tell-me/README.md

Tell-Me

A CLI application that provides AI-powered search and information retrieval using local LLM models. Similar to Perplexity, but running locally in your terminal.

Features

• 🔍 Web Search: Powered by SearXNG for comprehensive internet searches
• 📄 URL Fetching: Automatically fetches and converts web pages to clean Markdown
• 🤖 Local LLM Support: Works with any OpenAI-compatible API (Ollama, LM Studio, etc.)
• 🔌 MCP Support: Extend capabilities with Model Context Protocol servers
• <EFBFBD> Simple CLI: Clean terminal interface for easy interaction
• ⚙️ Configurable: Easy YAML-based configuration with customizable prompts
• 🔒 Privacy-Focused: All processing happens locally

Prerequisites

Before using Tell-Me, you need:

1. Go 1.21 or higher - Download Go
2. A running SearXNG instance - SearXNG Setup Guide
   • Quick Docker setup: docker run -d -p 8080:8080 searxng/searxng
3. An OpenAI-compatible LLM API such as:
   • Ollama (recommended for local use)
   • LM Studio
   • Any other OpenAI-compatible endpoint

Installation

1. Clone the repository

git clone https://git.netra.pivpav.com/public/tell-me
cd tell-me

2. Download dependencies

go mod download

3. Build the application

go build -o tell-me

4. Set up configuration

Create the config directory and copy the example configuration:

mkdir -p ~/.config
cp tell-me.yaml.example ~/.config/tell-me.yaml

5. Edit your configuration

Open ~/.config/tell-me.yaml in your favorite editor and configure:

# Your LLM API endpoint (e.g., Ollama, LM Studio)
api_url: http://localhost:11434/v1

# Model name to use
model: llama3.2

# Context window size
context_size: 16000

# API key (leave empty if not required)
api_key: ""

# Your SearXNG instance URL
searxng_url: http://localhost:8080

# System prompt (customize the AI's behavior)
prompt: |
  You are a helpful AI research assistant...
  (see tell-me.yaml.example for full prompt)

# MCP Server Configuration (optional)
mcp_servers: {}
  # Add MCP servers to extend functionality
  # See MCP section below for examples

Example configurations:

For Ollama:

api_url: http://localhost:11434/v1
model: llama3.2
context_size: 16000
api_key: ""
searxng_url: http://localhost:8080

For LM Studio:

api_url: http://localhost:1234/v1
model: your-model-name
context_size: 16000
api_key: ""
searxng_url: http://localhost:8080

Usage

Simply run the application:

./tell-me

You'll see a welcome screen, then you can start asking questions:

╔════════════════════════════════════════════════════════════════╗
║                         Tell-Me CLI                            ║
║          AI-powered search with local LLM support              ║
╚════════════════════════════════════════════════════════════════╝

Using model: llama3.2
SearXNG: http://localhost:8080

Type your questions below. Type 'exit' or 'quit' to exit.
────────────────────────────────────────────────────────────────

❯ What are the latest developments in AI?

The AI will:

1. Automatically search the web for current information
2. Fetch relevant URLs if needed
3. Synthesize the information into a comprehensive answer
4. Cite sources with URLs

Type exit or quit to exit the application, or press Ctrl-C.

MCP (Model Context Protocol) Support

Tell-Me supports the Model Context Protocol, allowing you to extend the AI assistant's capabilities with additional tools from MCP servers.

Supported MCP Servers

Tell-Me supports stdio-based MCP servers (local command execution). Remote SSE-based servers are not supported for security reasons.

Configuration

Add MCP servers to your ~/.config/tell-me.yaml:

mcp_servers:
  # Example: Filesystem access
  filesystem:
    command: /usr/local/bin/mcp-server-filesystem
    args:
      - --root
      - /path/to/allowed/directory
    env:
      LOG_LEVEL: info
  
  # Example: Weather information
  weather:
    command: /usr/local/bin/mcp-server-weather
    args: []
    env:
      API_KEY: your-weather-api-key

How It Works

1. User asks a question - You type your query in the terminal
2. AI searches first - The system prompt enforces web search before answering
3. Information gathering - Uses SearXNG to find relevant sources
4. Content fetching - Optionally fetches full content from specific URLs
5. Answer synthesis - AI combines information and provides a comprehensive answer with citations

Project Structure

tell-me/
├── main.go               # Main application entry point
├── config/
│   └── config.go         # Configuration loading from YAML file
├── llm/
│   └── client.go         # OpenAI-compatible API client with tool calling
├── mcp/
│   └── manager.go        # MCP server connection and tool management
├── tools/
│   ├── search.go         # SearXNG web search implementation
│   └── fetch.go          # URL fetching and HTML-to-Markdown conversion
├── go.mod                # Go module dependencies
├── tell-me.yaml.example  # Example YAML configuration file
└── README.md             # This file

License

MIT