What is Osmosis Mcp 4b Demo

Osmosis-MCP-4B-demo is an open-source SLM (Supervised Learning Model) designed for MCP (Model Control Protocol) applications, enabling advanced functionalities through integration with various APIs.

Use cases

Use cases for Osmosis-MCP-4B-demo include building chatbots that provide weather updates, applications that fetch and display search results, and systems that integrate location data for enhanced user experiences.

How to use

To use Osmosis-MCP-4B-demo, clone the repository, install the required dependencies in a virtual environment, set up your environment variables with necessary API keys, and serve a local model using a compatible model server.

Key features

Key features include integration with Brave Search for web searches, Google Maps for location-based services, and AccuWeather for weather information, along with tools for fetching content from URLs and providing current time information.

Where to use

Osmosis-MCP-4B-demo can be used in various fields including web development, data analysis, location services, and weather forecasting, making it suitable for applications requiring real-time data retrieval and processing.

Osmosis-MCP-4B

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Prerequisites

• Python 3.x
• uvx
• Access to a model server (e.g., running locally via vLLM/lm studio)
• API Keys for:
  • Brave Search
  • Google Maps
  • AccuWeather

Setup & Installation

0. Install LM studio here and use it to run the model from a http endpoint

   1. find and download model in discover
      
   2. select model to load and load an osmosis model
      
   3. start the web server with model loaded
      

1. Clone the repository (if applicable):

   git clone https://github.com/Gulp-AI/Osmosis-MCP-4B-demo
   cd Osmosis-MCP-4B-demo
   

2. Install dependencies:
   It’s recommended to use a virtual environment.

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -r requirements.txt
   

3. Set up Environment Variables:
   Create a .env file in the root of the project directory and add your API keys and model server configuration:

   BRAVE_API_KEY="your_brave_api_key"
   GOOGLE_MAPS_API_KEY="your_google_maps_api_key"
   ACCUWEATHER_API_KEY="your_accuweather_api_key"
   APP_STYLE="gui" # or "tui"
   

   if an api key is not provided, the tool will not be loaded.
   If APP_STYLE is omitted, the application defaults to GUI mode.
   The main.py script currently configures the model server URL directly. Ensure http://localhost:1234/v1 is the correct endpoint for your Qwen model server (this is what lm studio uses).

4. Serve local model:
   Use a tool like lm studio to provide a usable endpoint.

Environment Variables

The application uses the following environment variables (loaded from a .env file):

• BRAVE_API_KEY: Your API key for Brave Search.
• GOOGLE_MAPS_API_KEY: Your API key for Google Maps.
• ACCUWEATHER_API_KEY: Your API key for AccuWeather.
• APP_STYLE: ‘tui’ or ‘gui’

Available Tools (MCP Servers)

The agent is configured to use the following tools via MCP:

• Time: Provides current time information.
• Brave Search: Enables web search capabilities.
  • Requires: BRAVE_API_KEY
• Fetch: Fetches content from URLs.
• Google Maps: Provides location-based services.
  • Requires: GOOGLE_MAPS_API_KEY
• Weather: Provides weather forecasts.
  • Requires: ACCUWEATHER_API_KEY
• Code Interpreter: A built-in tool for executing Python code snippets.

These servers need to be running and accessible for the agent to utilize their respective functionalities. The main.py script provides the commands to start these MCP servers.

How to Run Graphical User Interface (GUI):

This mode launches a web-based interface for interacting with the agent. This is the default mode.
```bash
python main.py
```

The web UI will be accessible at the address provided by the `WebUI` component upon startup (on `http://localhost:7860`).