Retrieve detailed information about a specific RAG project, including its structure, content, and configuration for effective library management and content retrieval.
Add files to a RAG system for document retrieval, supporting PDF, DOCX, TXT, MD, CSV, and JSON formats to enable semantic search and information access.
Process user questions using the RAG workflow to generate context-based answers. Automates query tokenization, embedding creation, semantic search, and top-k result retrieval using IVSM functions and rag_config.yml settings.
An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.
A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.
A Model Context Protocol server providing tools for querying A-share stock market data, including historical prices, financial reports, market indices, and macroeconomic indicators.