mcpserver.so LogoMCPFly
Submit
MCP-ChatBot logo

MCP-ChatBot

by arkCyber

AI/Chatbots & Assistantschatbotrustollamaopenairagqdrantwhispertool integration

MCP-ChatBot is a powerful Rust-based chatbot framework leveraging the Model Context Protocol (MCP) for multi-server support and tool integration. It allows seamless switching between AI models and provides a flexible protocol for enhancing AI model interactions.

View on GitHub

Last updated: N/A

What is MCP-ChatBot?

MCP-ChatBot is a Rust-based chatbot framework built upon the Model Context Protocol (MCP). It provides a modular architecture for managing multiple specialized servers, integrating various tools, and supporting different AI models, including local (Ollama) and cloud-based (OpenAI) options.

How to use MCP-ChatBot?

To use MCP-ChatBot, clone the repository, build the project using cargo build --release, configure the necessary API keys in a .env file, and customize server configurations and prompts. You can then run the chatbot using cargo run and interact with it through the command-line interface, utilizing available commands for AI switching, RAG operations, and voice input.

Key features of MCP-ChatBot

  • Multi-AI Support (Ollama, OpenAI)

  • Tool Integration (Memory, SQLite, File Operations)

  • Multi-Server Architecture

  • Interactive CLI

  • RAG Support (Qdrant)

  • Voice Input (Whisper)

Use cases of MCP-ChatBot

  • Multi-Model Collaboration for complex tasks

  • Seamless integration of external tools and services

  • Maintaining consistent context across different operations

  • Efficient management of system resources and cleanup

FAQ from MCP-ChatBot

What is MCP?

MCP (Model Context Protocol) is a flexible protocol designed to enhance AI model interactions by providing structured context and tool integration capabilities.

What AI models are supported?

The chatbot supports Ollama (local) and OpenAI.

What is RAG support?

RAG (Retrieval Augmented Generation) support allows the chatbot to retrieve information from a vector database (Qdrant) to enhance its responses.

How do I configure API keys?

API keys should be configured in a .env file in the project root.

What tools are available?

Built-in tools include memory operations, SQLite database access, and file operations.