57 lines
1.8 KiB
Markdown
57 lines
1.8 KiB
Markdown
# AnyTool
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<a href='https://arxiv.org/abs/2402.04253'><img src='https://img.shields.io/badge/arXiv-2402.04253-b31b1b.svg'></a> <a href='https://github.com/buaacyw/GaussianEditor/blob/master/LICENSE.txt'><img src='https://img.shields.io/badge/License-Apache-blue'></a>
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This is the implementation of the paper [AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls](https://arxiv.org/abs/2402.04253)
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# 🔧 Installation
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## ✅ Dependencies
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Require Python 3.9+
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## 🚀 Quick install
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```bash
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pip install requirements.txt
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```
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# 🔆 Data
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**ToolBench**
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Refer to [ToolBench](https://github.com/OpenBMB/ToolBench).
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**AnyToolBench**
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Generation script
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```
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python data_generation_by_gpt4.py
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```
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We provide sample data in anytoolbench.json file.
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# 🚗 Run AnyTool
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Fill your OpenAI config and toolbench key into the config.py.
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Run ToolBench
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```
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python anytool.py --output_dir result/test_instruction/G1_instruction --query_path data/test_instruction/G1_instruction.json --max_api_number 64
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```
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Run AnyToolBench
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```
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python anytool.py --output_dir result/anytoolbench --query_path anytoolbench.json -max_api_number 64
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```
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# 👨🏫 Acknowledgement
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This repo is built on [ToolBench](https://github.com/OpenBMB/ToolBench).
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# 📑Citation
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If you find this project is helpful for your research, consider citing our paper
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```
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@article{du2024anytool,
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title={AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls},
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author={Du, Yu and Wei, Fangyun and Zhang, Hongyang},
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journal={arXiv preprint arXiv:2402.04253},
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year={2024}
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}
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```
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