98 lines
3.3 KiB
Markdown
98 lines
3.3 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/dyabel/AnyTool/blob/public/LICENSE'><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 -r requirements.txt
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```
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# 🔆 Preparation
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**OPENAI API config and the ToolBench key**
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Fill your OpenAI GPT-4 API config and toolbench key into the config.py (see config_example.py).
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Fill out the [form](https://docs.google.com/forms/d/e/1FAIpQLSdqHypmYanWU8ZhuUcrEuM5eFB03WqaqYJzvKUxUe1HzUBB3A/viewform?usp=send_form) to get the toolbench key.
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**ToolBench**
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Download the ToolBench data using the following link: [Google Drive](https://drive.google.com/drive/folders/1yBUQ732mPu-KclJnuQELEhtKakdXFc3J) or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/c9e50625743b40bfbe10/).
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The file structure is as follows:
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```
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├── /data/
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│ ├── /instruction/
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│ ├── /answer/
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│ ├── /toolenv/
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│ ├── /retrieval/
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│ ├── /test_instruction/
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│ ├── /test_query_ids/
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│ ├── /retrieval_test_query_ids/
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│ ├── toolllama_G123_dfs_train.json
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│ └── toolllama_G123_dfs_eval.json
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├── /reproduction_data/
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│ ├── /chatgpt_cot/
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│ ├── /chatgpt_dfs/
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│ ├── ...
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│ └── /toolllama_dfs/
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```
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For more details, please refer to [ToolBench](https://github.com/OpenBMB/ToolBench).
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**Prepare the API data**
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You should prepare the ToolBench data first. Make sure you have the directory of data/toolenv/tools
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```
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export PYTHONPATH=./
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python scripts/extract_api_details.py
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python scripts/extract_category_tool_details.py
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python scripts/extract_tool_database.py
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```
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**AnyToolBench**
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Generation script
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```
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export PYTHONPATH=./
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python scripts/anytoolbench_generation.py --output_path atb_data/anytoolbench_new.json
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```
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We provide sample data in [anytoolbench.json](./atb_data/anytoolbench.json) file.
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# 🚗 Run AnyTool
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Fill your OpenAI GPT API config and toolbench key into the config.py (see config_example.py). We use Azure OpenAI for all our experiments. You can modify it according to your own configuration.
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Experiment on ToolBench, take G1-I as an example.
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```
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export PYTHONPATH=./
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python scripts/main.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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Experiment on AnyToolBench
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```
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export PYTHONPATH=./
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python scripts/main.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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