Llama Integration Server

This repository contains a Model Context Protocol (MCP) server implementation that integrates with a locally running Llama model. The MCP server provides a standardized interface for context retrieval, enhancing AI applications with relevant information from a local LLM.

Overview

The project consists of two main components:

1. MCP Server - A FastAPI-based server that implements the Model Context Protocol and forwards queries to a local Llama model
2. Python Client - A sample client application that demonstrates how to interact with the MCP server

Prerequisites

• Python 3.7 or higher
• A running Llama model server (e.g., Ollama) at http://localhost:11434/
• Git installed on your machine
• GitHub account

Installation

Clone the Repository

git clone https://github.com/EXPESRaza/mcp-llama-integration.git
cd mcp-llama-integration

Install Dependencies

pip install -r requirements.txt

File Structure

mcp-llama-integration/
├── llama_mcp_server.py      # MCP server with Llama integration
├── llama_client_app.py      # Sample client application
└── README.md                # Project documentation

Setting Up the Llama Model

1. If you haven't already, install Ollama
2. Pull the Llama model:

   ollama pull llama3.2
   

3. Verify the model is running:

   curl http://localhost:11434/api/tags
   ``` browser
   http://localhost:11434
   http://localhost:11434/api/tags
   

   

Running the MCP Server

1. Start the server:

   python llama_mcp_server.py
   

2. The server will start running on http://localhost:8000

3. You can verify the server is running by checking the health endpoint:

   curl http://localhost:8000/health
   

   

   

Using the Client Application

1. In a separate terminal, start the client application:

   python llama_client_app.py
   

2. The application will prompt you for input

3. Type your queries and receive responses from the Llama model

4. Type 'exit' to quit the application

   

API Documentation

MCP Server Endpoints

POST /context

Request a context for a given query.

Request Body:

{
  "query_text": "Your query here",
  "user_id": "optional-user-id",
  "session_id": "optional-session-id",
  "additional_context": {}
}

Response:

{
  "context_elements": [
    {
      "content": "Response from Llama model",
      "source": "llama_model",
      "relevance_score": 0.9
    }
  ],
  "metadata": {
    "processing_time_ms": 150,
    "model": "llama3",
    "query": "Your query here"
  }
}

GET /health

Check the health status of the MCP server and its connection to the Llama model.

Response:

{
  "status": "healthy",
  "llama_status": "connected"
}

Customization

Changing the Llama Model

If you want to use a different Llama model, modify the model parameter in the query_llama function in llama_mcp_server.py:

payload = {
    "model": "your-model-name",  # Change this to your model name
    "prompt": text,
    "stream": False
}

Modifying the Prompt Template

To change how queries are formatted before sending to Llama, update the prompt template in the get_context function:

prompt = f"""Please provide relevant information for the following query:
{request.query_text}

Respond with factual, helpful information."""

Troubleshooting

Common Issues

1. Connection Refused Error

   • Make sure the Llama model is running at http://localhost:11434/
   • Verify Ollama is properly installed and running

2. Model Not Found Error

   • Ensure you've pulled the correct model with Ollama
   • Check available models with ollama list

3. Slow Responses

   • Llama model inference can be resource-intensive
   • Consider using a smaller model if performance is an issue

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.