Under Consideration
Linux Support
User problem Many potential users of Plotly Studio are operating on Linux hardware. Plotly Studio currently lacks support for these systems, preventing these users from utilizing the software. What is it? This is a proposed enhancement to Plotly Studio to introduce compatibility with Linux-based systems. It involves developing and testing specific versions or configurations of Plotly Studio that can run effectively on Linux computers. What does it allow users to do? This feature would allow users with Linux hardware to install, launch, and fully utilize Plotly Studio. It would enable them to leverage all of Plotly Studio's functionalities, including data import, AI-driven app generation, visualization, and deployment, without requiring a VM or using another computer. This ensures that a broader segment of our user base can access and benefit from Plotly Studio.
Matthew Brown 6 months ago
Plotly Studio
Under Consideration
Linux Support
User problem Many potential users of Plotly Studio are operating on Linux hardware. Plotly Studio currently lacks support for these systems, preventing these users from utilizing the software. What is it? This is a proposed enhancement to Plotly Studio to introduce compatibility with Linux-based systems. It involves developing and testing specific versions or configurations of Plotly Studio that can run effectively on Linux computers. What does it allow users to do? This feature would allow users with Linux hardware to install, launch, and fully utilize Plotly Studio. It would enable them to leverage all of Plotly Studio's functionalities, including data import, AI-driven app generation, visualization, and deployment, without requiring a VM or using another computer. This ensures that a broader segment of our user base can access and benefit from Plotly Studio.
Matthew Brown 6 months ago
Plotly Studio
Completed
Data Sources
This feature introduces native data connectors in Plotly Studio, enabling users to directly and securely link to popular cloud data warehouses and lakehouses like Snowflake and Databricks. This allows users to browse and select tables, execute SQL queries, import results for visualizations and applications, and build continuously updated data applications within Plotly Studio, overcoming current limitations with dynamic data sources.
Matthew Brown 6 months ago
Roadmap Candidate
Plotly Studio
Completed
Data Sources
This feature introduces native data connectors in Plotly Studio, enabling users to directly and securely link to popular cloud data warehouses and lakehouses like Snowflake and Databricks. This allows users to browse and select tables, execute SQL queries, import results for visualizations and applications, and build continuously updated data applications within Plotly Studio, overcoming current limitations with dynamic data sources.
Matthew Brown 6 months ago
Roadmap Candidate
Plotly Studio
Completed
Intel-based Mac Support
User Problem Many users of Plotly Studio are operating on older Mac hardware equipped with Intel chips. Plotly Studio currently lacks support for these systems, preventing these users from utilizing the software. While Apple has ceased manufacturing Intel-based Macs, a significant number of our users may still be relying on this hardware, hindering their ability to adopt or continue using Plotly Studio until they upgrade their systems. What is it? This is a proposed enhancement to Plotly Studio to introduce compatibility with Intel-based Mac systems. It involves developing and testing specific versions or configurations of Plotly Studio that can run effectively on Apple computers powered by Intel processors. This addresses the existing gap in hardware support, allowing the software to function natively on a wider range of Mac devices. What Does it Allow Users to Do? This feature would allow users with Intel-based Mac hardware to install, launch, and fully utilize Plotly Studio. It would enable them to leverage all of Plotly Studio's functionalities. Users would be able to access and benefit from Plotly Studio without requiring an upgrade to newer Apple Silicon-based hardware. This ensures that a broader segment of the user base can access the software, regardless of their Mac's processor architecture.
Matthew Brown 6 months ago
Plotly Studio
Completed
Intel-based Mac Support
User Problem Many users of Plotly Studio are operating on older Mac hardware equipped with Intel chips. Plotly Studio currently lacks support for these systems, preventing these users from utilizing the software. While Apple has ceased manufacturing Intel-based Macs, a significant number of our users may still be relying on this hardware, hindering their ability to adopt or continue using Plotly Studio until they upgrade their systems. What is it? This is a proposed enhancement to Plotly Studio to introduce compatibility with Intel-based Mac systems. It involves developing and testing specific versions or configurations of Plotly Studio that can run effectively on Apple computers powered by Intel processors. This addresses the existing gap in hardware support, allowing the software to function natively on a wider range of Mac devices. What Does it Allow Users to Do? This feature would allow users with Intel-based Mac hardware to install, launch, and fully utilize Plotly Studio. It would enable them to leverage all of Plotly Studio's functionalities. Users would be able to access and benefit from Plotly Studio without requiring an upgrade to newer Apple Silicon-based hardware. This ensures that a broader segment of the user base can access the software, regardless of their Mac's processor architecture.
Matthew Brown 6 months ago
Plotly Studio
In Progress
Scheduled Data Refresh & Caching
User Problem Users building data-driven applications in Plotly Studio currently have no way to trigger data updates, relying on hacky workarounds and re-publishing apps manually. This can lead to apps displaying stale data, requiring constant manual intervention, and hindering the ability to present timely and accurate insights to viewers. What is it? This is a new capability within Plotly Studio that introduces Scheduled Data Refreshes and Caching Rules for applications. It provides a robust, back-end mechanism to define how and when the underlying data extracts for an app are updated. What Does it Allow Users to Do? This feature allows users to: Automate Data Updates: Users can define a specific, recurring schedule for their application's data extracts to be pulled and refreshed automatically. This ensures their apps consistently display the most up-to-date information without manual effort. Customize Refresh Frequency: Users can set the refresh interval using through the Data Sources chat with natural language. This will be interpreted and applied as a crontab expression used in the @schedule decorator in the data source code. Set Caching Rules: Users can choose from distinct data refresh/caching behaviors to optimize performance and data freshness: Never: The data remains static. On-demand: The data refreshes every time the application is loaded. Scheduled: The data refreshes based on the user-defined frequency.
Matthew Brown 6 months ago
Roadmap Candidate
Plotly Studio
In Progress
Scheduled Data Refresh & Caching
User Problem Users building data-driven applications in Plotly Studio currently have no way to trigger data updates, relying on hacky workarounds and re-publishing apps manually. This can lead to apps displaying stale data, requiring constant manual intervention, and hindering the ability to present timely and accurate insights to viewers. What is it? This is a new capability within Plotly Studio that introduces Scheduled Data Refreshes and Caching Rules for applications. It provides a robust, back-end mechanism to define how and when the underlying data extracts for an app are updated. What Does it Allow Users to Do? This feature allows users to: Automate Data Updates: Users can define a specific, recurring schedule for their application's data extracts to be pulled and refreshed automatically. This ensures their apps consistently display the most up-to-date information without manual effort. Customize Refresh Frequency: Users can set the refresh interval using through the Data Sources chat with natural language. This will be interpreted and applied as a crontab expression used in the @schedule decorator in the data source code. Set Caching Rules: Users can choose from distinct data refresh/caching behaviors to optimize performance and data freshness: Never: The data remains static. On-demand: The data refreshes every time the application is loaded. Scheduled: The data refreshes based on the user-defined frequency.
Matthew Brown 6 months ago
Roadmap Candidate
Plotly Studio
Under Consideration
Save and Import Prompts
User problem Users currently lack a streamlined way to save and reuse specific prompts or configurations within Plotly Studio, making it challenging to replicate or build upon previously successful generations of charts, components, or entire applications. This can lead to inefficiencies and inconsistencies when trying to achieve specific design or functional outcomes. What is it? This feature introduces prompt saving and importing capabilities within Plotly Studio, applicable to individual charts, components, and the overall application. It's essentially a way for users to create a library of successful configurations and creative starting points. What does it allow users to do? This feature allows users to: Save outlines of applications they've generated, ensuring safe keeping and easy retrieval for later use or modification. Generate new applications, charts, or components using the exact, pre-defined prompt templates they've saved previously. Tweak and modify saved prompt templates as needed to adapt to new requirements, experiment with variations, or iterate quickly on a base design.
Matthew Brown 6 months ago
Roadmap Candidate
Plotly Studio
Under Consideration
Save and Import Prompts
User problem Users currently lack a streamlined way to save and reuse specific prompts or configurations within Plotly Studio, making it challenging to replicate or build upon previously successful generations of charts, components, or entire applications. This can lead to inefficiencies and inconsistencies when trying to achieve specific design or functional outcomes. What is it? This feature introduces prompt saving and importing capabilities within Plotly Studio, applicable to individual charts, components, and the overall application. It's essentially a way for users to create a library of successful configurations and creative starting points. What does it allow users to do? This feature allows users to: Save outlines of applications they've generated, ensuring safe keeping and easy retrieval for later use or modification. Generate new applications, charts, or components using the exact, pre-defined prompt templates they've saved previously. Tweak and modify saved prompt templates as needed to adapt to new requirements, experiment with variations, or iterate quickly on a base design.
Matthew Brown 6 months ago
Roadmap Candidate
Plotly Studio
Under Consideration
Replicate Project
User problem Users need a way to safeguard their work in Plotly Studio, particularly after successfully generating an app. Without a "save point" or replication feature, making subsequent edits carries the risk of irrevocably altering or even losing the functional state of their application. This lack of a robust versioning mechanism can lead to significant frustration and inefficiency, as users cannot freely experiment with changes or revert to a previously working version. --- ### What is it? This feature introduces the ability to create copies of an existing Plotly Studio project. This "project replication" essentially generates a duplicate of the entire project, including all associated files, configurations, and generated application code, at a specific point in time. --- ### What does it allow users to do? This feature allows users to: - Create "save points": Users can explicitly duplicate a project after achieving a stable or desirable state, effectively creating a snapshot of their work. - Experiment safely: Users can then make potentially destructive edits or explore new design variations on the duplicated project without impacting the original "good" version. - Revert to previous states: In case of errors, undesirable changes, or a need to compare different iterations, users can easily revert to any of their previously replicated project versions. - Streamline development workflow: By enabling non-destructive editing and version control, users can iterate on their applications more efficiently and with greater confidence.
Matthew Brown 6 months ago
Roadmap Candidate
Plotly Studio
Under Consideration
Replicate Project
User problem Users need a way to safeguard their work in Plotly Studio, particularly after successfully generating an app. Without a "save point" or replication feature, making subsequent edits carries the risk of irrevocably altering or even losing the functional state of their application. This lack of a robust versioning mechanism can lead to significant frustration and inefficiency, as users cannot freely experiment with changes or revert to a previously working version. --- ### What is it? This feature introduces the ability to create copies of an existing Plotly Studio project. This "project replication" essentially generates a duplicate of the entire project, including all associated files, configurations, and generated application code, at a specific point in time. --- ### What does it allow users to do? This feature allows users to: - Create "save points": Users can explicitly duplicate a project after achieving a stable or desirable state, effectively creating a snapshot of their work. - Experiment safely: Users can then make potentially destructive edits or explore new design variations on the duplicated project without impacting the original "good" version. - Revert to previous states: In case of errors, undesirable changes, or a need to compare different iterations, users can easily revert to any of their previously replicated project versions. - Streamline development workflow: By enabling non-destructive editing and version control, users can iterate on their applications more efficiently and with greater confidence.
Matthew Brown 6 months ago
Roadmap Candidate
Plotly Studio
Publish charts from notebooks
Add a quick action in the modebar for all Plotly charts to publish those charts directly to Cloud. Charts would be wrapped in the requisite Dash code automatically and published.
Nathan Drezner 4 months ago
Roadmap Candidate
Plotly Cloud
Publish charts from notebooks
Add a quick action in the modebar for all Plotly charts to publish those charts directly to Cloud. Charts would be wrapped in the requisite Dash code automatically and published.
Nathan Drezner 4 months ago
Roadmap Candidate
Plotly Cloud
In Progress
Theme Designer UI
User problem The current LLM-based theme generation in Plotly Studio is excellent for creating broad, high-level changes. However, it lacks the precision users need for making specific, granular adjustments. Users require a more direct and precise way to modify individual elements of their themes, such as colors, fonts, and spacing, without using generative AI. They need a tool that allows for surgical edits while providing a live preview of how these changes affect all visual components of their application. What is it? The Plotly Studio Theme Designer is a new user interface (UI) designed specifically for creating and editing application themes. It provides a visual, point-and-click environment for theme customization, offering direct control over a theme's properties. Unlike the generative AI workflow, this tool provides a structured approach to theme management, allowing for precise modifications to any design element. What does it allow users to do? ✨ The Theme Designer allows users to: Make surgical edits to a theme's colors, fonts, and spacing. Use simple form controls (like color pickers, dropdowns, and sliders) to adjust theme properties easily. See a live preview of how their theme changes will be applied to all UI elements, including charts, buttons, and text, ensuring a cohesive design before saving.
Matthew Brown 5 months ago
Roadmap Candidate
Plotly Studio
In Progress
Theme Designer UI
User problem The current LLM-based theme generation in Plotly Studio is excellent for creating broad, high-level changes. However, it lacks the precision users need for making specific, granular adjustments. Users require a more direct and precise way to modify individual elements of their themes, such as colors, fonts, and spacing, without using generative AI. They need a tool that allows for surgical edits while providing a live preview of how these changes affect all visual components of their application. What is it? The Plotly Studio Theme Designer is a new user interface (UI) designed specifically for creating and editing application themes. It provides a visual, point-and-click environment for theme customization, offering direct control over a theme's properties. Unlike the generative AI workflow, this tool provides a structured approach to theme management, allowing for precise modifications to any design element. What does it allow users to do? ✨ The Theme Designer allows users to: Make surgical edits to a theme's colors, fonts, and spacing. Use simple form controls (like color pickers, dropdowns, and sliders) to adjust theme properties easily. See a live preview of how their theme changes will be applied to all UI elements, including charts, buttons, and text, ensuring a cohesive design before saving.
Matthew Brown 5 months ago
Roadmap Candidate
Plotly Studio
Planned
AI Context: Python Library
User problem Users struggle to incorporate proprietary or specialized Python libraries into their data applications built with Plotly Studio, limiting the functionality and customization of their generated apps. This often leads to manual workarounds or an inability to fully leverage their existing codebases. What is it? This feature introduces a mechanism for Plotly Studio to integrate with and understand external Python libraries, including private and open-source packages. This integration allows Plotly Studio to access and interpret the Application Programming Interface (API) of these specified libraries. What does it allow users to do? This feature allows users to: Reference any Python library: Users can specify a particular Python library, whether it's a private internal tool or a publicly available open-source package. Leverage custom functions: Once the library is referenced, Plotly Studio can crawl its API, enabling users to incorporate custom calculations, data transformations, and other functions from that library directly into their data applications during the generation process. Enhance application capabilities: Users can build more sophisticated and tailored data applications by utilizing the specific functionalities provided by their chosen external libraries, reducing the need for manual coding or workarounds.
Matthew Brown 6 months ago
Roadmap Candidate
Plotly Studio
Planned
AI Context: Python Library
User problem Users struggle to incorporate proprietary or specialized Python libraries into their data applications built with Plotly Studio, limiting the functionality and customization of their generated apps. This often leads to manual workarounds or an inability to fully leverage their existing codebases. What is it? This feature introduces a mechanism for Plotly Studio to integrate with and understand external Python libraries, including private and open-source packages. This integration allows Plotly Studio to access and interpret the Application Programming Interface (API) of these specified libraries. What does it allow users to do? This feature allows users to: Reference any Python library: Users can specify a particular Python library, whether it's a private internal tool or a publicly available open-source package. Leverage custom functions: Once the library is referenced, Plotly Studio can crawl its API, enabling users to incorporate custom calculations, data transformations, and other functions from that library directly into their data applications during the generation process. Enhance application capabilities: Users can build more sophisticated and tailored data applications by utilizing the specific functionalities provided by their chosen external libraries, reducing the need for manual coding or workarounds.
Matthew Brown 6 months ago
Roadmap Candidate
Plotly Studio
Under Consideration
API key support
Allow users to generate API keys for Plotly Cloud access, making it easier to publish to Cloud from a CI pipeline or job.
Nathan Drezner About 1 month ago
Roadmap Candidate
Plotly Cloud
Under Consideration
API key support
Allow users to generate API keys for Plotly Cloud access, making it easier to publish to Cloud from a CI pipeline or job.
Nathan Drezner About 1 month ago
Roadmap Candidate
Plotly Cloud
In Progress
Drag and Drop Layouts
User Problem Users find the current method for rearranging and resizing components in their Plotly Studio applications to be cumbersome and confusing. They must navigate to the Layout tab and manually edit a descriptive prompt that lists components and their sizes (e.g., "Sales Revenue (50%)"). This indirect, text-based approach is often a source of frustration and errors, detracting from the intuitive experience users expect when designing their app's layout. What Is It? This feature is an interactive, drag-and-drop layout editor integrated directly into the Plotly Studio application interface. It replaces the current text-prompt-based layout editing process. The editor provides a visual canvas that represents the app's current layout, allowing users to manipulate components directly, similar to a modern design tool. What Does It Allow Users to Do? This new editor allows users to: Move Components: Users can click, hold, and drag any component—such as a graph, table, or control—to a new position within the application layout, instantly repositioning it. Resize Components: Users can click and drag the edges or corners of a component to easily adjust its width (and row height?) visually, seeing the changes immediately reflected in the layout. Intuitively Design Layouts: Users can manage the entire application design process—including component placement, spacing, and sizing—with a direct manipulation interface, eliminating the need to interpret or edit a code-like descriptive prompt. Simplify Workflow: Users can eliminate context-switching by making layout changes directly within the visual interface, speeding up the design and iteration process.
Matthew Brown 11 months ago
Roadmap Candidate
Plotly Studio
In Progress
Drag and Drop Layouts
User Problem Users find the current method for rearranging and resizing components in their Plotly Studio applications to be cumbersome and confusing. They must navigate to the Layout tab and manually edit a descriptive prompt that lists components and their sizes (e.g., "Sales Revenue (50%)"). This indirect, text-based approach is often a source of frustration and errors, detracting from the intuitive experience users expect when designing their app's layout. What Is It? This feature is an interactive, drag-and-drop layout editor integrated directly into the Plotly Studio application interface. It replaces the current text-prompt-based layout editing process. The editor provides a visual canvas that represents the app's current layout, allowing users to manipulate components directly, similar to a modern design tool. What Does It Allow Users to Do? This new editor allows users to: Move Components: Users can click, hold, and drag any component—such as a graph, table, or control—to a new position within the application layout, instantly repositioning it. Resize Components: Users can click and drag the edges or corners of a component to easily adjust its width (and row height?) visually, seeing the changes immediately reflected in the layout. Intuitively Design Layouts: Users can manage the entire application design process—including component placement, spacing, and sizing—with a direct manipulation interface, eliminating the need to interpret or edit a code-like descriptive prompt. Simplify Workflow: Users can eliminate context-switching by making layout changes directly within the visual interface, speeding up the design and iteration process.
Matthew Brown 11 months ago
Roadmap Candidate
Plotly Studio
Under Consideration
Expanded File Type Support
User Problem Plotly Studio currently restricts direct file uploads to CSV and Parquet formats. Users who work with other common data file types, such as Excel and GeoJSON, face friction because they cannot upload these files directly. This requires them to manually convert or process these files before analysis in Plotly Studio, slowing down their workflow. What Is It? This feature expands the direct file upload capability in Plotly Studio to include a wider range of file formats beyond the existing CSV and Parquet support. The goal is to allow users to upload virtually any data file type that the underlying data parsing tools (like those available in Python) can reliably read and convert into a standard dataframe for visualization and analysis. What Does It Allow Users to Do? This expanded support for file uploads allows users to: Upload Diverse Data: Users will be able to directly upload files in popular formats like Excel (.xlsx, .xls), GeoJSON, and potentially many others, straight into Plotly Studio for immediate use. Streamline Data Import: By eliminating the need for external file conversion or pre-processing steps, users can significantly accelerate their data-to-visualization workflow. They can skip manual steps and immediately begin their analysis. Maintain Focus: Users can keep their entire data analysis and visualization process within Plotly Studio, resulting in a more integrated and efficient experience. Handle Complex Data: The inclusion of formats like GeoJSON specifically caters to users working with geographic and spatial data, enabling them to easily upload and visualize map-based information.
Matthew Brown 28 days ago
Roadmap Candidate
Plotly Studio
Under Consideration
Expanded File Type Support
User Problem Plotly Studio currently restricts direct file uploads to CSV and Parquet formats. Users who work with other common data file types, such as Excel and GeoJSON, face friction because they cannot upload these files directly. This requires them to manually convert or process these files before analysis in Plotly Studio, slowing down their workflow. What Is It? This feature expands the direct file upload capability in Plotly Studio to include a wider range of file formats beyond the existing CSV and Parquet support. The goal is to allow users to upload virtually any data file type that the underlying data parsing tools (like those available in Python) can reliably read and convert into a standard dataframe for visualization and analysis. What Does It Allow Users to Do? This expanded support for file uploads allows users to: Upload Diverse Data: Users will be able to directly upload files in popular formats like Excel (.xlsx, .xls), GeoJSON, and potentially many others, straight into Plotly Studio for immediate use. Streamline Data Import: By eliminating the need for external file conversion or pre-processing steps, users can significantly accelerate their data-to-visualization workflow. They can skip manual steps and immediately begin their analysis. Maintain Focus: Users can keep their entire data analysis and visualization process within Plotly Studio, resulting in a more integrated and efficient experience. Handle Complex Data: The inclusion of formats like GeoJSON specifically caters to users working with geographic and spatial data, enabling them to easily upload and visualize map-based information.
Matthew Brown 28 days ago
Roadmap Candidate
Plotly Studio
Planned
Enhanced Performance with Large Datasets
User Problem Users of Plotly Studio are currently limited in the size and performance of the datasets they can analyze within their applications. The current 200MB cap and reliance on Python's Pandas library for all data querying and manipulation lead to a sluggish experience. For instance, applying a new filter to the data causes the entire application to freeze or show loading bars for a significant duration, similar to the initial load time. Users are requesting the capability to work with datasets that are an order of magnitude larger while maintaining a responsive and fast application experience. What is it? This feature involves a foundational re-architecture of the data handling and querying engine within Plotly Studio applications. This shift would replace the current Pandas-based, in-memory processing with a high-performance database technology like DuckDB or a similar optimized solution. The goal is to achieve significantly higher data throughput and faster query execution, ideally gaining performance as a side effect of switching to the new technology stack. This migration is the first step toward eventually backing applications with a native SQL layer rather than Python, which will enable future features to push query execution directly down to the original connected database. What does it allow users to do? This performance upgrade will allow users to: Work with much larger datasets: Users will be able to load and analyze datasets that are significantly larger than the current 200MB limit, opening up the analysis of high-volume business data. Experience faster, more responsive interactions: Core operations like applying global filters, aggregating data, or performing calculations will be nearly instantaneous, eliminating the frustrating loading times and application freezes that currently occur with moderately sized data. Build more complex, performant applications: Users can design more sophisticated, data-intensive applications without compromising on speed or user experience, leading to richer insights and more powerful data dashboards. Scale their data analysis: The ability to handle larger data volumes efficiently provides a clearer path for users to scale their analytical workflows as their data needs grow.
Matthew Brown 28 days ago
Roadmap Candidate
Plotly Studio
Planned
Enhanced Performance with Large Datasets
User Problem Users of Plotly Studio are currently limited in the size and performance of the datasets they can analyze within their applications. The current 200MB cap and reliance on Python's Pandas library for all data querying and manipulation lead to a sluggish experience. For instance, applying a new filter to the data causes the entire application to freeze or show loading bars for a significant duration, similar to the initial load time. Users are requesting the capability to work with datasets that are an order of magnitude larger while maintaining a responsive and fast application experience. What is it? This feature involves a foundational re-architecture of the data handling and querying engine within Plotly Studio applications. This shift would replace the current Pandas-based, in-memory processing with a high-performance database technology like DuckDB or a similar optimized solution. The goal is to achieve significantly higher data throughput and faster query execution, ideally gaining performance as a side effect of switching to the new technology stack. This migration is the first step toward eventually backing applications with a native SQL layer rather than Python, which will enable future features to push query execution directly down to the original connected database. What does it allow users to do? This performance upgrade will allow users to: Work with much larger datasets: Users will be able to load and analyze datasets that are significantly larger than the current 200MB limit, opening up the analysis of high-volume business data. Experience faster, more responsive interactions: Core operations like applying global filters, aggregating data, or performing calculations will be nearly instantaneous, eliminating the frustrating loading times and application freezes that currently occur with moderately sized data. Build more complex, performant applications: Users can design more sophisticated, data-intensive applications without compromising on speed or user experience, leading to richer insights and more powerful data dashboards. Scale their data analysis: The ability to handle larger data volumes efficiently provides a clearer path for users to scale their analytical workflows as their data needs grow.
Matthew Brown 28 days ago
Roadmap Candidate
Plotly Studio
Scheduled Tasks in Plotly Cloud
In Cloud, support running tasks outside of apps on a schedule. Cloud would add a management interface for any number of scripts, and a scheduling system to execute those scripts at regular intervals. The scripts would be able to output data to a filesystem accessible to any other apps running under the same user account in Cloud, allowing separation of data retrieval from the apps themselves.
Nathan Drezner About 1 month ago
Roadmap Candidate
Plotly Cloud
Scheduled Tasks in Plotly Cloud
In Cloud, support running tasks outside of apps on a schedule. Cloud would add a management interface for any number of scripts, and a scheduling system to execute those scripts at regular intervals. The scripts would be able to output data to a filesystem accessible to any other apps running under the same user account in Cloud, allowing separation of data retrieval from the apps themselves.
Nathan Drezner About 1 month ago
Roadmap Candidate
Plotly Cloud
Under Consideration
Chat-driven Generation
User Problem Users currently face a steep learning curve and lack of flexibility when creating applications and components like charts and tables using the platform's existing specification-based approach. Difficulty with Format: It's often unclear what format or structure the prompt should follow, leading to overwhelming complexity and trial-and-error. Slow Iteration: The process is slow, taking around 30 seconds for each chart to generate. This forces users to craft comprehensive, detailed initial instructions instead of allowing for quick, fluid, and iterative adjustments. This leads to a less intuitive experience where users spend too much time defining requirements upfront instead of getting instant visual feedback. What is it? The new Chat-driven Generation feature provides an intuitive, conversational interface for building data applications and components. It uses a Large Language Model (LLM) within a chat environment to interpret user instructions, dynamically generating the component, and creating a specification to accompany it. It shifts the creation process from a rigid, spec-based method to a guided, conversational workflow, without losing the benefits of a spec for judging a component’s accuracy and sharing with colleagues. What does it allow users to do? Chat-driven Generation empowers users with a more natural and efficient way to build. It allows users to: Iterate Fluidly: Users can issue individual, rapid instructions to quickly adjust or refine a chart, guiding the LLM toward the final result in a dynamic, step-by-step conversation rather than waiting for long initial builds. Attach Context Easily: The chat environment naturally retains the conversation history, allowing users to effortlessly attach context from previous instructions to their current request. Inspect and Save Specifications: While the user interacts with the chat, the system automatically generates an underlying specification (the code for the component). Users can inspect this specification for accuracy, save it permanently, and share it with colleagues to ensure reproducibility and collaboration. Use Generated Specs as Context: Saved specifications can be used as the starting point or context for future chart creation, streamlining repetitive tasks or building variations on existing designs.
Matthew Brown 2 months ago
Roadmap Candidate
Plotly Studio
Under Consideration
Chat-driven Generation
User Problem Users currently face a steep learning curve and lack of flexibility when creating applications and components like charts and tables using the platform's existing specification-based approach. Difficulty with Format: It's often unclear what format or structure the prompt should follow, leading to overwhelming complexity and trial-and-error. Slow Iteration: The process is slow, taking around 30 seconds for each chart to generate. This forces users to craft comprehensive, detailed initial instructions instead of allowing for quick, fluid, and iterative adjustments. This leads to a less intuitive experience where users spend too much time defining requirements upfront instead of getting instant visual feedback. What is it? The new Chat-driven Generation feature provides an intuitive, conversational interface for building data applications and components. It uses a Large Language Model (LLM) within a chat environment to interpret user instructions, dynamically generating the component, and creating a specification to accompany it. It shifts the creation process from a rigid, spec-based method to a guided, conversational workflow, without losing the benefits of a spec for judging a component’s accuracy and sharing with colleagues. What does it allow users to do? Chat-driven Generation empowers users with a more natural and efficient way to build. It allows users to: Iterate Fluidly: Users can issue individual, rapid instructions to quickly adjust or refine a chart, guiding the LLM toward the final result in a dynamic, step-by-step conversation rather than waiting for long initial builds. Attach Context Easily: The chat environment naturally retains the conversation history, allowing users to effortlessly attach context from previous instructions to their current request. Inspect and Save Specifications: While the user interacts with the chat, the system automatically generates an underlying specification (the code for the component). Users can inspect this specification for accuracy, save it permanently, and share it with colleagues to ensure reproducibility and collaboration. Use Generated Specs as Context: Saved specifications can be used as the starting point or context for future chart creation, streamlining repetitive tasks or building variations on existing designs.
Matthew Brown 2 months ago
Roadmap Candidate
Plotly Studio
Planned
Web Content as Context
User Problem Users often need to generate applications or components (charts, tables, etc.) based on external reference information found on the web, such as up-to-date documentation or specific articles. The LLM, trained on historical data, cannot natively access the current, full content of a specific URL or the latest information found through a web search. This leads to outputs that can be outdated, inaccurate, or fail to follow the instructions contained on a specific web page. What is it? Web Context enables the Plotly Studio LLM to fetch and integrate real-time or specific web content into its context. This is achieved by allowing users to provide one or more URLs (links to websites, documentation pages, etc.). The system will retrieve the text content from the specified source(s) and use it to "ground" the LLM's response. What does it allow users to do? This feature allows users to ensure the LLM generates artifacts based on the most relevant and current information available on the public web. Specifically, users can: Specify a Content Source: Provide specific URLs (e.g., a link to a REST API documentation page or a public data report) to ensure the LLM's generated code, charts, or tables are based on that precise source content. Generate Accurate Code and Designs: Direct the LLM to follow live design standards or technical specifications hosted on a website, reducing the need for manual corrections and speeding up the development of accurate, well-contextualized applications. Maintain Context Scope: Attach URL context to an entire project or to an individual component being generated, allowing for global or surgical context infusion into the LLM's task.
Matthew Brown 2 months ago
Roadmap Candidate
Plotly Studio
Planned
Web Content as Context
User Problem Users often need to generate applications or components (charts, tables, etc.) based on external reference information found on the web, such as up-to-date documentation or specific articles. The LLM, trained on historical data, cannot natively access the current, full content of a specific URL or the latest information found through a web search. This leads to outputs that can be outdated, inaccurate, or fail to follow the instructions contained on a specific web page. What is it? Web Context enables the Plotly Studio LLM to fetch and integrate real-time or specific web content into its context. This is achieved by allowing users to provide one or more URLs (links to websites, documentation pages, etc.). The system will retrieve the text content from the specified source(s) and use it to "ground" the LLM's response. What does it allow users to do? This feature allows users to ensure the LLM generates artifacts based on the most relevant and current information available on the public web. Specifically, users can: Specify a Content Source: Provide specific URLs (e.g., a link to a REST API documentation page or a public data report) to ensure the LLM's generated code, charts, or tables are based on that precise source content. Generate Accurate Code and Designs: Direct the LLM to follow live design standards or technical specifications hosted on a website, reducing the need for manual corrections and speeding up the development of accurate, well-contextualized applications. Maintain Context Scope: Attach URL context to an entire project or to an individual component being generated, allowing for global or surgical context infusion into the LLM's task.
Matthew Brown 2 months ago
Roadmap Candidate
Plotly Studio
