# 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
- � **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](https://go.dev/dl/)
2. **A running SearXNG instance** - [SearXNG Setup Guide](https://docs.searxng.org/admin/installation.html)
   - Quick Docker setup: `docker run -d -p 8080:8080 searxng/searxng`
3. **An OpenAI-compatible LLM API** such as:
   - [Ollama](https://ollama.ai/) (recommended for local use)
   - [LM Studio](https://lmstudio.ai/)
   - Any other OpenAI-compatible endpoint

## Installation

### 1. Clone the repository

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

### 2. Download dependencies

```bash
go mod download
```

### 3. Build the application

```bash
go build -o tell-me
```

### 4. Set up configuration

Create the config directory and copy the example configuration:

```bash
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:

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

**For LM Studio:**
```yaml
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:

```bash
./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](https://modelcontextprotocol.io/), 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`:

```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