LLM Functions

This project allows you to enhance large language models (LLMs) with custom functions written in bash/js/python/ruby. Imagine your LLM being able to execute system commands, access web APIs, or perform other complex tasks – all triggered by simple, natural language prompts.

Prerequisites

Make sure you have the following tools installed:

• argc: A bash command-line framewrok and command runner
• jq: A JSON processor
• curl: A command-line tool for transferring data with URLs

Getting Started with AIChat

1. Clone the repository:

git clone https://github.com/sigoden/llm-functions

2. Build function declarations file and bin dir:

First, create a ./functions.txt file with each function name on a new line.

Then, run argc build to build function declarations file ./functions.json and bin dir ./bin/.

3. Configure your AIChat:

Symlink this repo directory to aichat functions_dir:

ln -s "$(pwd)" "$(aichat --info | grep functions_dir | awk '{print $2}')"
# OR
argc install

Don't forget to add the following config to your AIChat config.yaml file:

function_calling: true

AIChat will automatically load functions.json and execute functions located in the ./bin directory based on your prompts.

4. Start using your functions:

Now you can interact with your LLM using natural language prompts that trigger your defined functions.

Function Types

Retrieve Type

The function returns JSON data to LLM for further processing.

AIChat does not ask permission to run the function or print the output.

Execute Type

The function does not have to return JSON data.

The function can perform dangerous tasks like creating/deleting files, changing network adapter, and setting a scheduled task...

AIChat will ask permission before running the function.

AIChat categorizes functions starting with may_ as execute type and all others as retrieve type.

Writing Your Own Functions

The project supports write functions in bash/js/python.

Bash

Create a new bashscript (.e.g. may_execute_command.sh) in the ./sh directory.

#!/usr/bin/env bash
set -e

# @describe Executes a shell command.
# @option --command~ Command to execute, such as `ls -la`

main() {
    eval $argc_shell_command
}

eval "$(argc --argc-eval "$0" "$@")"

llm-functions will automatic generate function declaration.json from comment tags.

The relationship between comment tags and parameters in function declarations is as follows:

# @flag --boolean                   Parameter `{"type": "boolean"}`
# @option --string                  Parameter `{"type": "string"}`
# @option --string-enum[foo|bar]    Parameter `{"type": "string", "enum": ["foo", "bar"]}`
# @option --integer <INT>           Parameter `{"type": "integer"}`
# @option --number <NUM>            Parameter `{"type": "number"}`
# @option --array* <VALUE>          Parameter `{"type": "array", "items": {"type":"string"}}`
# @option --scalar-required!        Use `!` to mark a scalar parameter as required.
# @option --array-required+         Use `+` to mark a array parameter as required

Javascript

Create a new javascript (.e.g. may_execute_command.js) in the ./js directory.

exports.declarate = function declarate() {
  return {
    "name": "may_execute_js_code",
    "description": "Runs the javascript code in node.js.",
    "parameters": {
      "type": "object",
      "properties": {
        "code": {
          "type": "string",
          "description": "Javascript code to execute, such as `console.log(\"hello world\")`"
        }
      },
      "required": [
        "code"
      ]
    }
  }
}

exports.execute = function execute(data) {
  eval(data.code)
}

Python

Create a new python script in the ./py directory (e.g., may_execute_py_code.py).

def declarate():
  return {
    "name": "may_execute_py_code",
    "description": "Runs the python code.",
    "parameters": {
      "type": "object",
      "properties": {
        "code": {
          "type": "string",
          "description": "python code to execute, such as `print(\"hello world\")`"
        }
      },
      "required": [
        "code"
      ]
    }
  }


def execute(data):
  exec(data["code"])

Ruby

Create a new ruby script in the ./rb directory (e.g., may_execute_rb_code.rb).

def declarate
  {
    "name": "may_execute_rb_code",
    "description": "Runs the ruby code.",
    "parameters": {
      "type": "object",
      "properties": {
        "code": {
          "type": "string",
          "description": "Ruby code to execute, such as `puts \"hello world\"`"
        }
      },
      "required": [
        "code"
      ]
    }
  }
end

def execute(data)
  eval(data["code"])
end

License

The project is under the MIT License, Refer to the LICENSE file for detailed information.