130 lines
3.6 KiB
Markdown
130 lines
3.6 KiB
Markdown
# LLM Functions
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This project allows you to enhance large language models (LLMs) with custom functions written in bash/js/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.
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## Prerequisites
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Make sure you have the following tools installed:
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- [argc](https://github.com/sigoden/argc): A bash command-line framewrok and command runner
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- [jq](https://github.com/jqlang/jq): A JSON processor
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## Getting Started with AIChat
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**1. Clone the repository:**
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```sh
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git clone https://github.com/sigoden/llm-functions
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```
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**2. Build function declarations file and bin dir:**
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First, create a `./functions.txt` file with each function name on a new line.
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Then, run `argc build` to build function declarations file `./functions.json` and bin dir `./bin/`.
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**3. Configure your AIChat:**
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Symlink this repo directory to aichat **functions_dir**:
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```sh
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ln -s "$(pwd)" "$(aichat --info | grep functions_dir | awk '{print $2}')"
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# OR
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argc install
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```
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Don't forget to add the following config to your AIChat `config.yaml` file:
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```yaml
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function_calling: true
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```
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AIChat will automatically load `functions.json` and execute functions located in the `./bin` directory based on your prompts.
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**4. Start using your functions:**
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Now you can interact with your LLM using natural language prompts that trigger your defined functions.
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## Function Types
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### Retrieve Type
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The function returns JSON data to LLM for further processing.
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AIChat does not ask permission to run the function or print the output.
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### Execute Type
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The function does not have to return JSON data.
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The function can perform dangerous tasks like creating/deleting files, changing network adapter, and setting a scheduled task...
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AIChat will ask permission before running the function.
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**AIChat categorizes functions starting with `may_` as `execute type` and all others as `retrieve type`.**
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## Writing Your Own Functions
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You can write functions in bash/javascript/python.
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`llm-functions` will automatic generate function declarations from comments. Refer to `demo_tool.{sh,js,py}` for examples of how to use comments for autogeneration of declarations.
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### Bash
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Create a new bashscript in the [./tools/](./tools/) directory (.e.g. `may_execute_command.sh`).
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```sh
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#!/usr/bin/env bash
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set -e
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# @describe Runs a shell command.
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# @option --command! The command to execute.
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main() {
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eval "$argc_command"
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}
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eval "$(argc --argc-eval "$0" "$@")"
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```
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### Javascript
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Create a new javascript in the [./tools/](./tools/) directory (.e.g. `may_execute_js_code.js`).
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```js
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/**
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* Runs the javascript code in node.js.
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* @typedef {Object} Args
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* @property {string} code - Javascript code to execute, such as `console.log("hello world")`
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* @param {Args} args
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*/
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exports.main = function main({ code }) {
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eval(code);
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}
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```
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### Python
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Create a new python script in the [./tools/](./tools/) directory (e.g., `may_execute_py_code.py`).
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```py
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def main(code: str):
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"""Runs the python code.
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Args:
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code: Python code to execute, such as `print("hello world")`
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"""
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exec(code)
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```
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## License
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The project is under the MIT License, Refer to the [LICENSE](https://github.com/sigoden/llm-functions/blob/main/LICENSE) file for detailed information. |