Llama MCP Streamlit
by nikunj2003
This project is an interactive AI assistant built with Streamlit, NVIDIA NIM's API/Ollama, and Model Control Protocol (MCP). It provides a conversational interface where you can interact with an LLM to execute real-time external tools via MCP, retrieve data, and perform actions seamlessly.
Last updated: N/A
What is Llama MCP Streamlit?
Llama MCP Streamlit is an AI assistant application that leverages Streamlit for the UI, NVIDIA NIM or Ollama as the LLM backend, and Model Control Protocol (MCP) for tool integration. It enables users to interact with a language model to perform actions through external tools.
How to use Llama MCP Streamlit?
To use this server, first configure the .env file with the appropriate API keys. Then, either use poetry to install dependencies and run the streamlit app, or use Docker to build and run the container. Finally, access the Streamlit app through your browser and interact with the chat interface, utilizing provided tools through MCP.
Key features of Llama MCP Streamlit
Real-time tool execution via MCP
LLM-powered chat interface
Streamlit UI with interactive chat elements
Support for multiple LLM backends (NVIDIA NIM & Ollama)
Use cases of Llama MCP Streamlit
Real-time access to file systems
Automated tasks using tools and LLMs
Building custom internal AI assistant
Interact with specialized local LLMs (Ollama)
FAQ from Llama MCP Streamlit
What is MCP?
What is MCP?
MCP stands for Model Control Protocol, used for executing real-time external tools through the LLM.
Which LLM backends are supported?
Which LLM backends are supported?
The application supports NVIDIA NIM and Ollama.
How do I configure which MCP server to use?
How do I configure which MCP server to use?
Modify the utils/mcp_server.py file to specify the MCP server using either NPX or Docker.
Can I use this for commercial purposes?
Can I use this for commercial purposes?
The project is licensed under the MIT License, which generally permits commercial use. However, ensure you comply with any requirements set by underlying dependencies like Nvidia NIM or Ollama.
What are the advantages of using Docker with this project?
What are the advantages of using Docker with this project?
Docker provides a consistent and isolated environment for running the application, simplifying deployment and ensuring consistent behavior across different systems.