149 lines
3.9 KiB
Markdown
149 lines
3.9 KiB
Markdown
# LLM Functions
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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.
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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 tools and agents:**
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- Create a `./tools.txt` file with each tool filename on a new line.
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```
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get_current_weather.sh
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execute_command.sh
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#execute_py_code.py
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```
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- Create a `./agents.txt` file with each agent name on a new line.
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```
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todo-sh
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#todo-js
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#todo-py
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```
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- Run `argc build` to build functions declarations files (`functions.json`) and binaries (`./bin`) for tools and agents.
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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 -w functions_dir | awk '{print $2}')"
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# OR
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argc install
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```
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AIChat will automatically load `functions.json` and execute commands located in the `./bin` directory based on your prompts.
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**4. Start using your functions:**
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Now you can interact with your LLM using natural language prompts that trigger your defined functions.
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## Writing Your Own Tools
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Writing tools is super easy, you only need to write functions with comments.
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`llm-functions` will automatically generate binaries, function declarations, and so on
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Refer to `./tools/demo_tool.{sh,js,py}` for examples of how to use comments for autogeneration of declarations.
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### Bash
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Create a new bashscript in the [./tools/](./tools/) directory (.e.g. `may_execute_command.sh`).
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```sh
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#!/usr/bin/env bash
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set -e
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# @describe Runs a shell command.
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# @option --command! The command to execute.
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main() {
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eval "$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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## Writing Agents
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Agent = Prompt + Tools (Function Callings) + Knowndge (RAG). It's also known as OpenAI's GPTs.
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The agent has the following folder structure:
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```
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└── agents
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└── myagent
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├── functions.json # Function declarations file (Auto-generated)
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├── index.yaml # Agent definition file
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├── tools.txt # Reuse tools
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└── tools.{sh,js,py} # Agent tools script
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```
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The agent definition file (`index.yaml`) defines crucial aspects of your agent:
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```yaml
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name: TestAgent
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description: This is test agent
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version: v0.1.0
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instructions: You are a test ai agent to ...
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conversation_starters:
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- What can you do?
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documents:
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- files/doc.pdf
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```
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Refer to `./agents/todo-{sh,js,py}` for examples of how to implement a agent.
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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. |