Reality Calendar
Provides LLMs with cached Google Drive data via an OpenAI-compatible proxy for efficient interaction.
About
This Python application serves as a backend for Large Language Models (LLMs), enabling them to access structured data from a Google Drive Excel file. To enhance efficiency and mitigate Google Drive API limitations, it implements a SQLite-based cache that refreshes daily and upon server restart. The tool integrates APScheduler for managing background synchronization tasks and utilizes `mcpo` to transform the MCP server into an OpenAI-compatible proxy, facilitating seamless integration with LLM frontends like OpenWebUI.
Key Features
- SQLite-based data caching for enhanced performance
- Daily and on-restart cache synchronization with Google Drive spreadsheets
- OpenAI-compatible proxy for seamless LLM integration via `mcpo`
- Background data updates managed by APScheduler
- Supports Google Drive API for spreadsheet access and parsing
- 0 GitHub stars
Use Cases
- Serving structured data from Google Drive to LLM applications
- Integrating LLM agents with external data sources requiring efficient caching
- Creating an OpenAI-compatible endpoint for data-driven LLM tool interactions
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