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Installation and Setup Guide MCP Server

@ricardoborges

Repository

Basic information

Category

AI & Agents

Transports

stdio

Author

ricardoborges

About Installation and Setup Guide MCP Server

LLM chat app for integration tests using llama-stack-client, llama, Ollama, MCP, Tools

#mcp#chat-application#gradio#llm#ollama#llama-stack#mcp-server#mcp-client

Installation and Setup Guide MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "chatlab": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

Installation and Setup Guide MCP Server tools

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Overview

What is Installation and Setup Guide?

This document provides step-by-step instructions for setting up the development environment and running the ChatLab application. It uses Ollama or Together.ai for LLM inference and LLama-Stack to manage the inference environment.

How to use Installation and Setup Guide?

Follow the installation steps sequentially: install Ollama or obtain a Together.ai API key, set up LLama-Stack using uv and a virtual environment, clone the repository, install dependencies via uv pip install -r myproject.toml, set environment variables (TAVILY_SEARCH_API_KEY, TOGETHER_API_KEY or DEFAULT_STACK="Ollama"), then run gradio main.py.

Key features of Installation and Setup Guide

  • Step-by-step guide for Ollama and Together.ai setup
  • Uses LLama-Stack to manage inference environments
  • Gradio-based web application interface
  • Supports local and cloud inference models

Use cases of Installation and Setup Guide

  • Setting up a local LLM environment with Ollama
  • Configuring cloud inference via Together.ai API
  • Running the ChatLab application with Gradio UI

Frequently asked questions

1

What prerequisites are needed?

Ollama must be installed (unless using Together.ai API) and the virtual environment must be activated correctly.

2

What dependencies are required?

Ollama or Together.ai API key, LLama-Stack, Python packages as listed in `myproject.toml`, and a Tavily search API key for search functionality.

3

Where does data live?

API keys are stored in a `.env` file (TAVILY_SEARCH_API_KEY, TOGETHER_API_KEY). Model inference runs either locally (Ollama) or on Together.ai servers.

4

What are known limitations?

The guide does not mention any specific limits; troubleshooting covers checking services and dependencies.

5

What transports or authentication are used?

No transports are mentioned. Authentication uses API keys for Together.ai and Tavily; local Ollama requires no external auth.

Comments

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