Visual Object Overview

The Reasoning Flows layer for visualization, reporting, and delivery of data insights.

🧭 Purpose

The Visual Object tools in Reasoning Flows enable the creation of visual representations and reports based on already processed and transformed data.
These objects help teams illustrate trends, patterns, and insights through notebooks, APIs, and application environments — turning data into shareable intelligence.

🔹 Where It Fits in Reasoning Flows

In the Reasoning Flows architecture:

Extract & Load → Brings data into the platform.
Transform & Prepare → Cleans and structures the data.
AI & Machine Learning → Builds and trains predictive models.
Visual Objects → Display and communicate outcomes through visualizations, dashboards, or APIs.
Knowledge Atlas → Documents, contextualizes, and connects these insights within the organization’s knowledge framework.

Goal: Visual Objects are the delivery layer of Reasoning Flows — transforming prepared and modeled data into interpretable insights.

🧩 Development Environments

High Performance Computing (HPC)

These objects provide open coding environments where developers can write and execute code to create custom logic, backend services, or visual applications.
They are ideal for teams building dashboards, REST APIs, or dynamic reporting systems.

Key Objects:

PHP 7.4 Application – Legacy environment for custom backend or procedural logic.
PHP 8.2 Application – Modern PHP runtime with improved performance and security.
Python 3.8 Advanced ML Application – Full Python environment for advanced analytics and integration tasks.
Python 3 FastAPI – Optimized for deploying real-time APIs, microservices, or visual applications.

Notebooks

The Reasoning Flows Python Notebook is an interactive cell-based coding environment that enables live data analysis, visualization, and automation.
Users can write, test, and execute Python code directly in the platform — leveraging Reasoning Flows resources, datasets, and libraries.

Key Object:

Reasoning Flows Python Notebook

Use Case: Exploratory data analysis, testing model outputs, creating plots, and documenting workflows.

Notification Engine

The Notification Engine allows users to configure and customize automated email systems within Reasoning Flows.
It is commonly used to distribute visual reports, model summaries, or status alerts to stakeholders.

Key Object:

AP Notification Engine
GUI-based configuration for designing templates, triggers, and delivery rules.
Requires a Mailgun API key for operation.

Prepare & Transform Tools

These tools refine data outputs before visualization or model inference.
They enable flexible table modification, field reformatting, and metric rendering, ensuring that data feeding visual reports is standardized and ready for consumption.

Key Object:

AP Model Render
Executes trained models or transformations and writes structured results into tables — often used to prepare KPIs or dashboard datasets.

Web-Hook Sender

The Web-Hook Sender object integrates Reasoning Flows with external systems by sending web-hook events or JSON payloads to defined endpoints.
Useful for triggering report updates, dashboard refreshes, or real-time notifications.

Key Object:

AP Web-Hook Sender
GUI-based setup for defining payloads and endpoints.

📘 Summary Table

Object Type	Purpose	Main Use Case	Examples
High Performance Computing	Open coding environments for custom visualization and logic.	Developing APIs, dashboards, or backend logic.	PHP 7.4 / 8.2 Application, Python FastAPI
Notebooks	Interactive Python coding with live visualization.	Data analysis, prototyping, documentation.	Reasoning Flows Python Notebook
Notification Engine	Configure and send automated email notifications.	Report delivery, alerts, workflow updates.	AP Notification Engine
Prepare & Transform Tools	Prepare final metrics or datasets for visual outputs.	Model rendering, data formatting.	AP Model Render
Web-Hook Sender	Send data or triggers to external systems.	Real-time event integration, dashboard refresh.	AP Web-Hook Sender

🧠 Best Practices

Use Python FastAPI for lightweight visualization or analytics APIs.
Automate report delivery through the Notification Engine or webhooks.
Always prepare clean, formatted data using AP Model Render before visualization.
Combine Notebooks with HPC environments for reproducible visual analytics workflows.
Document any output data or dashboards in the Knowledge Atlas for discoverability.

🔗 Related Documentation

Transform & Prepare Overview — Learn how to structure data before visualization.
AI & Machine Learning Overview — Explore how model outputs can be visualized through Reasoning Flows.
Knowledge Atlas Overview — See how visual results and KPIs are documented and connected semantically.