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

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-tuing, RL training) or RAG using knowledge base cleaning. DataFlow has been empirically validated to improve domain-oriented LLM's performance in fields such as healthcare, finance, and law.

Specifically, we 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.

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.

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

Current Pipelines in Dataflow are as follows:

• 📝 Text Pipeline: Mine question-answer pairs from large-scale plain-text data (mostly crawed from InterNet) for use in SFT and RL training.
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  • [HuggingFace🤗 demo input & output for Text Pipeline]
• 🧠 Reasoning Pipeline: Enhances existing question–answer pairs with (1) extended chain-of-thought, (2) category classification, and (3) difficulty estimation.
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  • [HuggingFace🤗 demo input & output for Reasoning Pipeline]
• 🗃️ Text2SQL Pipeline: Translates natural language questions into SQL queries, supplemented with explanations, chain-of-thought reasoning, and contextual schema information.
  • 
  • [HuggingFace🤗 demo input & output for Text2SQL Pipeline]
• 📚 Knowlege Base Cleaning Pipeline: Extract and structure knowledge from unorganized sources like tables, PDFs, and Word documents into usable entries for downstream RAG or QA pair generation.
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• 🤖 Agentic RAG 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.
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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 for details.

• DataFlow Agent: An intelligent assistant that performs data analysis, writes custom operators, and automatically orchestrates them into pipelines based on specific task objectives.

  • 
  • [HuggingFace🤗 demo input & output for DataFlow Agent]

For environment setup and installation, please using the following commands👇

conda create -n dataflow python=3.10 
conda activate dataflow
pip install open-dataflow

If you want to use your own GPU to inference locally, please use:

pip install open-dataflow[vllm]

Dataflow supports Python>=3.10

You can use follwing command to check if installed correctly:

dataflow -v

You are expected to see following outputs:

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.

You can quickly launch a Gradio-based interface to test DataFlow operators with the following command:

dataflow webui

This will start an interactive web UI, allowing you to visualize all operators seamlessly.

For Quick-Start and Guide, please visit our Documentation.

For Detailed Experiments setting, please visit our documentation.

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.

We filted 3k record from alpaca dataset and compare it with radom selected 3k data from alpaca dataset by training it on Qwen2.5-7B. Results are:

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:

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:

Our team has published the following papers that form core components of the DataFlow system:

Contributing Institutions:

We sincerely appreciate MinerU's outstanding contribution, particularly its robust text extraction capabilities from PDFs and documents, which greatly facilitates data loading.

Join the DataFlow open-source community to ask questions, share ideas, and collaborate with other developers!

• 📮 GitHub Issues: Report bugs or suggest features

• 🔧 GitHub Pull Requests: Contribute code improvements

• 💬 Join our community groups to connect with us and other contributors!

If you use DataFlow in your research, feel free to give us a cite.

@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}
}