```
# 🏭 Scenarios
We have applied RD-Agent to multiple valuable data-driven industrial scenarios.
## 🎯 Goal: Agent for Data-driven R&D
In this project, we are aiming to build an Agent to automate Data-Driven R\&D that can
+ 📄 Read real-world material (reports, papers, etc.) and **extract** key formulas, descriptions of interested **features** and **models**, which are the key components of data-driven R&D .
+ 🛠️ **Implement** the extracted formulas (e.g., features, factors, and models) in runnable codes.
+ Due to the limited ability of LLM in implementing at once, build an evolving process for the agent to improve performance by learning from feedback and knowledge.
+ 💡 Propose **new ideas** based on current knowledge and observations.
## 📈 Scenarios/Demos
In the two key areas of data-driven scenarios, model implementation and data building, our system aims to serve two main roles: 🦾Copilot and 🤖Agent.
- The 🦾Copilot follows human instructions to automate repetitive tasks.
- The 🤖Agent, being more autonomous, actively proposes ideas for better results in the future.
The supported scenarios are listed below:
| Scenario/Target | Model Implementation | Data Building |
| -- | -- | -- |
| **💹 Finance** | 🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/model_loop) | 🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/factor_loop)
🦾 [Auto reports reading & implementation](https://rdagent.azurewebsites.net/report_factor) |
| **🩺 Medical** | 🤖 [Iteratively Proposing Ideas & Evolving](https://rdagent.azurewebsites.net/dmm) | - |
| **🏭 General** | 🦾 [Auto paper reading & implementation](https://rdagent.azurewebsites.net/report_model) | - |
Different scenarios vary in entrance and configuration. Please check the detailed setup tutorial in the scenarios documents.
Here is a gallery of successful explorations. You can download the source code and view the execution trace using the command below:
```bash
rdagent ui --port 80 --log_dir gallary/
```
Please refer to **[📖readthedocs_scen](https://rdagent.readthedocs.io/en/latest/scens/catalog.html)** for more details of the scenarios.
# ⚙️ Framework
Automating the R&D process in data science is a highly valuable yet underexplored area in industry. We propose a framework to push the boundaries of this important research field.
The research questions within this framework can be divided into three main categories:
| Research Area | Paper/Work List |
|--------------------|-----------------|
| **Benchmark the R&D abilities** | [Benchmark](#benchmark) |
| **Idea proposal:** Explore new ideas or refine existing ones | [Research](#research) |
| **Ability to realize ideas:** Implement and execute ideas | [Development](#development) |
We believe that the key to delivering high-quality solutions lies in the ability to evolve R&D capabilities. Agents should learn like human experts, continuously improving their R&D skills.
More documents can be found in the **[📖 readthedocs](https://rdagent.readthedocs.io/)**.
# 📃 Paper/Work list
## 📊 Benchmark
- [Towards Data-Centric Automatic R&D](https://arxiv.org/abs/2404.11276)
```BibTeX
@misc{chen2024datacentric,
title={Towards Data-Centric Automatic R&D},
author={Haotian Chen and Xinjie Shen and Zeqi Ye and Wenjun Feng and Haoxue Wang and Xiao Yang and Xu Yang and Weiqing Liu and Jiang Bian},
year={2024},
eprint={2404.11276},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## 🔍 Research
In a data mining expert's daily research and development process, they propose a hypothesis (e.g., a model structure like RNN can capture patterns in time-series data), design experiments (e.g., finance data contains time-series and we can verify the hypothesis in this scenario), implement the experiment as code (e.g., Pytorch model structure), and then execute the code to get feedback (e.g., metrics, loss curve, etc.). The experts learn from the feedback and improve in the next iteration.
Based on the principles above, we have established a basic method framework that continuously proposes hypotheses, verifies them, and gets feedback from the real-world practice. This is the first scientific research automation framework that supports linking with real-world verification.
For more detail, please refer to our **[🖥️ Live Demo page](https://rdagent.azurewebsites.net)**.
## 🛠️ Development
- [Collaborative Evolving Strategy for Automatic Data-Centric Development](https://arxiv.org/abs/2407.18690)
```BibTeX
@misc{yang2024collaborative,
title={Collaborative Evolving Strategy for Automatic Data-Centric Development},
author={Xu Yang and Haotian Chen and Wenjun Feng and Haoxue Wang and Zeqi Ye and Xinjie Shen and Xiao Yang and Shizhao Sun and Weiqing Liu and Jiang Bian},
year={2024},
eprint={2407.18690},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
# 🤝 Contributing
## 📝 Guidelines
This project welcomes contributions and suggestions.
Contributing to this project is straightforward and rewarding. Whether it's solving an issue, addressing a bug, enhancing documentation, or even correcting a typo, every contribution is valuable and helps improve RDAgent.
To get started, you can explore the issues list, or search for `TODO:` comments in the codebase by running the command `grep -r "TODO:"`.
Before we released RD-Agent as an open-source project on GitHub, it was an internal project within our group. Unfortunately, the internal commit history was not preserved when we removed some confidential code. As a result, some contributions from our group members, including Haotian Chen, Wenjun Feng, Haoxue Wang, Zeqi Ye, Xinjie Shen, and Jinhui Li, were not included in the public commits.
# ⚖️ Legal disclaimer
The RD-agent is provided “as is”, without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. The RD-agent is aimed to facilitate research and development process in the financial industry and not ready-to-use for any financial investment or advice. Users shall independently assess and test the risks of the RD-agent in a specific use scenario, ensure the responsible use of AI technology, including but not limited to developing and integrating risk mitigation measures, and comply with all applicable laws and regulations in all applicable jurisdictions. The RD-agent does not provide financial opinions or reflect the opinions of Microsoft, nor is it designed to replace the role of qualified financial professionals in formulating, assessing, and approving finance products. The inputs and outputs of the RD-agent belong to the users and users shall assume all liability under any theory of liability, whether in contract, torts, regulatory, negligence, products liability, or otherwise, associated with use of the RD-agent and any inputs and outputs thereof.
🖥️ Live Demo | 🎥 Demo Video | 📖 Documentation
