0.7 alpha 2

[Chenglong-MS]

This pull request introduces Docker support for the Data Formulator project, making it easier to run the application without local Python or Node.js setup. It also refactors CORS handling for better security and configuration, and includes several improvements and fixes to agent logic and logging. The changes span new Docker-related files, Python backend adjustments, and updates to agent code for more accurate metadata and logging.

Dockerization and Development Environment:

• Added a production-ready Dockerfile with a multi-stage build to bundle the frontend and backend, and a docker-compose.yml for easy orchestration and persistent workspace data. .dockerignore is included to optimize builds. [1] [2] [3]
• Updated DEVELOPMENT.md with Docker usage instructions and caveats about sandboxing in containerized environments.
• Added VS Code workspace settings for Python interpreter and terminal tool auto-approval.

Backend API and CORS Handling:

• Centralized and configurable CORS headers using an @after_request handler in agent_routes.py, removing duplicated and insecure Access-Control-Allow-Origin: * headers from individual endpoints. Now, CORS is controlled via the CORS_ORIGIN environment variable. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Agent Logic and Metadata Improvements:

• Updated semantic type references and metadata in agent prompts and logic for more precise field categorization (e.g., using "Category" instead of "Type", refining monetary and discrete numeric types). [1] [2] [3]
• Improved guidance in agent prompts for scale/domain inference and clarified open-ended measure handling.
• Enhanced logging in agent_data_rec.py and agent_data_transform.py to include LLM token usage and clarify timing breakdowns. [1] [2]
• Adjusted image detail levels in chart/image payloads for agents to optimize LLM input and output. [1] [2] [3] [4]

Other Improvements:

• Added more detailed logging in agent routines for better observability during LLM calls. [1] [2]
• Minor corrections and consistency improvements in agent prompt templates and comments. [1] [2] [3]

These changes collectively improve deployment flexibility, security, and developer experience, while also enhancing the correctness and observability of agent operations.