148 lines
4.1 KiB
Markdown
148 lines
4.1 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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**Tool Showcase**
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**Agent showcase**
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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](https://github.com/sigoden/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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**I. 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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search_tavily.sh
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```
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**II. Create a `./agents.txt` file with each agent name on a new line.**
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```
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coder
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todo
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```
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**III. Run `argc build` to build tools and agents.**
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### 3. Install to 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 $2}')"
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# OR
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argc install
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```
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### 4. Start using the functions:
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Done! You can experience the magic of `llm-functions` in AIChat.
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## Writing Your Own Tools
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Building tools for our platform is remarkably straightforward. You can leverage your existing programming knowledge, as tools are essentially just functions written in your preferred language.
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LLM Functions automatically generates the JSON declarations for the tools based on **comments**. Refer to `./tools/demo_tool.{sh,js,py}` for examples of how to use comments for autogeneration of declarations.
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### Bash
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Create a new bashscript in the [./tools/](./tools/) directory (.e.g. `may_execute_command.sh`).
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```sh
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#!/usr/bin/env bash
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set -e
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# @describe Runs a shell command.
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# @option --command! The command to execute.
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main() {
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eval "$argc_command" >> "$LLM_OUTPUT"
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}
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eval "$(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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return 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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return exec(code)
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```
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## Writing Your Own 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 JSON declarations (Auto-generated)
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├── index.yaml # Agent definition
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├── tools.txt # Shared tools from ./tools
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└── tools.{sh,js,py} # Agent tools
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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: 0.1.0
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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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- local-file.txt
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- local-dir/
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- https://example.com/remote-file.txt
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```
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Refer to [./agents/demo](https://github.com/sigoden/llm-functions/tree/main/agents/demo) 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. |