PKG-INFO

Metadata-Version: 2.4
Name: open-dataflow
Version: 1.0.7
Summary: Modern Data Centric AI system for Large Language Models
Author-email: Hao Liang <hao.liang@stu.pku.edu.cn>, Xiaochen Ma <xiaochen.ma.cs@gmail.com>
License: Apache-2.0
Project-URL: Github, https://github.com/Open-DataFlow/DataFlow
Project-URL: Documentation, https://open-dataflow.github.io/DataFlow-Doc/
Project-URL: Bug Reports, https://github.com/Open-DataFlow/DataFlow/issues
Keywords: AI,artificial intelligence
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: Free For Educational Use
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
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# DataFlow

<div align="center">
  <img src="https://github.com/user-attachments/assets/3fe636ad-3026-4faf-aa44-c84b8f97a05d">


[![Documents](https://img.shields.io/badge/Documents-Click_here-brightgreen?logo=read-the-docs)](https://OpenDCAI.github.io/DataFlow-Doc/)
[![](https://img.shields.io/github/license/OpenDCAI/DataFlow)](https://github.com/OpenDCAI/DataFlow/blob/main/LICENSE)
[![](https://img.shields.io/github/stars/OpenDCAI/DataFlow?style=social)](https://github.com/OpenDCAI/DataFlow)
[![](https://img.shields.io/github/contributors/OpenDCAI/DataFlow)](https://github.com/OpenDCAI/DataFlow/graphs/contributors)
[![](https://img.shields.io/github/repo-size/OpenDCAI/DataFlow?color=green)](https://github.com/OpenDCAI/DataFlow)
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/OpenDCAI/DataFlow)

<!-- [![](https://img.shields.io/github/last-commit/OpenDCAI/DataFlow)](https://github.com/OpenDCAI/DataFlow/commits/main/) -->
<!--[![](https://img.shields.io/github/issues-raw/OpenDCAI/DataFlow)](https://github.com/OpenDCAI/DataFlow/issues) -->
🎉 If you like our project, please give us a star ⭐ on GitHub for the latest update.

**Beginner-friendly learning resources (continuously updated)**: 🎬 [DataFlow Video Tutorials](https://space.bilibili.com/3546929239689711?spm_id_from=333.337.0.0); 📚 [DataFlow Written Tutorials](https://wcny4qa9krto.feishu.cn/wiki/I9tbw2qnBi0lEakmmAGclTysnFd)


[简体中文](./README-zh.md) | English


</div>

## 📰 1. News
- **[2025-11-20] Introducing New Data Agents for DataFlow!** 🤖
You can try them out now and follow the tutorial on Bilibili for a quick start.

- [2025-06-28] 🎉 We’re excited to announce that DataFlow, our Data-centric AI system, is now released! Stay tuned for future updates.

## 🔍 2. Overview

<!--  <img src="./static/images/dataflow_framework.jpg"> -->
  ![dataflow_framework](https://github.com/user-attachments/assets/b44db630-754a-44a8-bec7-6d350bf5ed61)


DataFlow is a data preparation and training system designed to **parse, generate, process, and evaluate** high-quality data from noisy sources (PDF, plain-text, low-quality QA), thereby improving the performance of large language models (LLMs) in specific domains through targeted training (Pre-training, Supervised Fine-tuning, RL training) or RAG using knowledge base cleaning. **DataFlow has been empirically validated to improve domain-oriented LLMs' performance in fields such as healthcare, finance, and law.**

Specifically, we are constructing diverse `operators` leveraging rule-based methods, deep learning models, LLMs, and LLM APIs. These operators are systematically integrated into distinct `pipelines`, collectively forming the comprehensive `DataFlow system`. Additionally, we develop an intelligent `DataFlow-agent` capable of dynamically assembling new `pipelines` by recombining existing `operators` on demand.



<!-- Text: 输入是烂数据 通过大模型 输出QA （主要是强化学习）
NL2SQL: 反向构造SQL QA
Reasonning：Question很短，构建长链COT ，是否有category，是否有难度（通过大模型）
Agentic RAG: 输入QA，出来是 QA。没有额外信息解决不了，必须要引入
Knowlege Base Cleaning: PDF，表格+doc text输入，输出是高质量知识库
Dataflow-agent: 用Agent自动合成pipeline。编排已有算子。 -->

## 🛠️ 3. Operators Functionality

### 🔧 3.1 How Operators Work

DataFlow adopts a modular operator design philosophy, building flexible data processing pipelines by combining different types of operators. As the basic unit of data processing, an operator can receive structured data input (such as in json/jsonl/csv format) and, after intelligent processing, output high-quality data results. For a detailed guide on using operators, please refer to the [Operator Documentation](https://opendcai.github.io/DataFlow-Doc/en/guide/text_evaluation_operators/ ).


![dataflow_operator](https://github.com/user-attachments/assets/d79a0d8b-09ef-457e-af8b-85af0d03b73d)

### 📊 3.2 Operator Classification System

In the DataFlow framework, operators are divided into three core categories based on their functional characteristics:

| Operator Type | Quantity | Main Function |
|---|---|---|
| **Generic Operators** | 80+ | Covers general functions for text evaluation, processing, and synthesis |
| **Domain-Specific Operators** | 40+ | Specialized processing for specific domains (e.g., medical, financial, legal) |
| **Evaluation Operators** | 20+ | Comprehensively evaluates data quality from 6 dimensions |

## 🛠️ 4. Pipelines Functionality
### 🔧 4.1 Ready-to-Use PipeLines
Current Pipelines in Dataflow are as follows:
- [📝 **Text Pipeline**](https://opendcai.github.io/DataFlow-Doc/en/guide/textpipeline): Mine question-answer pairs from large-scale plain-text data (mostly crawed from InterNet) for use in SFT and RL training.
  - ![dataflow_text_pipeline](https://github.com/user-attachments/assets/34e3aef2-ba4f-4997-9127-9d21fdb2dede)
  - [[HuggingFace🤗 demo input & output for **Text Pipeline**]](https://huggingface.co/datasets/Open-Dataflow/dataflow-demo-Text)
- [🧠 **Reasoning Pipeline**](https://opendcai.github.io/DataFlow-Doc/en/guide/reasoningpipeline/#_2-question-handling): Enhances existing question–answer pairs with (1) extended chain-of-thought, (2) category classification, and (3) difficulty estimation.
  - ![dataflow_reasoning_pipeline](https://github.com/user-attachments/assets/fef5829b-3991-4dcb-99ad-d61d95c982ea)
  - [[HuggingFace🤗 demo input & output for **Reasoning Pipeline**]](https://huggingface.co/datasets/Open-Dataflow/dataflow-demo-Reasonning)
- [🗃️ **Text2SQL Pipeline**](https://opendcai.github.io/DataFlow-Doc/en/guide/text2sqlpipeline/): Translates natural language questions into SQL queries, supplemented with explanations, chain-of-thought reasoning, and contextual schema information.
  - ![dataflow_text2sql_pipeline](https://github.com/user-attachments/assets/bae9914e-851b-4502-8696-291d6c1b8824)
  - [[HuggingFace🤗 demo input & output for **Text2SQL Pipeline**]](https://huggingface.co/datasets/Open-Dataflow/dataflow-demo-Text2SQL)
- [📚 **Knowlege Base Cleaning Pipeline**](https://opendcai.github.io/DataFlow-Doc/en/guide/r51ooua8/): Extract and structure knowledge from unorganized sources like tables, PDFs, and Word documents into usable entries for downstream RAG or QA pair generation.
  - ![dataflow_KnowledgeBaseClean_pipeline](https://github.com/user-attachments/assets/6f21e682-ec10-42af-b5e2-8fec2929eeae)
- [🤖 **Agentic RAG Pipeline**](https://opendcai.github.io/DataFlow-Doc/en/guide/agenticrag_pipeline/): Identify and extract QA pairs from existing QA datasets or knowledge bases that require external knowledge to answer, for use in downstream training of Agnetic RAG tasks.
  - ![dataflow_agenticRAG_pipeline](https://github.com/user-attachments/assets/65e80dca-f286-495b-abb7-804b3fc34a53)

### ⚙️ 4.2 Flexible Operator PipeLines
In this framework, operators are categorized into Fundamental Operators, Generic Operators, Domain-Specific Operators, and Evaluation Operators, etc., supporting data processing and evaluation functionalities. Please refer to the [documentation](https://OpenDCAI.github.io/DataFlow-Doc/) for details.

### 🤖 4.3 Agent Guided Pipelines
<!-- Building on top of this, we also provide the -->
- **DataFlow Agent**: An intelligent assistant that performs data analysis, writes custom `operators`, and automatically orchestrates them into `pipelines` based on specific task objectives.

  - ![dataflow_agent_pipeline](https://github.com/user-attachments/assets/fe0776fa-55bd-49cd-bfe6-06ad377f62bb)
  - [[HuggingFace🤗 demo input & output for **DataFlow Agent**]](https://huggingface.co/datasets/Open-Dataflow/dataflow-demo-Agent)

<!-- ### 3.1 Text Pipeline
![](./static/images/demo_reasoning.png) -->

## ⚡ 5. Quick Start

### 🛠️ 5.1 Environment Setup and Installation

Please use the following commands for environment setup and installation👇

```shell
conda create -n dataflow python=3.10 
conda activate dataflow

pip install open-dataflow
```
If you want to use your own GPU for local inference, please use:
```shell
pip install open-dataflow[vllm]
```
> DataFlow supports Python>=3.10 environments

After installation, you can use the following command to check if dataflow has been installed correctly:

```shell
dataflow -v
```

If installed correctly, you should see:
```log
open-dataflow codebase version: 1.0.0
        Checking for updates...
        Local version:  1.0.0
        PyPI newest version:  1.0.0
You are using the latest version: 1.0.0.
```

#### 🐳 5.1.1 Docker Installation (Alternative)

We also provide a **Dockerfile** for easy deployment and a **pre-built Docker image** for immediate use.

##### Option 1: Use Pre-built Docker Image

You can directly pull and use our pre-built Docker image:

```shell
# Pull the pre-built image
docker pull molyheci/dataflow:cu124

# Run the container with GPU support
docker run --gpus all -it molyheci/dataflow:cu124

# Inside the container, verify installation
dataflow -v
```

##### Option 2: Build from Dockerfile

Alternatively, you can build the Docker image from the provided Dockerfile:

```shell
# Clone the repository (HTTPS)
git clone https://github.com/OpenDCAI/DataFlow.git
# Or use SSH
# git clone git@github.com:OpenDCAI/DataFlow.git

cd DataFlow

# Build the Docker image
docker build -t dataflow:custom .

# Run the container
docker run --gpus all -it dataflow:custom

# Inside the container, verify installation
dataflow -v
```

> **Note**: The Docker image includes CUDA 12.4.1 support and comes with vLLM pre-installed for GPU acceleration. Make sure you have [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) installed to use GPU features.

### 📖 5.2 Reference Project Documentation

For detailed **usage instructions** and **getting started guide**, please visit our [Documentation](https://OpenDCAI.github.io/DataFlow-Doc/).

## 🧪 6. Experimental Results
For Detailed Experiments setting, please visit our documentation.


### 📝 6.1 Text Pipeline

#### 6.1.1 Pre-training data filter pipeline
The `pre-training data processing pipeline` was applied to randomly sampled data from the RedPajama dataset, resulting in a final data retention rate of 13.65%. The analysis results using `QuratingScorer` are shown in the figure. As can be seen, the filtered pretraining data significantly outperforms the original data across four scoring dimensions: writing style, requirement for expert knowledge, factual content, and educational value. This demonstrates the effectiveness of the DataFlow pretraining data processing.

<div align="center">
  <img src="https://github.com/user-attachments/assets/bc756c64-6640-4f46-b8ed-a4cd9be0a623" width="60%">
</div>


#### 6.1.2 SFT data filter pipeline
We filtered 3k records from `alpaca` dataset and compared it with randomly selected 3k data from `alpaca` dataset by training it on Qwen2.5-7B. Results are:

<div align="center">
  <img src="https://github.com/user-attachments/assets/38d477d4-523d-4843-83f7-b7f518a18c1d" width="60%">
</div>

### 🧠 6.2 Reasoning Pipeline

We verify our reasoning pipeline by SFT on a Qwen2.5-32B-Instruct with Reasoning Pipeline synsthized data. We generated 1k and 5k SFT data pairs. Results are: 

<div align="center">
  <img src="https://github.com/user-attachments/assets/d3af9728-0372-4c2c-9cd3-73f1e337d4c0" width="60%">
</div>

### 🗃️ 6.3 Text2SQL PipeLine
We fine-tuned the Qwen2.5-Coder-7B-Instruct model using both Supervised Fine-tuning (SFT) and Reinforcement Learning (RL), with data constructed via the DataFlow-Text2SQL Pipeline. Results are:

<div align="center">
  <img src="https://github.com/user-attachments/assets/7809f57a-33c5-4792-b91b-10e4f39bafc1" width="60%">
</div>



## 📄 7. Publications
Our team has published the following papers that form core components of the DataFlow system:

| Paper Title | DataFlow Component | Venue | Year |
|-------------|-------------------|-------|------|
| [MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification](https://arxiv.org/pdf/2502.13383) | Multimodal reasoning verification framework for data processing and evaluation | ACL | 2025 |
| [Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration](https://arxiv.org/pdf/2410.08102) | Multi-actor collaborative data selection mechanism for enhanced data filtering and processing | ACL | 2025 |

**Contributing Institutions**: 
<img src="./static/logo/pku.png" alt="PKU" height="30"/> 
<img src="./static/logo/hkust.png" alt="HKUST" height="30"/> 
<img src="./static/logo/CAS.png" alt="CAS" height="30"/> 
<img src="./static/logo/shanghai_ailab.png" alt="Shanghai AI Lab" height="30"/> 
<img src="./static/logo/baichuan.png" alt="Baichuan" height="30"/> 
<img src="./static/logo/ant_group.png" alt="Ant Group" height="30"/>

## 🏆 8. Awards & Achievements

We are honored to have received **first-place awards** in two major international AI competitions, recognizing the excellence and robustness of DataFlow and its reasoning capabilities:

| Competition                                                         | Track                                                      | Award                     | Organizer                                                 | Date            |
| ------------------------------------------------------------------- | ---------------------------------------------------------- | ------------------------- | --------------------------------------------------------- | --------------- |
| **ICML 2025 Challenges on Automated Math Reasoning and Extensions** | Track 2: *Physics Reasoning with Diagrams and Expressions* | 🥇 **First Place Winner** | ICML AI for Math Workshop & AWS Codabench                 | July 18, 2025   |
| **2025 Language and Intelligence Challenge (LIC)**                  | Track 2: *Beijing Academy of Artificial Intelligence*      | 🥇 **First Prize**        | Beijing Academy of Artificial Intelligence (BAAI) & Baidu | August 10, 2025 |

<div align="center">

<table>
  <tr>
    <td align="center" width="50%">
      <img src="https://github.com/user-attachments/assets/8f28e0fe-c883-42c0-b224-3693f6281a14" alt="ICML 2025 Certificate" width="95%"><br>
      <sub><em>ICML 2025 Automated Math Reasoning Challenge — First Place Winner</em></sub>
    </td>
    <td align="center" width="30%">
      <img src="https://github.com/user-attachments/assets/364618b6-4dfa-4c34-928f-e3da85cbd03a" alt="LIC 2025 Certificate" width="95%"><br>
      <sub><em>BAAI Language & Intelligence Challenge 2025 — First Prize</em></sub>
    </td>
  </tr>
</table>

</div>


## 💐 9. Acknowledgements
We sincerely appreciate [MinerU](https://github.com/opendatalab/MinerU)'s outstanding contribution, particularly its robust text extraction capabilities from PDFs and documents, which greatly facilitate data loading.

## 🤝 10. Community & Support
Join the DataFlow open-source community to ask questions, share ideas, and collaborate with other developers!

•	📮 [GitHub Issues](../../issues): Report bugs or suggest features
 
•	🔧 [GitHub Pull Requests](../../pulls): Contribute code improvements

•	💬 Join our community groups to connect with us and other contributors!
 
<div align="center">
  <img src="https://github.com/user-attachments/assets/c04cc04c-f1f4-49b0-9758-56d9d8d37c4a" width="60%">
</div>


## 📜 11. Citation
If you use DataFlow in your research, feel free to give us a cite.
```bibtex
@misc{dataflow2025,
  author       = {DataFlow Develop Team},
  title        = {DataFlow: A Unified Framework for Data-Centric AI},
  year         = {2025},
  howpublished = {\url{https://github.com/OpenDCAI/DataFlow}},
  note         = {Accessed: 2025-07-08}
}
```

## 📊 12. Statistics
<div align="center">
  <a href="https://star-history.com/#OpenDCAI/DataFlow&Date">
    <picture>
      <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=OpenDCAI/DataFlow&type=Date&theme=dark" />
      <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=OpenDCAI/DataFlow&type=Date" />
      <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=OpenDCAI/DataFlow&type=Date" style="width:50%;" />
    </picture>
  </a>
  
</div>

---
<div align="center">
  <sub>
    Connect with the 
    <a href="https://zwt233.github.io/" target="_blank"><strong>PKU-DCAI Research Team</strong></a> 
    on Xiaohongshu: <strong>26133106768</strong>
  </sub>
</div>