add readme

Author: Chenglong-MS

DEVELOPMENT.md

@@ -20,9 +20,25 @@ How to set up your local machine.
     ```
 - **Configure environment variable (optional)s**
     - copy `api-keys.env.example` to `api-keys.env` and add your API keys.
-    - required fields for different providers are different, please refer to the [LiteLLM setup](https://docs.litellm.ai/docs#litellm-python-sdk) guide for more details.
-        - currently only endpoint, model, api_key, api_base, api_version are supported.
-    - this helps data formulator to automatically load the API keys when you run the app, so you don't need to set the API keys in the app UI.
+        - required fields for different providers are different, please refer to the [LiteLLM setup](https://docs.litellm.ai/docs#litellm-python-sdk) guide for more details.
+            - currently only endpoint, model, api_key, api_base, api_version are supported.
+        - this helps data formulator to automatically load the API keys when you run the app, so you don't need to set the API keys in the app UI.
+
+    - set `.env` to configure server properties:
+        - copy `.env.template` to `.env`
+        - configure settings as needed:
+            - DISABLE_DISPLAY_KEYS: if true, API keys will not be shown in the frontend
+            - EXEC_PYTHON_IN_SUBPROCESS: if true, Python code runs in a subprocess (safer but slower), you may consider setting it true when you are hosting Data Formulator for others
+            - LOCAL_DB_DIR: directory to store the local database (uses temp directory if not set)
+            - External database settings (when USE_EXTERNAL_DB=true):
+                - DB_NAME: name to refer to this database connection
+                - DB_TYPE: mysql or postgresql (currently only these two are supported)
+                - DB_HOST: database host address
+                - DB_PORT: database port
+                - DB_DATABASE: database name
+                - DB_USER: database username
+                - DB_PASSWORD: database password
+
 
 - **Run the app**
     - **Windows**

README.md

@@ -22,6 +22,16 @@ Transform data and create rich visualizations iteratively with AI 🪄. Try Data
 
 ## News 🔥🔥🔥
 
+- [04-23-2025] Data Formulator 0.2: working with *large* data 📦📦📦
+  - Explore large data by:
+    1. Upload large data file to the local database (powered by [DuckDB](https://github.com/duckdb/duckdb)).
+    2. Use drag-and-drop to specify charts, and Data Formulator dynamically fetches data from the database to create visualizations (with ⚡️⚡️⚡️ speeds).
+    3. Ask AI agents a new exploration question, and they generate SQL queries to transform the data to create rich visualizations!
+    4. Anchor the result, follow up, create a new branch, and join tables to dive deeper. 
+  - Checkout the demos at [[https://github.com/microsoft/data-formulator/releases/tag/0.2]](https://github.com/microsoft/data-formulator/releases/tag/0.2)
+  - Improved overall system performance, and enjoy the updated derive concept functionality.
+  - `pip install --upgrade data-formulator` now!
+
 - [03-20-2025] Data Formulator 0.1.7: Anchoring ⚓︎
   - Anchor an intermediate dataset, so that followup data analysis are built on top of the anchored data, not the original one.
   - Clean a data and work with only the cleaned data; create a subset from the original data or join multiple data, and then focus your analysis from there. The AI agent will be less likely to get confused and work faster. ⚡️⚡️