189 lines
5.3 KiB
Markdown
189 lines
5.3 KiB
Markdown
# LLM Functions
|
||
|
||
This project empowers you to effortlessly build powerful LLM tools and agents using familiar languages like Bash, JavaScript, and Python.
|
||
|
||
Forget complex integrations, **harness the power of [function calling](https://platform.openai.com/docs/guides/function-calling)** to connect your LLMs directly to custom code and unlock a world of possibilities. Execute system commands, process data, interact with APIs – the only limit is your imagination.
|
||
|
||
**Tools Showcase**
|
||
|
||
|
||
**Agents showcase**
|
||
|
||
|
||
## 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](https://github.com/sigoden/aichat)
|
||
|
||
**Currently, AIChat is the only CLI tool that supports `llm-functions`. We look forward to more tools supporting `llm-functions`.**
|
||
|
||
### 1. Clone the repository
|
||
|
||
```sh
|
||
git clone https://github.com/sigoden/llm-functions
|
||
```
|
||
|
||
### 2. Build tools and agents
|
||
|
||
#### I. Create a `./tools.txt` file with each tool filename on a new line.
|
||
|
||
```
|
||
get_current_weather.sh
|
||
execute_command.sh
|
||
#execute_py_code.py
|
||
```
|
||
|
||
<details>
|
||
<summary>Where is the web_search tool?</summary>
|
||
<br>
|
||
|
||
The `web_search` tool itself doesn't exist directly, Instead, you can choose from a variety of web search tools.
|
||
|
||
To use one as the `web_search` tool, follow these steps:
|
||
|
||
1. **Choose a Tool:** Available tools include:
|
||
* `web_search_cohere.sh`
|
||
* `web_search_perplexity.sh`
|
||
* `web_search_tavily.sh`
|
||
* `web_search_vertexai.sh`
|
||
|
||
2. **Link Your Choice:** Use the `argc` command to link your chosen tool as `web_search`. For example, to use `web_search_perplexity.sh`:
|
||
|
||
```sh
|
||
$ argc link-web-search web_search_perplexity.sh
|
||
```
|
||
|
||
This command creates a symbolic link, making `web_search.sh` point to your selected `web_search_perplexity.sh` tool.
|
||
|
||
Now there is a `web_search.sh` ready to be added to your `./tools.txt`.
|
||
|
||
</details>
|
||
|
||
#### II. Create a `./agents.txt` file with each agent name on a new line.
|
||
|
||
```
|
||
coder
|
||
todo
|
||
```
|
||
|
||
#### III. Build `bin` and `functions.json`
|
||
|
||
```sh
|
||
argc build
|
||
```
|
||
|
||
### 3. Install to AIChat
|
||
|
||
Symlink this repo directory to AIChat's **functions_dir**:
|
||
|
||
```sh
|
||
ln -s "$(pwd)" "$(aichat --info | grep -w functions_dir | awk '{print $2}')"
|
||
# OR
|
||
argc install
|
||
```
|
||
|
||
### 4. Start using the functions
|
||
|
||
Done! Now you can use the tools and agents with AIChat.
|
||
|
||
```sh
|
||
aichat --role %functions% what is the weather in Paris?
|
||
aichat --agent todo list all my todos
|
||
```
|
||
|
||
## Writing Your Own Tools
|
||
|
||
Building tools for our platform is remarkably straightforward. You can leverage your existing programming knowledge, as tools are essentially just functions written in your preferred language.
|
||
|
||
LLM Functions automatically generates the JSON declarations for the tools based on **comments**. Refer to `./tools/demo_tool.{sh,js,py}` for examples of how to use comments for autogeneration of declarations.
|
||
|
||
### Bash
|
||
|
||
Create a new bashscript in the [./tools/](./tools/) directory (.e.g. `execute_command.sh`).
|
||
|
||
```sh
|
||
#!/usr/bin/env bash
|
||
set -e
|
||
|
||
# @describe Execute the shell command.
|
||
# @option --command! The command to execute.
|
||
|
||
main() {
|
||
eval "$argc_command" >> "$LLM_OUTPUT"
|
||
}
|
||
|
||
eval "$(argc --argc-eval "$0" "$@")"
|
||
```
|
||
|
||
### Javascript
|
||
|
||
Create a new javascript in the [./tools/](./tools/) directory (.e.g. `execute_js_code.js`).
|
||
|
||
```js
|
||
/**
|
||
* Execute 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 }) {
|
||
return eval(code);
|
||
}
|
||
|
||
```
|
||
|
||
### Python
|
||
|
||
Create a new python script in the [./tools/](./tools/) directory (e.g. `execute_py_code.py`).
|
||
|
||
```py
|
||
def main(code: str):
|
||
"""Execute the python code.
|
||
Args:
|
||
code: Python code to execute, such as `print("hello world")`
|
||
"""
|
||
return exec(code)
|
||
|
||
```
|
||
|
||
## Writing Your Own Agents
|
||
|
||
Agent = Prompt + Tools (Function Calling) + Documents (RAG), which is equivalent to OpenAI's GPTs.
|
||
|
||
The agent has the following folder structure:
|
||
```
|
||
└── agents
|
||
└── myagent
|
||
├── functions.json # JSON declarations for functions (Auto-generated)
|
||
├── index.yaml # Agent definition
|
||
├── tools.txt # Shared tools
|
||
└── tools.{sh,js,py} # Agent tools
|
||
```
|
||
|
||
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?
|
||
variables:
|
||
- name: foo
|
||
description: This is a foo
|
||
documents:
|
||
- local-file.txt
|
||
- local-dir/
|
||
- https://example.com/remote-file.txt
|
||
```
|
||
|
||
Refer to [./agents/demo](https://github.com/sigoden/llm-functions/tree/main/agents/demo) 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. |