metadata

title: ControlLLM
emoji: 🚀
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 4.9.1
app_file: app.py
pinned: false
license: apache-2.0

ControlLLM

ControlLLM: Augmenting Large Language Models with Tools by Searching on Graphs [Paper]

We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks. Despite the remarkable performance of LLMs, they still struggle with tool invocation due to ambiguous user prompts, inaccurate tool selection and parameterization, and inefficient tool scheduling. To overcome these challenges, our framework comprises three key components: (1) a $\textit{task decomposer}$ that breaks down a complex task into clear subtasks with well-defined inputs and outputs; (2) a $\textit{Thoughts-on-Graph (ToG) paradigm}$ that searches the optimal solution path on a pre-built tool graph, which specifies the parameter and dependency relations among different tools; and (3) an $\textit{execution engine with a rich toolbox}$ that interprets the solution path and runs the tools efficiently on different computational devices. We evaluate our framework on diverse tasks involving image, audio, and video processing, demonstrating its superior accuracy, efficiency, and versatility compared to existing methods.

🤖 Video Demo

https://github.com/OpenGVLab/ControlLLM/assets/13723743/cf72861e-0e7b-4c15-89ee-7fa1d838d00f

🏠 System Overview

🎁 Major Features

Image Perception
Image Editing
Image Generation
Video Perception
Video Editing
Video Generation
Audio Perception
Audio Generation
Multi-Solution
Pointing Inputs
Resource Type Awareness

🗓️ Schedule

Launch online demo

🛠️Installation

Basic requirements

Linux
Python 3.10+
PyTorch 2.0+
CUDA 11.8+

Clone project

Execute the following command in the root directory:

git clone https://github.com/OpenGVLab/ControlLLM.git

Install dependencies

Setup environment:


conda create -n cllm python=3.10

conda activate cllm

conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia

Install LLaVA:

pip install git+https://github.com/haotian-liu/LLaVA.git

Then install other dependencies:

cd controlllm

pip install -r requirements.txt

👨‍🏫 Get Started

Launch tool services

Please put your personal OpenAI Key and Weather Key into the corresponding environment variables.


cd ./controlllm
# openai key
export OPENAI_API_KEY="..."
# openai base
export OPENAI_BASE_URL="..."
# weather api key
export WEATHER_API_KEY="..."

python -m cllm.services.launch --port 10011 --host 0.0.0.0

Launch ToG service

cd ./controlllm

export TOG_SERVICES_PORT=10011
export OPENAI_BASE_URL="..."
export OPENAI_API_KEY="..."
python -m cllm.services.tog.launch --port 10012 --host 0.0.0.0

Launch gradio demo

Use openssl to generate the certificate:

mkdir certificate

openssl req -x509 -newkey rsa:4096 -keyout certificate/key.pem -out certificate/cert.pem -sha256 -days 365 -nodes

Launch gradio demo:

cd ./controlllm

export TOG_PORT=10012
export TOG_SERVICES_PORT=10011
export RESOURCE_ROOT="./client_resources"
export GRADIO_TEMP_DIR="$HOME/.tmp"
export OPENAI_BASE_URL="..."
export OPENAI_API_KEY="..."

python -m cllm.app.gradio --controller "cllm.agents.tog.Controller" --server_port 10024

Tools as Services

Take image generation as an example, we first launch the service.


python -m cllm.services.image_generation.launch --port 10011 --host 0.0.0.0

Then, we call the services via python api.

from cllm.services.image_generation.api import *
setup(port=10011)
text2image('A horse')

😬 Launch all in one endpoint

python -m cllm.services.launch --port 10011 --host 0.0.0.0

🛠️ Support Tools

See Tools

🎫 License

This project is released under the Apache 2.0 license.

🖊️ Citation

If you find this project useful in your research, please cite our paper:

@article{2023controlllm,
  title={ControlLLM: Augment Language Models with Tools by Searching on Graphs},
  author={Liu, Zhaoyang and Lai, Zeqiang and Gao Zhangwei and Cui, Erfei and Li, Zhiheng and Zhu, Xizhou and Lu, Lewei and Chen, Qifeng and Qiao, Yu and Dai, Jifeng and Wang Wenhai},
  journal={arXiv preprint arXiv:2305.10601},
  year={2023}
}

🤝 Acknowledgement

Thanks to the open source of the following projects: Hugging Face LangChain SAM Stable Diffusion ControlNet InstructPix2Pix EasyOCR ImageBind PixArt-alpha LLaVA Modelscope AudioCraft Whisper Llama 2 LLaMA

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