# Intelligent Q&A

A Discord bot server integrated with the Mastra MCP component, providing intelligent Q&A, thread management, and command processing functions.

This project is a RAG (Retrieval Augmented Generation) service based on the AWS serverless architecture. It uses Lambda, OpenSearch Serverless, and S3 for document storage and retrieval, provides an MCP protocol interface through API Gateway, and integrates OpenAI for embedding and generation.

A custom MCP server that connects to the Dust.tt AI agent platform, realizing multi - cloud service integration through HTTP calls, providing systems thinking agent, RAG support, and web navigation capabilities.

An MCP server based on the LlamaIndexTS library, providing multiple LLM tool functions

This is an MCP document Q&A server based on the GroundX API that supports uploading documents and conducting intelligent Q&A on the document content.

A RAG system based on Python code analysis. It parses the code structure through AST and stores it in the Weaviate vector database, providing code query, natural language Q&A, and visualization functions, and supporting multi-codebase management and dependency analysis.

This project provides a set of toolkits for crawling website content and generating Markdown documents. At the same time, it realizes the semantic search function of documents through the MCP server and supports integration with tools such as Cursor.

This project sets up an MCP server for querying World Mini Apps documentation and integrating it with Cursor to help developers develop MiniApps more efficiently.

The project uses TypeScript and Node.js to develop an MCP server and integrates the LLM Studio API to optimize question answering, supporting dynamic prompt optimization

An MCP server based on TypeScript that implements a Retrieval Augmented Generation (RAG) system for local documents, supporting querying and indexing of Git repositories and text files.

An intelligent learning assistance system based on PDF document analysis, providing functions such as document conversion, content organization, and question generation to help users learn efficiently.