Overview

Welcome to Aparavi Data Toolchain

The Aparavi Data Toolchain is a visual workflow builder for creating sophisticated data processing and AI pipelines. It enables you to connect various data sources, transformation tools, and AI capabilities through an intuitive drag-and-drop interface without extensive coding.

With Aparavi, you can easily build complex workflows that process, transform, and analyze your data across multiple sources and destinations, making it ideal for data integration, transformation, and AI-powered analytics projects.

Basic Interface Overview

The Aparavi interface consists of several key areas:

  • Access to project management, workflow execution, and settings
  • Library of available components organized by category
  • The main workspace where you build your workflow
  • Configure the selected node’s settings
  • View logs and execution results

Navigation

  • Home
  • Projects (with an existing project, or with no projects)
  • System

    Main Workflow Canvas 

    The Main Workflow Canvas is the core interface of the Data Toolchain for AI, where users build, visualize, and manage their data workflows.

    Key Interface Elements 

    • Project Header
      • Project Name Field – Allows you to name or rename the current project.
      • Save Button – Saves the current state of the project.
      • Delete Button – Deletes the current project.
      • Log Viewer – Displays project logs when clicked.
    • Node Library – Organized into categories. Clicking a node will allow you to drag it into the canvas to start building your pipeline.
      • Source – Connects to external systems and data repositories to bring raw or unstructured data into the workflow.
        • Examples – Google Drive, AWS S3, Outlook, SMB/NAS, Local File System.
      • Embedding – Transforms text or images into vector embeddings, enabling downstream AI models to work with structured representations of unstructured content.
        • Example – Embedding – Image.
      • LLMs – Large Language Models used to generate, summarize, classify, or transform text based on context or input from previous nodes. 
      • Database – Connects to SQL or NoSQL databases, allowing for queries, lookups, or data enrichment within a pipeline. 
      • Image – Handles image-specific tasks like resizing, format conversion, or preprocessing before embedding or analysis. 
      • Store – Used to write and persist processed output to a destination, such as cloud buckets, local drives, or database tables. 
      • Text – Text manipulation utilities such as extraction, formatting, cleansing, or splitting operations within the workflow.
      • Audio – Processes audio input — this may include transcription, format conversion, or embedding audio for ML usage. 
      • Data – Utility nodes that help manage data structures, formats, and flow — such as joins, filters, or conditional logic. 
      • Infrastructure – Nodes for orchestration, automation, or triggering — potentially linked to Webhooks, schedules, or external systems. 
    • Canvas Area – Each workflow step is built visually here in the blank workspace by dragging and connecting nodes.
    • Canvas Controls
      • Zoom In / Zoom Out – Adjust canvas zoom level.
      • Move / Pan – Navigate the canvas by dragging.
      • Add Node Shortcut – Quickly access node library.
      • Lock Canvas – Prevent accidental movement or edits.
      • Reset View – Center the canvas view.