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<h2>User Problem</h2><p></p><p>Users who interact with dashboards generated by Plotly Studio lack a direct, simple way to ask follow-up questions or gain deeper insights from the underlying data that may not be easily attained by viewing visualizations alone. They may need to know specific facts, understand trends, or get immediate answers related to the charts they are viewing without having to leave the application or consult a data specialist.</p><div data-type="horizontalRule"><hr></div><h2>What Is It?</h2><p></p><p>Data Chat for App Viewers is an intelligent, integrated capability designed to empower end-users with natural language querying against the data powering a Plotly Studio app. It acts as a data assistant, leveraging the structure of the data and the visualizations to provide context-aware, analytical responses.</p><div data-type="horizontalRule"><hr></div><h2>What Does It Allow Users to Do?</h2><p></p><p>Data Chat for App Viewers allows users to interact directly with the data powering their dashboards and charts through simple text input, enabling them to:</p><ul><li><p><strong>Ask pointed questions</strong> about specific data points, segments, or filters present in the app.</p></li><li><p><strong>Request trend analysis</strong> for the charts they are reviewing, such as "What was the growth rate in Q3?" or "Identify any anomalies in sales data over the last six months."</p></li><li><p><strong>Generate immediate answers</strong> to analytical questions they have while reviewing a dashboard, providing instant clarity and reducing the need to manually manipulate data or build new visualizations.</p></li><li><p><strong>Obtain context-specific information</strong> related to a chart or dashboard, enhancing their data understanding and decision-making process.</p></li></ul><p></p>

Planned

Roadmap Candidate

Data Chat for App Viewers

Matthew Brown

<h2>User Problem</h2><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p>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.</p><p></p><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p>This performance upgrade will allow users to:</p><ul><li><p><strong>Work with much larger datasets</strong>: 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.</p></li><li><p><strong>Experience faster, more responsive interactions</strong>: 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.</p></li><li><p><strong>Build more complex, performant applications</strong>: Users can design more sophisticated, data-intensive applications without compromising on speed or user experience, leading to richer insights and more powerful data dashboards.</p></li><li><p><strong>Scale their data analysis</strong>: The ability to handle larger data volumes efficiently provides a clearer path for users to scale their analytical workflows as their data needs grow.</p></li></ul><p></p>

Enhanced Performance with Large Datasets

<h2>User Problem</h2><p></p><p>Currently, Plotly Studio apps rely on in-memory processing (Pandas) for queries, which can lead to slow performance and scalability issues, especially when handling large datasets or complex analytical operations. Furthermore, the existing architecture requires custom or limited methods for connecting to diverse data sources, complicating the process for users who need to integrate their applications with existing database infrastructure.</p><div data-type="horizontalRule"><hr></div><h2>What Is It?</h2><p></p><p>This feature is a foundational architectural enhancement that transitions Plotly Studio apps to be SQL-backed, initially leveraging a high-performance, in-process SQL database technology like DuckDB. This change means that data within the app is treated as an SQL database, and all subsequent in-app queries, filtering, and aggregation operations are executed using SQL.</p><div data-type="horizontalRule"><hr></div><h2>What Does It Allow Users to Do?</h2><p></p><p>This enhancement offers several significant benefits:</p><ul><li><p><strong>Significantly Increased Query Performance</strong>: By utilizing a robust SQL engine, the feature dramatically accelerates the speed at which in-app queries, filters, and aggregations are executed, leading to a much smoother and more responsive user experience, particularly with larger datasets. It also allows the resulting application to go beyond the current 10k row limitation in the table visualization.</p></li><li><p><strong>Robust Built-in Connection Options</strong>: The underlying SQL engine immediately provides users with standardized and reliable built-in connection methods to load data from various file types (like CSV, Parquet, and JSON) and other common data sources.</p></li><li><p><strong>Future-Proofing for External Database Connectivity</strong>: This foundational transition sets the stage for a longer-term capability where Plotly Studio apps could be enabled to directly push-down queries to separate, external SQL databases (e.g., PostgreSQL, Snowflake, BigQuery). This would allow users to leverage the processing power of their existing external data warehouses without needing to ingest the entire dataset into the app first.</p></li></ul><p></p>

High Priority

SQL-backed Apps

<h2>User Problem</h2><p>Manually establishing data source connections can be a repetitive and time-consuming process. Currently, users may find themselves spending several minutes configuring the same connection parameters every time they start a new project. This redundancy creates a barrier to entry for new analysis and increases the likelihood of configuration errors. Additionally, within team environments, individual team members often have to reinvent the wheel by setting up their own connections to the same "trusted" data sources, leading to fragmented workflows and inconsistent data access.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p>Reusable and Shareable Data Sources is a management feature integrated with the platform's data chat agent. It allows users to leverage an interactive, guided setup process to establish a connection once and store it as a persistent asset. This feature is intended for integration into Plotly Cloud Teams, enabling a centralized repository of verified connections that can be managed and shared across an entire data organization.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p>This feature streamlines the transition from setup to insight by providing the following capabilities:</p><ul><li><p><strong>Guided Configuration:</strong> Users can build complex data connections interactively with the assistance of a chat agent, reducing the technical burden of initial setup.</p></li><li><p><strong>One-Click Reuse:</strong> Once a connection is established, users can instantly apply it to subsequent projects without re-entering credentials or parameters.</p></li><li><p><strong>Team Collaboration:</strong> Team members can share access to these data sources, ensuring everyone on a project is working from the same "source of truth."</p></li><li><p><strong>Faster Project Onboarding:</strong> By eliminating the multi-minute setup phase for known databases, users can bypass administrative hurdles and begin data visualization or analysis immediately.</p></li></ul><p></p>

Reusable and Shareable Data Sources

<h2>User problem</h2><p>Data analysis often requires a tedious, manual sequence of cleaning, filtering, and visualizing data to uncover meaningful insights. When faced with complex or broad questions, users frequently struggle with the "blank canvas" problem—not knowing exactly which analytical steps to take or which chart types will best represent the answer. This process is time-consuming and often requires significant technical expertise to ensure the resulting visualizations accurately reflect the underlying data.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p>Deep Research Mode is an agentic AI analysis environment within Plotly Studio designed for exhaustive data exploration. Unlike standard automated charting, this mode employs a multi-step reasoning agent that breaks down a user’s high-level inquiry into a logical sequence of analytical tasks. It operates as an interactive workspace where the AI performs the "heavy lifting" of data processing and visualization while maintaining full transparency for the user.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p>Deep Research Mode provides a comprehensive set of capabilities for transforming raw data views into actionable narratives:</p><ul><li><p><strong>Execute Complex Inquiries:</strong> Users can input pointed or broad questions, prompting the AI to architect a custom multi-step analysis plan.</p></li><li><p><strong>Generate Integrated Assets:</strong> The tool automatically produces supporting tables and visualizations, culminating in a written summary that directly addresses the initial request.</p></li><li><p><strong>Maintain Creative Control:</strong> Users can pause the multi-step process at any point to review progress, branch off into new analytical directions, or pivot the scope of the research.</p></li><li><p><strong>Verify with Chat:</strong> To ensure accuracy, users can engage in a side-chat with the project to interrogate specific analysis steps and verify the logic behind the AI's output.</p></li><li><p><strong>Transition to Production:</strong> If a generated visualization proves valuable for long-term use, users can save it to their library or embed it directly into an existing application.</p></li></ul><p></p>

Deep Research Mode

<h2>User problem</h2><p></p><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p></p><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p></p><p>This feature allows users to:</p><ul><li><p><strong>Save outlines of applications</strong> they've generated, ensuring safe keeping and easy retrieval for later use or modification.</p></li><li><p><strong>Generate new applications, charts, or components</strong> using the exact, pre-defined prompt templates they've saved previously.</p></li><li><p><strong>Tweak and modify</strong> saved prompt templates as needed to adapt to new requirements, experiment with variations, or iterate quickly on a base design.</p></li></ul><p></p>

Save and Import Prompts

<h2>User problem</h2><p></p><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p></p><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do? ✨</h2><p></p><p>The Theme Designer allows users to:</p><ul><li><p>Make surgical edits to a theme's colors, fonts, and spacing.</p></li><li><p>Use simple form controls (like color pickers, dropdowns, and sliders) to adjust theme properties easily.</p></li><li><p>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.</p></li></ul><p></p>

Theme Designer UI

<h2>User problem</h2><p>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.</p><p></p><h2>What is it?</h2><p>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.</p><p></p><h2>What does it allow users to do? </h2><p>This feature allows users to: </p><ul><li><p><strong>Reference any Python library:</strong> Users can specify a particular Python library, whether it's a private internal tool or a publicly available open-source package. </p></li><li><p><strong>Leverage custom functions:</strong> 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. </p></li><li><p><strong>Enhance application capabilities:</strong> 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.</p></li></ul><p></p><p></p>

AI Context: Python Library

<p></p><h2>User Problem</h2><p></p><p>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.</p><ul><li><p><strong>Difficulty with Format:</strong> It's often unclear what format or structure the prompt should follow, leading to overwhelming complexity and trial-and-error.</p></li><li><p><strong>Slow Iteration:</strong> 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.</p></li></ul><p>This leads to a less intuitive experience where users spend too much time defining requirements upfront instead of getting instant visual feedback.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p></p><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p></p><p>Chat-driven Generation empowers users with a more natural and efficient way to build. It allows users to:</p><ul><li><p><strong>Iterate Fluidly:</strong> 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.</p></li><li><p><strong>Attach Context Easily:</strong> The chat environment naturally retains the conversation history, allowing users to effortlessly attach context from previous instructions to their current request.</p></li><li><p><strong>Inspect and Save Specifications:</strong> 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.</p></li><li><p><strong>Use Generated Specs as Context:</strong> Saved specifications can be used as the starting point or context for future chart creation, streamlining repetitive tasks or building variations on existing designs.</p></li></ul><p></p>

Chat-driven Generation

<h2>User Problem</h2><p></p><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p></p><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p></p><p>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:</p><ul><li><p><strong>Specify a Content Source:</strong> 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.</p></li><li><p><strong>Generate Accurate Code and Designs:</strong> 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.</p></li><li><p><strong>Maintain Context Scope:</strong> 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.</p></li></ul><p></p>

Web Content as Context

<h2>User Problem</h2><p>Many users find that current Plotly Studio applications lack the aesthetic and functional characteristics of traditional Business Intelligence (BI) dashboards. The existing layouts often feel less polished than industry-standard solutions, making it difficult for stakeholders to navigate complex data sets or view reports on varying device sizes. Additionally, the organization of control elements can feel cluttered, detracting from the professional quality required for executive-level presentations.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p>The App Style Overhaul is a comprehensive design framework intended to modernize the visual architecture and user interface of Plotly Studio applications. This update focuses on structural refinement, introducing a standardized navigation system, and refreshing the visual language of individual components. The overhaul prioritizes a clean aesthetic that mirrors high-end BI tools while ensuring that the underlying data remains the focal point of the application.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p>This design update provides several functional improvements to the app-building and viewing experience:</p><ul><li><p><strong>Streamline Navigation:</strong> Users can navigate between different charts and pages using a dedicated left-hand sidebar, enabling single-click transitions throughout the application.</p></li><li><p><strong>Optimize Data Real Estate:</strong> A new "single component view" allows users to toggle from a broad dashboard layout to a maximized view of a specific chart, ensuring critical data can be analyzed without visual distractions.</p></li><li><p><strong>Improve Interface Organization:</strong> Strategic relocation of form controls and input elements creates a cleaner workspace, reducing cognitive load for the end viewer.</p></li><li><p><strong>Maintain Professionalism Across Devices:</strong> With responsive component design, users can ensure their dashboards maintain high-quality polish and readability whether viewed on a desktop monitor or a mobile device.</p></li><li><p><strong>Elevate Visual Polish:</strong> The use of leaner, modernized component styling allows users to create applications that meet the professional visual standards expected in enterprise environments.</p></li></ul><p></p>

App Style Overhaul

<h2>User problem</h2><p></p><p>Users frequently need to debug issues with their charts, understand how their data is being processed, and confirm the accuracy of the results. Current tools for this are limited to traditional code debugging methods, like adding print statements, which are often not intuitive or accessible for users with data literacy but not deep coding expertise. This lack of transparency can lead to anxiety that the data analysis is a "black box" done entirely by AI, rather than by the code the AI generates. Users need a clear, approachable, and detailed way to see the step-by-step logic of the data transformation.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p></p><p>The Logs tab is a new, fourth interface pane in the chart editor, appearing alongside Preview, Spec, and Code. It serves as a visual, step-by-step record of data processing and application events that occur when a chart or component runs. The design prioritizes being approachable and scannable over looking like traditional "Matrix" code logs, making it suitable for users who are not professional developers. It features a timeline rail on the left to show distinct actions over time and uses rich rendering (like compact tables and bolded headers) to display information clearly.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p></p><p>The Logs tab provides transparency and detailed insight into the application's execution, specifically enabling users to:</p><ul><li><p><strong>Spot Check Data and Debug Issues:</strong> Users can see the sequence of data transformations from the source to the final chart, including previews of the dataset at various steps. This allows them to quickly spot check intermediate results and pinpoint where an error or unexpected result might originate.</p></li><li><p><strong>Identify Performance Bottlenecks:</strong> The UI displays timestamps and time deltas between log messages, allowing users to easily see which parts of the code take longer to execute.</p></li><li><p><strong>Gain Code Comfort and Trust in AI:</strong> By clearly illustrating that the AI's role is writing code (and the code is doing the data analysis), the logs reduce the perception of a "black box." The logs help users become more comfortable with code-based analytics without requiring them to write the code themselves.</p></li><li><p><strong>Utilize Custom and AI-Assisted Debugging:</strong></p><ul><li><p><strong>Custom Debugging:</strong> Users can request the AI to insert custom <code>logger.debug</code> statements in the code (e.g., to display the top 10 results) without affecting the final rendered chart.</p></li><li><p><strong>AI-Assisted Debugging:</strong> The system uses log outputs to inform its AI auto-correction loop, which may insert its own <code>logger.debug</code> statements when attempting to patch faulty code, resulting in better fixes.</p></li></ul></li><li><p><strong>Analyze Errors:</strong> When an error occurs, the tab displays rich tracebacks, including the error, the traceback, and the log messages immediately preceding it to provide additional context for the failure.</p><p></p></li></ul><p></p>

Verify AI-Generated Data Analysis

<h2>User Problem</h2><p></p><p>Users often require specific, detailed context—like custom code, screenshots of existing dashboards or charts, styling guidelines, or detailed requirement documents—to ensure the LLM generates applications and components (charts, tables, etc.) that meet exact project specifications. Without a dedicated method to provide this context, the LLM's output may be generic or fail to align with the project's unique needs, leading to extra manual rework.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p></p><p>This feature is a File Upload and Context Attachment mechanism for Plotly Studio projects and components. It allows users to upload files and associate their content as context for the underlying Large Language Model (LLM). The LLM can then utilize this information during the generation process for applications or individual components.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p></p><p>This feature allows users to directly influence and guide the LLM's output by providing essential external files. Specifically, users can:</p><ul><li><p><strong>Provide Code &amp; Styling:</strong> Upload files like stylesheets (for custom theming) or <strong>Python scripts</strong> (for specific calculations or data manipulation logic) to ensure generated components and apps adhere to technical standards.</p></li><li><p><strong>Specify Requirements:</strong> Attach requirements documents, screenshots, or detailed specifications to direct the LLM to build apps and charts that precisely match project goals and constraints.</p></li><li><p><strong>Control Context Scope:</strong> Attach files to an entire project when building a new application, or attach them to individual components when generating specific charts or tables, giving users fine-grained control over the context provided to the LLM.</p><p></p></li></ul><p></p>

Files as AI Context

<h2>User Problem</h2><p>Currently, users who rely on private Python packages for their data analysis and application development face a significant hurdle. They need a secure and streamlined way to integrate these internal packages into their Plotly Studio projects, similar to how they would use publicly available packages. Without this capability, their workflows are disrupted, and they're unable to fully leverage their organization's proprietary code within Plotly Studio.</p><div data-type="horizontalRule"><hr></div><h2>What Is It?</h2><p>This feature introduces Private Index Packages support within Plotly Studio. It provides a secure and integrated mechanism for Plotly Studio to connect with and install a private package from a private package index (like a private PyPI server or an artifactory).</p><div data-type="horizontalRule"><hr></div><h2>What Does It Allow Users to Do?</h2><p>Private Index Packages in Plotly Studio will allow users to:</p><ul><li><p><strong>Install private Python packages:</strong> Users can seamlessly install packages hosted on their organization's private index directly into their Plotly Studio environments.</p></li><li><p><strong>Manage dependencies:</strong> Users can easily manage dependencies on private packages, ensuring their data applications have access to the necessary internal libraries.</p></li><li><p><strong>Enhance security:</strong> Users can work with their sensitive or proprietary code in a secure environment without exposing it to public repositories.</p></li></ul><p></p>

Private Index Packages

<p><strong>Background</strong> Plotly Studio users don't often know how to write CSS themselves, and struggle to use DDK effectively because it requires manual code to be written in a <code>theme.js</code> file and is not user-friendly. While Plotly Studio offers a "Custom" theme, users have reported that this is too limited for their needs. </p> <strong>Problem</strong> Plotly Studio users need an easier, and more granular way to define the style of their app. Ideally users should be able to affect styles interactively and see the preview instantaneously.  <strong>Early Requirements</strong> Users need to be able to define: - Colors:     - Primary, secondary, etc. colors     - Default chart color palette (can be overridden by chart configurations) - Fonts     - Font face     - Sizes - Layout spacing - Rounded vs. sharp corners - Table styles     - Alternating backgrounds     - Spacing     - Column summaries On\/Off

App Style Editor UI

<h3>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.</h3><p></p>

Completed

Data Sources

<h2>User Problem</h2><p></p><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p></p><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What Does it Allow Users to Do?</h2><p></p><p>This feature allows users to:</p><ul><li><p><strong>Automate Data Updates:</strong> 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.</p></li><li><p><strong>Customize Refresh Frequency</strong>: 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.</p></li><li><p><strong>Set Caching Rules:</strong> Users can choose from distinct data refresh/caching behaviors to optimize performance and data freshness:</p><ul><li><p><strong>Never:</strong> The data remains static.</p></li><li><p><strong>On-demand:</strong> The data refreshes every time the application is loaded.</p></li><li><p><strong>Scheduled:</strong> The data refreshes based on the user-defined frequency.</p></li></ul></li></ul><p></p>

<h2>ปัญหาของผู้ใช้</h2><p></p><p>ผู้ใช้ที่กำลังสร้างแอปพลิเคชันที่ขับเคลื่อนด้วยข้อมูลใน Plotly Studio ในปัจจุบันยังไม่มีวิธีในการกระตุ้นการอัปเดตข้อมูล ต้องพึ่งพาวิธีการแก้ปัญหาชั่วคราวและการเผยแพร่แอปใหม่ด้วยตนเอง ซึ่งอาจทำให้แอปแสดงข้อมูลที่ล้าสมัย ต้องการการแทรกแซงด้วยตนเองอย่างต่อเนื่อง และขัดขวางความสามารถในการนำเสนอข้อมูลเชิงลึกที่ทันเวลาและแม่นยำให้กับผู้ชม</p><div data-type="horizontalRule"><hr></div><h2>มันคืออะไร?</h2><p></p><p>นี่คือความสามารถใหม่ภายใน Plotly Studio ที่แนะนำการรีเฟรชข้อมูลตามกำหนดเวลาและกฎการแคชสำหรับแอปพลิเคชัน มันให้กลไกที่แข็งแกร่งและอยู่เบื้องหลังเพื่อกำหนดวิธีการและเวลาที่ข้อมูลพื้นฐานสำหรับการอัปเดตแอปจะถูกดึงออกมา</p><div data-type="horizontalRule"><hr></div><h2>ผู้ใช้สามารถทำอะไรได้บ้าง?</h2><p></p><p>คุณสมบัตินี้ช่วยให้ผู้ใช้สามารถ:</p><ul><li><p><strong>ระบบอัตโนมัติการอัปเดตข้อมูล:</strong>ผู้ใช้สามารถกำหนดตารางเวลาที่เฉพาะเจาะจงและเกิดขึ้นเป็นประจำสำหรับการดึงข้อมูลของแอปพลิเคชันของตนเพื่อให้ข้อมูลถูกดึงและอัปเดตโดยอัตโนมัติ ซึ่งทำให้แอปพลิเคชันของพวกเขาแสดงข้อมูลที่ทันสมัยที่สุดอยู่เสมอโดยไม่ต้องมีการดำเนินการด้วยตนเอง</p></li><li><p><strong>ปรับแต่งความถี่ในการรีเฟรช</strong>: ผู้ใช้สามารถเลือกช่วงเวลาในการรีเฟรชผ่านอินเทอร์เฟซที่ใช้งานง่าย (เช่น ทุก 30 นาที, วันละครั้ง, รายสัปดาห์) หรือกำหนดตารางเวลาที่เฉพาะเจาะจงและขั้นสูงโดยใช้คำสั่ง crontab</p></li><li><p><strong>กำหนดกฎการแคชที่ชัดเจน:</strong>ผู้ใช้สามารถเลือกจากพฤติกรรมการรีเฟรช/แคชข้อมูลที่แตกต่างกันเพื่อเพิ่มประสิทธิภาพและความสดใหม่ของข้อมูล:</p><ul><li><p><strong>ไม่เคย:</strong>ข้อมูลยังคงไม่เปลี่ยนแปลง</p></li><li><p><strong>ตามความต้องการ:</strong>ข้อมูลจะได้รับการรีเฟรชทุกครั้งที่แอปพลิเคชันถูกโหลด</p></li><li><p><strong>กำหนดเวลา:</strong>ข้อมูลจะได้รับการปรับปรุงใหม่ตามความถี่ที่ผู้ใช้กำหนด</p></li></ul></li><li><p><strong>ตรวจสอบการดำเนินการ:</strong>ผู้ใช้จะเห็นกำหนดการเสนอที่แสดงรายละเอียดการรีเฟรชข้อมูลในอนาคต ซึ่งช่วยให้พวกเขาสามารถยืนยันได้อย่างรวดเร็วว่าการตั้งค่าที่เลือกตรงกับความคาดหวังของพวกเขาในเรื่องความสดใหม่ของข้อมูล</p></li></ul><p></p>

การปรับปรุงข้อมูลตามกำหนดเวลาและการแคช

Scheduled Data Refresh & Caching

<h2>User problem</h2><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p>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. </p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p>This feature allows users to:</p><ul><li><p> <strong>Create "save points":</strong> Users can explicitly duplicate a project after achieving a stable or desirable state, effectively creating a snapshot of their work. </p></li><li><p><strong>Experiment safely:</strong> Users can then make potentially destructive edits or explore new design variations on the duplicated project without impacting the original "good" version. </p></li><li><p><strong>Revert to previous states:</strong> 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.</p></li><li><p><strong>Streamline development workflow:</strong> By enabling non-destructive editing and version control, users can iterate on their applications more efficiently and with greater confidence.</p></li></ul><p></p>

Replicate Project

<h2>User Problem</h2><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What Is It?</h2><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What Does It Allow Users to Do?</h2><p>This new editor allows users to:</p><ul><li><p><strong>Move Components:</strong> 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.</p></li><li><p><strong>Resize Components:</strong> 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.</p></li><li><p><strong>Intuitively Design Layouts:</strong> 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.</p></li><li><p><strong>Simplify Workflow:</strong> Users can eliminate context-switching by making layout changes directly within the visual interface, speeding up the design and iteration process.</p></li></ul><p></p>

Drag and Drop Layouts

<h2>User Problem</h2><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What Is It?</h2><p>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.</p><div data-type="horizontalRule"><hr></div><h2>What Does It Allow Users to Do?</h2><p>This expanded support for file uploads allows users to:</p><ul><li><p><strong>Upload Diverse Data</strong>: 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.</p></li><li><p><strong>Streamline Data Import</strong>: 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.</p></li><li><p><strong>Maintain Focus</strong>: Users can keep their entire data analysis and visualization process within Plotly Studio, resulting in a more integrated and efficient experience.</p></li><li><p><strong>Handle Complex Data</strong>: 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.</p></li></ul><p></p>

Expanded File Type Support

<h2>User problem</h2><p>Users currently lack a secure and centralized way to store and manage sensitive credentials (like API keys or database passwords) that are required by their Plotly applications. Storing these directly in application code or configuration files risks exposure in source control, application logs, or during debugging, especially in environments involving large language model (LLM) requests. They need a mechanism to use credentials within their applications without the risk of them being unintentionally leaked. </p><p></p><h2>What is it? </h2><p>Secrets in Plotly Studio is a new capability for secure credential storage within Plotly Cloud. It provides a dedicated, safe location for users to define and manage their sensitive information as groups of key\/value pairs.</p><p></p><p>The first implementation of this feature will be leveraged by Data Views feature, without a UI to manage secrets independently, with a management UI to follow in a subsequent release.</p><p></p><h2>What does it allow users to do? It allows users to: </h2><ul><li><p>Store and organize sensitive credentials (secrets) securely in the user’s computer keychain and in Plotly Cloud.</p></li><li><p>Safely access these stored secrets within their Plotly applications using a new dedicated secrets library.</p></li><li><p>Prevent accidental leakage of sensitive credentials, as the secrets library is designed to ensure the values are not exposed in LLM requests or application logs.</p></li><li><p>Provide secure access to other Plotly features, such as the upcoming Data Views feature, by providing the necessary credentials through this secure mechanism.</p></li></ul><p></p><p></p>

Secrets Support

<h2>User problem</h2><p>Users currently lack the ability to perform basic data preparation tasks directly within Plotly Studio, leading to the need for external tools to clean, filter, group, and combine datasets before they can be used to generate applications. This creates an inefficient workflow and a barrier to quickly leveraging Plotly Studio for data visualization and app creation.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p>This feature introduces a suite of lightweight data preparation tools directly integrated into Plotly Studio. It will provide users with an intuitive interface to inspect and refine their datasets before they are used for application generation.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p>This feature will allow users to:</p><ul><li><p><strong>Handle null or missing values:</strong> Users can easily identify and address missing data points through various methods, such as removing rows with nulls, filling nulls with a specific value (e.g., mean, median, mode, or a constant), or interpolating missing values.</p></li><li><p><strong>Pre-filter their data:</strong> Users can apply filters to their datasets based on specific criteria, allowing them to focus on relevant subsets of their data for analysis and visualization.</p></li><li><p><strong>Group their data:</strong> Users can group data by one or more columns, enabling aggregation and summarization of information for more insightful analysis.</p></li><li><p><strong>Join multiple tables together:</strong> Users can combine data from different tables based on common keys, facilitating the creation of comprehensive datasets for their applications. </p></li></ul><p></p>

Lightweight Data Preparation

<h2>User Problem</h2><p>Currently, users working on complex applications in Plotly Studio face significant friction and the constant risk of data loss. Without a built-in way to track changes, users must manually manage different versions of their work or risk losing hours of progress if an experiment fails or a mistake is made. This lack of a safety net makes it difficult to manage long-running, high-value projects and discourages power users from performing deep, iterative design work.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p>This feature is an integrated local version control system designed specifically for the Plotly Studio environment. It allows for the systematic creation of "snapshots"—complete records of an component’s state at a specific point in time. These snapshots can be generated automatically at periodic intervals or manually by the user, providing a comprehensive history of the component’s development lifecycle.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p>The version control system provides:</p><ul><li><p><strong>Rollback to Previous States:</strong> Users can instantly revert a component’s state to a specific previous snapshot. This ensures that if an error occurs or a design direction proves unsuccessful, the project can be restored to a known working state without manual reconstruction.</p></li></ul><p></p>

Component Versioning

Nathan Drezner

<h2>User Problem</h2><p>Currently, users encounter a high-density grid of large project cards immediately upon opening Plotly Studio. As a user's library grows, this layout becomes difficult to navigate because there are no native tools to filter or categorize content. Without a search function or a metadata system, users must manually scroll through every project to locate specific work, leading to friction and inefficiency in their workflow.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p>This feature is a project management interface update that introduces a more compact visual layout for project cards. It integrates a dedicated search bar and a flexible tagging system into the Plotly Studio dashboard. By transitioning from large, uniform cards to a streamlined design, the interface can display more information simultaneously while providing the tools necessary to sort and filter that information.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p>The proposed updates provide several functional improvements for managing large volumes of work:</p><ul><li><p><strong>Efficient Discovery:</strong> Users can utilize the search bar to find specific projects by title or keywords instantly, eliminating manual scrolling.</p></li><li><p><strong>Custom Organization:</strong> Users can assign custom tags to their projects, allowing them to categorize work by client, department, status, or project type.</p></li><li><p><strong>Improved Scannability:</strong> The smaller card components allow users to view a higher number of projects on a single screen, providing a better bird's-eye view of their entire workspace.</p></li><li><p><strong>Filtered Views:</strong> By selecting specific tags, users can isolate related projects, creating a focused environment for specific tasks or workflows.</p></li></ul><p></p>

Project Search & Tagging

<h2>User Problem</h2><p>Currently, users managing multiple projects or collaborating across different departments in Plotly Cloud lack a streamlined way to organize individuals into functional groups. This makes managing permissions repetitive and time-consuming, as access controls must often be managed on an individual basis. Furthermore, users struggle with visibility into resource consumption, such as AI credits, when multiple people are working toward a collective goal or under a specific budget.</p><div data-type="horizontalRule"><hr></div><h2>What is it?</h2><p><strong>Teams for Plotly Cloud</strong> is a structural management feature for Plotly Cloud designed to group users together for shared resource allocation and centralized access control. This feature integrates directly into the Plotly Studio interface, introducing a dedicated <strong>Team Switcher</strong> and an updated <strong>Sharing Options UI</strong>. It establishes a clear framework for defining which group of users a project belongs to and which credit pool should be utilized for AI-driven tasks.</p><div data-type="horizontalRule"><hr></div><h2>What does it allow users to do?</h2><p>The introduction of Teams in Plotly Cloud via Plotly Studio provides users with several key capabilities to improve their workflow and administrative oversight:</p><ul><li><p><strong>Centralize Access Control:</strong> Users can define specific teams and apply sharing permissions to the entire group at once, rather than managing individual collaborators for every new Plotly Studio app or dashboard.</p></li><li><p><strong>Manage Resource Allocation:</strong> Through the Team Switcher, users can select the appropriate team to act as the publishing target and the source for AI credits.</p></li><li><p><strong>Monitor Usage Transparency:</strong> Users can view a real-time credit burndown bar within the interface, allowing them to track how much of a team’s AI credit pool remains as they work.</p></li><li><p><strong>Streamline App Distribution:</strong> The integrated Sharing Options UI allows users to configure the visibility and security settings of their published applications directly from the Studio environment, ensuring the right teams have immediate access to the output.</p></li></ul><p></p>