[deploy] Merge pull request #189 from microsoft/dev

[deploy] Dev: data formulator 0.5 release

Author: Chenglong-MS

DEVELOPMENT.md

@@ -106,5 +106,41 @@ How to set up your local machine.
     Open [http://localhost:5000](http://localhost:5000) to view it in the browser.
 
 
+## Security Considerations for Production Deployment
+
+⚠️ **IMPORTANT SECURITY WARNING FOR PRODUCTION DEPLOYMENT**
+
+When deploying Data Formulator to production, please be aware of the following security considerations:
+
+### Database Storage Security
+
+1. **Local DuckDB Files**: When database functionality is enabled (default), Data Formulator stores DuckDB database files locally on the server. These files contain user data and are stored in the system's temporary directory or a configured `LOCAL_DB_DIR`.
+
+2. **Session Management**: 
+   - When database is **enabled**: Session IDs are stored in Flask sessions (cookies) and linked to local DuckDB files
+   - When database is **disabled**: No persistent storage is used, and no cookies are set. Session IDs are generated per request for API consistency
+
+3. **Data Persistence**: User data processed through Data Formulator may be temporarily stored in these local DuckDB files, which could be a security risk in multi-tenant environments.
+
+### Recommended Security Measures
+
+For production deployment, consider:
+
+1. **Use `--disable-database` flag** for stateless deployments where no data persistence is needed
+2. **Implement proper authentication, authorization, and other security measures** as needed for your specific use case, for example:
+   - Store DuckDB file in a database
+   - User authentication (OAuth, JWT tokens, etc.)
+   - Role-based access control
+   - API rate limiting
+   - HTTPS/TLS encryption
+   - Input validation and sanitization 
+
+### Configuration for Production
+
+```bash
+# For stateless deployment (recommended for public hosting)
+python -m data_formulator.app --disable-database
+```
+
 ## Usage
 See the [Usage section on the README.md page](README.md#usage).

MANIFEST.in

@@ -1,2 +1,4 @@
 include py-src/data_formulator/dist/*
-include py-src/data_formulator/dist/assets/*
+include py-src/data_formulator/dist/assets/*
+global-exclude .DS_Store
+exclude py-src/examples

README.md

@@ -1,30 +1,61 @@
 <h1>
-    <img src="./public/favicon.ico" alt="Data Formulator icon" width="28"> <b>Data Formulator: Create Rich Visualizations with AI</b>
+    <img src="./public/favicon.ico" alt="Data Formulator icon" width="28"> <b>Data Formulator: Vibe with data, in control</b>
 </h1>
 
 <div>
 
 [![arxiv](https://img.shields.io/badge/Paper-arXiv:2408.16119-b31b1b.svg)](https://arxiv.org/abs/2408.16119)&ensp;
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)&ensp;
-[![YouTube](https://img.shields.io/badge/YouTube-white?logo=youtube&logoColor=%23FF0000)](https://youtu.be/3ndlwt0Wi3c)&ensp;
+[![YouTube](https://img.shields.io/badge/YouTube-white?logo=youtube&logoColor=%23FF0000)](https://www.youtube.com/watch?v=GfTE2FLyMrs)&ensp;
 [![build](https://github.com/microsoft/data-formulator/actions/workflows/python-build.yml/badge.svg)](https://github.com/microsoft/data-formulator/actions/workflows/python-build.yml)
 [![Discord](https://img.shields.io/badge/discord-chat-green?logo=discord)](https://discord.gg/mYCZMQKYZb)
 
 </div>
 
-Transform data and create rich visualizations iteratively with AI 🪄. Try Data Formulator now!
+🪄 Turn data into insights with AI Agents, with the exploration paths you choose. Try Data Formulator now!
 
-Any questions? Ask on the Discord channel! [![Discord](https://img.shields.io/badge/discord-chat-green?logo=discord)](https://discord.gg/mYCZMQKYZb)
+- 🤖 New in v0.5: agent model + interative control [(video)](https://www.youtube.com/watch?v=GfTE2FLyMrs)
+- 🔥🔥🔥 Try our online demo at [https://data-formulator.ai](https://data-formulator.ai)
+- Any questions, thoughts? Discuss in the Discord channel! [![Discord](https://img.shields.io/badge/discord-chat-green?logo=discord)](https://discord.gg/mYCZMQKYZb)
 
 <!-- [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/microsoft/data-formulator?quickstart=1) -->
 
-<kbd>
-  <a target="_blank" rel="noopener noreferrer" href="https://codespaces.new/microsoft/data-formulator?quickstart=1" title="open Data Formulator in GitHub Codespaces"><img src="public/data-formulator-screenshot.png"></a>
-</kbd>
+https://github.com/user-attachments/assets/8ca57b68-4d7a-42cb-bcce-43f8b1681ce2
 
+<!-- <kbd>
+  <a target="_blank" rel="noopener noreferrer" href="https://codespaces.new/microsoft/data-formulator?quickstart=1" title="open Data Formulator in GitHub Codespaces"><img src="public/data-formulator-screenshot-v0.5.png"></a>
+</kbd> -->
 
 
 ## News 🔥🔥🔥
+
+[11-07-2025] Data Formulator 0.5: Vibe with your data, in control
+
+- 📊 **Load (almost) any data**: load structured data, extract data from screenshots, from messy text blocks, or connect to databases.
+- 🤖 **Explore data with AI agents**: 
+  - In agent mode, provide a high-level goal and ask agents to explore data for you.
+  - To stay in control, directly interact with agents: ask for recommendations or specify chart designs with UI + NL inputs, and AI agents will formulate data to realize your design.
+  - Use data threads to control branching exploration paths: backtrack, branch, or follow up.
+- ✅ **Verify AI generated results**: interact with charts and inspect data, formulas, explanations, and code.
+- 📝 **Create reports to share insights**: choose charts you want to share, and ask agents to create reports grounded in data formulated throughout exploration.
+
+## Previous Updates
+
+Here are milestones that lead to the current design:
+- **v0.2.2** ([Demo](https://github.com/microsoft/data-formulator/pull/176)): Goal-driven exploration with agent recommendations and performance improvements
+- **v0.2.1.3/4** ([Readme](https://github.com/microsoft/data-formulator/tree/main/py-src/data_formulator/data_loader) | [Demo](https://github.com/microsoft/data-formulator/pull/155)): External data loaders (MySQL, PostgreSQL, MSSQL, Azure Data Explorer, S3, Azure Blob)
+- **v0.2** ([Demos](https://github.com/microsoft/data-formulator/releases/tag/0.2)): Large data support with DuckDB integration
+- **v0.1.7** ([Demos](https://github.com/microsoft/data-formulator/releases/tag/0.1.7)): Dataset anchoring for cleaner workflows
+- **v0.1.6** ([Demo](https://github.com/microsoft/data-formulator/releases/tag/0.1.6)): Multi-table support with automatic joins
+- **Model Support**: OpenAI, Azure, Ollama, Anthropic via [LiteLLM](https://github.com/BerriAI/litellm) ([feedback](https://github.com/microsoft/data-formulator/issues/49))
+- **Python Package**: Easy local installation ([try it](#get-started))
+- **Visualization Challenges**: Test your skills ([challenges](https://github.com/microsoft/data-formulator/issues/53))
+- **Data Extraction**: Parse data from images and text ([demo](https://github.com/microsoft/data-formulator/pull/31#issuecomment-2403652717))
+- **Initial Release**: [Blog](https://www.microsoft.com/en-us/research/blog/data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations/) | [Video](https://youtu.be/3ndlwt0Wi3c)
+
+<details>
+<summary><b>View detailed update history</b></summary>
+
 - [07-10-2025] Data Formulator 0.2.2: Start with an analysis goal
   - Some key frontend performance updates. 
   - You can start your exploration with a goal, or, tab and see if the agent can recommend some good exploration ideas for you. [Demo](https://github.com/microsoft/data-formulator/pull/176)
@@ -74,11 +105,13 @@ Any questions? Ask on the Discord channel! [![Discord](https://img.shields.io/ba
 
 - [10-01-2024] Initial release of Data Formulator, check out our [[blog]](https://www.microsoft.com/en-us/research/blog/data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations/) and [[video]](https://youtu.be/3ndlwt0Wi3c)!
 
+</details>
+
 ## Overview
 
-**Data Formulator** is an application from Microsoft Research that uses large language models to transform data, expediting the practice of data visualization.
+**Data Formulator** is an application from Microsoft Research that uses AI agents to make it easier to turn data into insights.
 
-Data Formulator is an AI-powered tool for analysts to iteratively create rich visualizations. Unlike most chat-based AI tools where users need to describe everything in natural language, Data Formulator combines *user interface interactions (UI)* and *natural language (NL) inputs* for easier interaction. This blended approach makes it easier for users to describe their chart designs while delegating data transformation to AI. 
+Data Formulator is an AI-powered tool for analysts to iteratively explore and visualize data. Started with data in any format (screenshot, text, csv, or database), users can work with AI agents with a novel blended interface that combines *user interface interactions (UI)* and *natural language (NL) inputs* to communicate their intents, control branching exploration directions, and create reports to share their insights. 
 
 ## Get Started
 
@@ -92,16 +125,13 @@ Play with Data Formulator with one of the following options:
   # install data_formulator
   pip install data_formulator
 
-  # start data_formulator
-  data_formulator 
-  
-  # alternatively, you can run data formulator with this command
+  # Run data formulator with this command
   python -m data_formulator
   ```
 
   Data Formulator will be automatically opened in the browser at [http://localhost:5000](http://localhost:5000).
 
-  *Update: you can specify the port number (e.g., 8080) by `python -m data_formulator --port 8080` if the default port is occupied.*
+  *you can specify the port number (e.g., 8080) by `python -m data_formulator --port 8080` if the default port is occupied.*
 
 - **Option 2: Codespaces (5 minutes)**
 
@@ -111,16 +141,38 @@ Play with Data Formulator with one of the following options:
 
 - **Option 3: Working in the developer mode**
 
-  You can build Data Formulator locally if you prefer full control over your development environment and the ability to customize the setup to your specific needs. For detailed instructions, refer to [DEVELOPMENT.md](DEVELOPMENT.md).
+  You can build Data Formulator locally if you prefer full control over your development environment and develop your own version on top. For detailed instructions, refer to [DEVELOPMENT.md](DEVELOPMENT.md).
 
 
 ## Using Data Formulator
 
-Once you've completed the setup using either option, follow these steps to start using Data Formulator:
+### Load Data
+
+Besides uploading csv, tsv or xlsx files that contain structured data, you can ask Data Formulator to extract data from screenshots, text blocks or websites, or load data from databases use connectors. Then you are ready to explore.
+
+<img width="1920" alt="image" src="https://github.com/user-attachments/assets/e23cdb47-984c-4ce4-a014-8f36e025e393" />
+
+### Explore Data
+
+There are four levels to explore data based depending on whether you want more vibe or more control:
 
+- Level 1 (most control): Create charts with UI via drag-and-drop, if all fields to be visualized are already in the data.
+- Level 2: Specify chart designs with natural language + NL. Describe how new fields should be visualized in your chart, AI will automatically transform data to realize the design.
+- Level 3: Get recommendations: Ask AI agents to recommend charts directly from NL descriptions, or even directly ask for exploration ideas.
+- Level 4 (most vibe): In agent mode, provide a high-level goal and let AI agents automatically plan and explore data in multiple turns. Exploration threads will be created automatically.
+
+https://github.com/user-attachments/assets/164aff58-9f93-4792-b8ed-9944578fbb72
+
+- Level 5: In practice, leverage all of them to keep up with both vibe and control!
+
+### Create Reports
+
+Use the report builder to compose a report of the style you like, based on selected charts. Then share the reports to others!
+
+<!-- 
 ### The basics of data visualization
-* Provide OpenAI keys and select a model (GPT-4o suggested) and choose a dataset.
-* Choose a chart type, and then drag-and-drop data fields to chart properties (x, y, color, ...) to specify visual encodings.
+* Set up model provider, for agentic experience, model with reasoning and strong code generation ablity is recommended.
+* Describe the exploration 
 
 https://github.com/user-attachments/assets/0fbea012-1d2d-46c3-a923-b1fc5eb5e5b8
 
@@ -140,12 +192,19 @@ https://github.com/user-attachments/assets/160c69d2-f42d-435c-9ff3-b1229b5bddba
 
 https://github.com/user-attachments/assets/c93b3e84-8ca8-49ae-80ea-f91ceef34acb
 
-Repeat this process as needed to explore and understand your data. Your explorations are trackable in the **Data Threads** panel. 
+Repeat this process as needed to explore and understand your data. Your explorations are trackable in the **Data Threads** panel.  -->
 
 ## Developers' Guide
 
 Follow the [developers' instructions](DEVELOPMENT.md) to build your new data analysis tools on top of Data Formulator.
 
+Help wanted:
+
+* Add more database connectors (https://github.com/microsoft/data-formulator/issues/156)
+* Scaling up messy data extractor: more document types and larger files.
+* Adding more chart templates (e.g., maps).
+* other ideas?
+
 ## Research Papers
 * [Data Formulator 2: Iteratively Creating Rich Visualizations with AI](https://arxiv.org/abs/2408.16119)
 

api-keys.env.template

@@ -1,24 +1,24 @@
 # OpenAI Configuration
 OPENAI_ENABLED=true
 OPENAI_API_KEY=#your-openai-api-key
-OPENAI_MODELS=gpt-4.5-preview,o3-mini,gpt-4o-mini,o1,o1-mini # comma separated list of models
+OPENAI_MODELS=gpt-5,gpt-5-mini,gpt-5-nano,o1,o1-mini # comma separated list of models
 
 # Azure OpenAI Configuration
 AZURE_ENABLED=true
 AZURE_API_KEY=#your-azure-openai-api-key
 AZURE_API_BASE=https://your-azure-openai-endpoint.openai.azure.com/
 AZURE_API_VERSION=2024-02-15-preview
-AZURE_MODELS=o3-mini,gpt-4o
+AZURE_MODELS=gpt-4.1
 
 # Anthropic Configuration
 ANTHROPIC_ENABLED=true
 ANTHROPIC_API_KEY=#your-anthropic-api-key
-ANTHROPIC_MODELS=claude-3-7-sonnet-latest,claude-3-5-haiku-latest
+ANTHROPIC_MODELS=claude-sonnet-4-20250514
 
 # Ollama Configuration
 OLLAMA_ENABLED=true
 OLLAMA_API_BASE=http://localhost:11434
-OLLAMA_MODELS=codellama:7b # models with good code generation capabilities recommended
+OLLAMA_MODELS=deepseek-v3.1:latest # models with good code generation capabilities recommended
 
 # if you want to add other models, you can add them with PROVIDER_API_KEY=your-api-key, PROVIDER_MODELS=model1,model2 etc 
 # (replacing PROVIDER with the provider name like GEMINI, ANTHROPIC, AZURE, OPENAI, OLLAMA etc. as long as they are supported by LiteLLM)

index.html

@@ -9,6 +9,13 @@
       name="Data Formulator"
       content="Concept-driven Visualization Authoring"
     />
+    
+    <!-- Preload critical images for faster initial load -->
+    <link rel="preload" as="image" href="/gas_prices-thumbnail.webp" type="image/webp" />
+    <link rel="preload" as="image" href="/global_energy-thumbnail.webp" type="image/webp" />
+    <link rel="preload" as="image" href="/movies-thumbnail.webp" type="image/webp" />
+    <link rel="preload" as="image" href="/unemployment-thumbnail.webp" type="image/webp" />
+    
     <!--
       manifest.json provides metadata used when your web app is installed on a
       user's mobile device or desktop. See https://developers.google.com/web/fundamentals/web-app-manifest/

package.json

@@ -8,45 +8,46 @@
         "@emotion/styled": "^11.14.0",
         "@fontsource/roboto": "^4.5.5",
         "@mui/icons-material": "^7.1.1",
+        "@mui/lab": "^7.0.1-beta.18",
         "@mui/material": "^7.1.1",
         "@reduxjs/toolkit": "^1.8.6",
         "@types/dompurify": "^3.0.5",
         "@types/validator": "^13.12.2",
-        "ag-grid-community": "^32.0.2",
-        "ag-grid-enterprise": "^32.0.2",
-        "ag-grid-react": "^32.0.2",
+        "allotment": "^1.20.4",
         "d3": "^7.3.0",
         "dompurify": "^3.2.4",
+        "exceljs": "^4.4.0",
+        "html2canvas": "^1.4.1",
+        "katex": "^0.16.22",
         "localforage": "^1.10.0",
         "lodash": "^4.17.21",
         "markdown-to-jsx": "^7.4.0",
-        "mui-markdown": "^1.1.13",
+        "mui-markdown": "^2.0.3",
         "prettier": "^2.8.3",
         "prism-react-renderer": "^1.3.5",
-        "prismjs": "^1.29.0",
+        "prismjs": "^1.30.0",
+        "prop-types": "^15.8.1",
         "react": "^18.2.0",
         "react-animate-height": "^3.0.4",
         "react-animate-on-change": "^2.2.0",
-        "react-diff-viewer": "^3.1.1",
         "react-dnd": "^16.0.1",
         "react-dnd-html5-backend": "^16.0.1",
         "react-dom": "^18.2.0",
+        "react-katex": "^3.1.0",
         "react-redux": "^8.0.4",
         "react-router-dom": "^6.22.0",
         "react-selectable-fast": "^3.4.0",
         "react-simple-code-editor": "^0.13.1",
-        "react-split-pane": "^0.1.92",
         "react-vega": "^7.6.0",
         "react-virtuoso": "^4.3.10",
         "redux": "^4.2.0",
         "redux-persist": "^6.0.0",
         "typescript": "^4.9.5",
-        "validator": "^13.12.0",
+        "validator": "^13.15.20",
         "vega": "^5.32.0",
         "vega-embed": "^6.21.0",
         "vega-lite": "^5.5.0",
-        "vm-browserify": "^1.1.2",
-        "xlsx": "^0.18.5"
+        "vm-browserify": "^1.1.2"
     },
     "scripts": {
         "lint": "eslint -c eslint.config.js src/**/*.{ts,tsx} --fix",
@@ -73,6 +74,7 @@
         "@types/prismjs": "^1.26.0",
         "@types/react": "^18.3.3",
         "@types/react-dom": "^18.3.0",
+        "@types/react-katex": "^3.0.4",
         "@typescript-eslint/eslint-plugin": "^8.16.0",
         "@typescript-eslint/parser": "^8.16.0",
         "@vitejs/plugin-react-swc": "^3.7.0",
@@ -82,6 +84,6 @@
         "globals": "^15.12.0",
         "sass": "^1.77.6",
         "typescript-eslint": "^8.16.0",
-        "vite": "^5.4.19"
+        "vite": "^5.4.21"
     }
 }