RapidCanvas Docs
  • Welcome
  • GETTING STARTED
    • Quick start guide
    • Introduction to RapidCanvas
    • RapidCanvas Concepts
    • Accessing the platform
  • BASIC
    • Projects
      • Projects Overview
        • Creating a project
        • Reviewing the Projects listing page
        • Duplicating a Project
        • Modifying the project settings
        • Deleting Project(s)
        • Configuring global variables at the project level
        • Working on a project
        • Generating the about content for the project
        • Generating AI snippets for each node on the Canvas
        • Marking & Unmarking a Project as Favorite
      • Canvas overview
        • Shortcut options on canvas
        • Queuing the Recipes
        • Bulk Deletion of Canvas Nodes
        • AI Guide
      • Recipes
        • AI-assisted recipe
        • Rapid model recipe
        • Template recipe
        • Code Recipe
        • RAG Recipes
      • Scheduler overview
        • Creating a scheduler
        • Running the scheduler manually
        • Managing schedulers in a project
        • Viewing the schedulers in a project
        • Viewing the run history of a specific scheduler
        • Publishing the updated data pipeline to selected jobs from canvas
        • Fetching the latest data pipeline to a specific scheduler
        • Comparing the canvas of the scheduler with current canvas of the project
      • Predictions
        • Manual Prediction
        • Prediction Scheduler
      • Segments and Scenarios
      • DataApps
        • Model DataApp
        • Project Canvas Datasets
        • Custom Uploaded Datasets
        • SQL Sources
        • Documents and PDFs
        • Prediction Service
        • Scheduler
        • Import DataApp
    • Connectors
      • Importing dataset(s) from the local system
      • Importing Text Files from the Local System
      • Connectors overview
      • Connect to external connectors
        • Importing data from Google Cloud Storage (GCS)
        • Importing data from Amazon S3
        • Importing data from Azure Blob
        • Importing data from Mongo DB
        • Importing data from Snowflake
        • Importing data from MySQL
        • Importing data from Amazon Redshift
        • Importing data from Fivetran connectors
    • Workspaces
      • User roles and permissions
    • Artifacts & Models
      • Adding Artifacts at the Project Level
      • Adding Models at the Project Level
      • Creating an artifact at the workspace level
      • Managing artifacts at the workspace level
      • Managing Models at the Workspace Level
      • Prediction services
    • Environments Overview
      • Creating an environment
      • Editing the environment details
      • Deleting an environment
      • Monitoring the resource utilization in an environment
  • ADVANCED
    • Starter Guide
      • Quick Start
    • Setup and Installation
      • Installing and setting up the SDK
    • Helper Functions
    • Notebook Guide
      • Introduction
      • Create a template
      • Code Snippets
      • DataApps
      • Prediction Service
      • How to
        • How to Authenticate
        • Create a new project
        • Create a Custom Environment
        • Add a dataset
        • Add a recipe to the dataset
        • Manage cloud connection
        • Code recipes
        • Display a template on the UI
        • Create Global Variables
        • Scheduler
        • Create new scenarios
        • Create Template
        • Use a template in a flow notebook
      • Reference Implementations
        • DataApps
        • Artifacts
        • Connectors
        • Feature Store
        • ML model
        • ML Pipeline
        • Multiple Files
      • Sample Projects
        • Model build and predict
    • FAQ
      • FAQs
  • Additional Reading
    • Release Notes
      • April 21, 2025
      • April 01, 2025
      • Mar 18, 2025
      • Feb 27, 2025
      • Jan 27, 2025
      • Dec 26, 2024
      • Nov 26, 2024
      • Oct 24, 2024
      • Sep 11, 2024
        • Aug 08, 2024
      • Aug 29, 2024
      • July 18, 2024
      • July 03, 2024
      • June 19, 2024
      • May 30, 2024
      • May 15, 2024
      • April 17, 2024
      • Mar 28, 2024
      • Mar 20, 2024
      • Feb 28, 2024
      • Feb 19, 2024
      • Jan 30, 2024
      • Jan 16, 2024
      • Dec 12, 2023
      • Nov 07, 2023
      • Oct 25, 2023
      • Oct 01, 2024
    • Glossary
Powered by GitBook
On this page
  • New Features
  • Enhancements
  • Ask AI
  • React Dataapp Optimisations
  1. Additional Reading
  2. Release Notes

Feb 28, 2024

New Features

Following are the new features introduced in this release:

Embark on a rejuvenated user experience with the redesign of Projects, Dashboard, and View Data pages. This phase introduces the following key features:

  • Modern icons for datasets, artifacts, recipe types, models, and chart blocks, enhancing overall usability.

  • Refreshed color schemes for a contemporary and visually appealing interface.

  • Replacement of buttons with icons, aiming to improve visibility and accessibility.

  • Inclusion of clickable breadcrumbs on every page within the module, facilitating easy navigation and allowing users to backtrack or explore different sections effortlessly.

These updates ensure a consistent and intuitive user experience across all pages, with no alterations to core functionality. Stay tuned for upcoming releases as we continue to implement design enhancements across other modules.

Enhancements

Ask AI

  • Enhanced user experience with the Ask AI chat window by displaying dataset size metrics, including total rows and columns, for each output dataset, enabling users to gain insights at a glance.

  • Implemented intelligent detection of the output type to be generated based on text prompts, streamlining the process and making it more user-friendly.

  • Introduced the ability to stop query runs in progress using the stop button, allowing users to modify prompts without waiting for prior prompts to complete execution.

  • Implemented comprehensive logs for the Ask AI recipe type, providing a detailed record of both successful and failed recipes. In the event of a failed execution, users can refer to the logs to identify errors and warnings, facilitating a more efficient troubleshooting process.

React Dataapp Optimisations

  • Efficiency improvement in the overall relaunch duration. The initial launch of the day may require more time, but subsequent relaunches will be expedited.

  • Implemented a smooth shutdown process for the data app and managed deployment failures related to dependency issues in the data app.

PreviousMar 20, 2024NextFeb 19, 2024

Last updated 2 months ago