What is Cleverchatty Cli

cleverchatty-cli is a command line interface (CLI) application that utilizes the CleverChatty package to function as an AI chat tool, enabling communication with various large language models (LLMs) and supporting external tool calls through the Model Context Protocol (MCP).

Use cases

Use cases for cleverchatty-cli include developing AI chatbots, conducting natural language processing tasks, integrating AI capabilities into existing applications, and experimenting with different LLMs for research purposes.

How to use

To use cleverchatty-cli, first install a model using ollama, such as ‘qwen2.5:3b’. You can run the application by executing ‘go run github.com/gelembjuk/cleverchatty-cli@latest -m ollama:qwen2.5:3b’ or by building it from the source with ‘go build’ and then running ‘./cleverchatty-cli -m ollama:qwen2.5:3b’. A configuration file can be created to specify MCP servers and other options.

Key features

Key features of cleverchatty-cli include the ability to communicate with multiple LLMs, support for external tool calls via MCP, and customizable configurations through command line arguments or a config file.

Where to use

cleverchatty-cli can be used in various fields such as software development, AI research, and any domain that requires interaction with AI models or integration of AI functionalities into applications.

CleverChatty CLi

The CLI application based on the CleverChatty package.

This application is the comand line AI chat tool. It allows to comunicate with veriety of LLMs and it has support of external tools calls using the MCP(Model Context Protocol).

Quick run

Install some model with ollama, for example, qwen2.5:3b:

ollama list
NAME                   ID              SIZE      MODIFIED    
.... 
qwen2.5:3b             357c53fb659c    1.9 GB    3 weeks ago 
....

And chat with this model.

go run github.com/gelembjuk/cleverchatty-cli@latest -m ollama:qwen2.5:3b

Or, when you clone the repo, you can run it with:

go build
./cleverchatty-cli -m ollama:qwen2.5:3b

However, this tool is really useful when you use some MCP servers. It is possible by creating a config file and adding the list of MCP servers to it.

Config

The config is not required. Most options from the config can be overriden with the command line arguments.

All supported models providers are:

• ollama
• google
• anthropic
• openai

For openai, anthropic, google it is needed to have additional arguments - API key and API endpoint in some cases. See the config file example below. It is possible to provide the additional arguments with the config file or with the command line options.

The config should be stored in config.json file.

{
  "log_file_path": "",
  "model": "ollama:qwen2.5:3b",
  "system_instruction": "Talk to a user like you are Elvis Presley.",
  "mcpServers": {
    "File_Storage_Server": {
      "url": "http://localhost:8000/sse",
      "headers": [
        "Authorization: Basic Q0F************MQ=="
      ]
    },
    "Some_STDIO_Server": {
      "command": "uv",
      "args": [
        "-m",
        "uvicorn",
        "example:app",
        "--host"
      ]
    }
  },
  "anthropic": {
    "apikey": "sk-**************AA",
    "base_url": "https://api.anthropic.com/v1",
    "default_model": "claude-2"
  },
  "openai": {
    "apikey": "sk-********0A",
    "base_url": "https://api.openai.com/v1",
    "default_model": "gpt-3.5-turbo"
  },
  "google": {
    "apikey": "AI***************z4",
    "default_model": "google-bert"
  }
}

The section mcpServers contains all MCP servers config. It supports both STDIO and SSE servers.

Usig the memory server

This tools allows to use long-term AI memory using a MCP server of specific interface.

"mcpServers": {
        "Memory_Server": {
            "url": "http://localhost:8001/sse",
            "headers": [
            ],
            "interface": "memory"
        },
        .... other MCP servers
    },

The value “interface”: “memory” indicates that this server is a memory server. It will be used to store the chat history and the AI will be able to use it in the future conversations.

The interface requires a MCP server to have two tools - remember and recall.

Example:

@mcp.tool()
def remember(role: str, contents) -> str:
    """Remembers new data in the memory
    
    Args:
        role (str): The role of the data, e.g. "user", "assistant"
        contents (str): The contents to remember, usually the text of the message
    """

    Memory(config).remember(role, contents)

    return "ok"

@mcp.tool()
def recall(query: str = "") -> str:
    """Recall the memory"""
    
    r = Memory(config).recall(query)

    if not r:
        return "none"
    
    return r

remember tool accepts two arguments:

• role: The role of the data, e.g. “user”, “assistant”
• contents: The contents to remember, usually the text of the message

recall tool accepts one argument:

• query: The query to search for the data in the memory. If empty, it is expected to return some common memories.

Using the Retrieval-Augmented Generation server

This tools allows to use RAG using a MCP server of specific interface.

"mcpServers": {
        "RAG_Server": {
            "url": "http://localhost:8002/sse",
            "headers": [
            ],
            "interface": "rag"
        },
        .... other MCP servers
    },

The value “interface”: “rag” indicates that this server is a RAG server. It will be used to store the chat history and the AI will be able to use it in the future conversations.

The expected interface is the one tool named knowledge_search with two arguments: query and num.

Example:

func createServer() *server.MCPServer {
	// Create MCP server
	s := server.NewMCPServer(
		"Retrieval-Augmented Generation Server. Adding the company data to the AI chat.",
		"1.0.0",
	)

	execTool := mcp.NewTool("knowledge_search",
		mcp.WithDescription("Search for documents and information over the company data storage"),
		mcp.WithString("query",
			mcp.Required(),
			mcp.Description("The query to search for documents and information"),
		),
		mcp.WithNumber("num",
			mcp.Description("The number of results to return"),
		),
	)

	s.AddTool(execTool, cmdKnowledgeSearch)
	return s
}

To run the chat using this config stored in teh config.json, you can use the command:

 go run github.com/gelembjuk/cleverchatty-cli@latest --config config.json

Installation

Prerequisites

• Go 1.20 or later
• Ollama installed and running

Install

go install github.com/gelembjuk/cleverchatty-cli@latest