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# LLM Functions
This project allows you to enhance large language models (LLMs) with custom tools and agents developed in bash/javascript/python. 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](https://github.com/sigoden/argc): A bash command-line framewrok and command runner
- [jq](https://github.com/jqlang/jq): A JSON processor
## Getting Started with AIChat
**1. Clone the repository:**
```sh
git clone https://github.com/sigoden/llm-functions
```
**2. Build tools and agents:**
- Create a `./tools.txt` file with each tool filename on a new line.
```
get_current_weather.sh
execute_command.sh
#execute_py_code.py
```
- Create a `./agents.txt` file with each agent name on a new line.
```
todo
#demo
```
- Run `argc build` to build functions declarations files (`functions.json`) and binaries (`./bin`) for tools and agents.
**3. Configure your AIChat:**
Symlink this repo directory to aichat **functions_dir**:
```sh
ln -s "$(pwd)" "$(aichat --info | grep -w functions_dir | awk '{print $2}')"
# OR
argc install
```
AIChat will automatically load `functions.json` and execute commands located in the `./bin` directory based on your prompts.
**4. Start using your functions:**
Now you can interact with your LLM using natural language prompts that trigger your defined functions.
## Writing Your Own Tools
Writing tools is super easy, you only need to write functions with comments.
`llm-functions` will automatically generate binaries, function declarations, and so on
Refer to `./tools/demo_tool.{sh,js,py}` for examples of how to use comments for autogeneration of declarations.
### Bash
Create a new bashscript in the [./tools/](./tools/) directory (.e.g. `may_execute_command.sh`).
```sh
#!/usr/bin/env bash
set -e
# @describe Runs a shell command.
# @option --command! The command to execute.
main() {
eval "$argc_command"
}
eval "$(argc --argc-eval "$0" "$@")"
```
### Javascript
Create a new javascript in the [./tools/](./tools/) directory (.e.g. `may_execute_js_code.js`).
```js
/**
* Runs the javascript code in node.js.
* @typedef {Object} Args
* @property {string} code - Javascript code to execute, such as `console.log("hello world")`
* @param {Args} args
*/
exports.main = function main({ code }) {
eval(code);
}
```
### Python
Create a new python script in the [./tools/](./tools/) directory (e.g., `may_execute_py_code.py`).
```py
def main(code: str):
"""Runs the python code.
Args:
code: Python code to execute, such as `print("hello world")`
"""
exec(code)
```
## Writing Agents
Agent = Prompt + Tools (Function Callings) + Knowndge (RAG). It's also known as OpenAI's GPTs.
The agent has the following folder structure:
```
└── agents
└── myagent
├── functions.json # Function declarations file (Auto-generated)
├── index.yaml # Agent definition file
├── tools.txt # Reuse tools
└── tools.{sh,js,py} # Agent tools script
```
The agent definition file (`index.yaml`) defines crucial aspects of your agent:
```yaml
name: TestAgent
description: This is test agent
version: 0.1.0
instructions: You are a test ai agent to ...
conversation_starters:
- What can you do?
documents:
- files/doc.pdf
```
Refer to `./agents/todo-{sh,js,py}` for examples of how to implement a agent.
## License
The project is under the MIT License, Refer to the [LICENSE](https://github.com/sigoden/llm-functions/blob/main/LICENSE) file for detailed information.
|
