

  1. Click Create.
New Flow form
New Flow form
  1. Click OK on the confirmation popup.
Confirmation popup

Checkpoint: Your new Flow should now appear in the Reasoning Flows list.


Step 2: Create an Object#

  1. Click Enter on your newly created Flow to open it.
Enter Flow button
  1. Inside the Flow, click New Object (or the + button).
New Object button
  1. Fill in the New Object form:
Field Description
Type The functional category of the object
Development Environment The specific tool or runtime (varies by Type)
Name A descriptive name for the object
Description Optional description

Object Types and Development Environments#

Select a Type first, then choose the appropriate Development Environment:


Extract & Load

For automated data extraction from registered data sources.

Development Environment Description
AP DataPipe Engine - MySQL GUI-based extraction from MySQL-compatible databases
AP DataPipe Engine - File GUI-based extraction from file sources (CSV, JSON)
Python 3.12 DataPipe Engine Code-driven extraction for custom data sources

Repository Table

For registering and managing tables in your data repository.


Transform & Prepare

For refining data before analytics or modeling.

Development Environment Description
AP Prepared Table Converts raw tables into analysis-ready datasets
AP Transform String to Binary Converts text fields to binary representations
AP Transform String to Numeric Converts text fields to numeric values
AP Transform Dates to Numeric Converts date fields to numeric timestamps
AP SQL Code Execution Executes custom SQL logic
AP Model Render Generates formatted outputs from model results
SingularAI Text Splitter Segments text into smaller chunks for processing

AI & Machine Learning

For model training, deployment, and prediction.

Development Environment Description
AP AutoML Engine Low-code model development with visual workflows
AP AutoML GPU Engine GPU-accelerated training for large datasets
SingularAI Text Embeddings Generates vector representations of text
AP Generative AI Workflow Integrates generative AI into pipelines

When to use GPU: Choose AP AutoML GPU Engine for datasets >100,000 rows, deep learning models, or when standard training is too slow.


Visual Object

For custom applications, APIs, and visualizations.

Development Environment Description Recommended For
PHP 8.2 Application Modern PHP with latest features New PHP projects
PHP 7.4 Application Legacy PHP support Existing projects requiring older dependencies
Python 3.8 Advanced ML Application ML workflows with scikit-learn, TensorFlow, PyTorch Machine learning
Python 3.8 Advanced ML & Plotly ML with interactive visualizations Dashboards, charts
Python 3 FastAPI High-performance REST APIs API development
Python 3.9 Google Cloud Speech Audio transcription Speech-to-text
ARPIA Python Notebook Interactive development environment Exploration, prototyping

Data Models (Nodes)

For defining semantic entities and documenting datasets in the Reasoning Atlas.


AI Apps

For orchestrating multiple objects into complete, deployable applications.


App Droplet Object

For publishing applications to external servers.

Development Environment Description
PHP Traditional web applications and backend interfaces
Python APIs, microservices, ML model endpoints

  1. Click Create.
New Object form
  1. Click OK on the confirmation popup.
Object confirmation
  1. Your Object editor will open, ready for development.
Object editor screen

Next Steps#

Now that you've created a Flow and Object, you can:

  • Write code in the editor using PHP or Python
  • Add a config.html to create an interactive configuration form (see Configurable Objects Overview)
  • Connect to your Repository using the database helpers (dkconnect for Python, datakubes-connect.php for PHP)
  • Schedule execution for batch processing
  • Add more Objects to build complete workflows

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