Business Analytics RAG

Combines business analytics capabilities for statistical operations on CSV data with RAG-powered knowledge retrieval from business documents, supporting both Google Gemini and custom OpenAI-compatible APIs for comprehensive business intelligence workflows.

Business analytics and knowledge retrieval system that combines MCP servers for data analysis and RAG (Retrieval-Augmented Generation) capabilities with flexible LLM backend support for both Google Gemini and custom localhost APIs. The implementation provides two specialized MCP servers: a business analytics server that performs statistical operations like mean calculation, correlation analysis, and linear regression on CSV business data, and a RAG server that searches through business knowledge documents for terms, definitions, and company policies. Built with Python using pandas for data processing and supporting both Gemini API and custom OpenAI-compatible endpoints, it's designed for business intelligence workflows where users need to combine quantitative data analysis with contextual business knowledge retrieval through natural language interactions.