Detailed Introduction
DataFlow is a data preparation and pipeline system designed for domain-specific training and retrieval-augmented generation (RAG). It composes modular operator components into reusable pipelines to parse, clean, augment, and evaluate data from noisy sources such as PDFs, plain text, and low-quality QA, producing high-quality datasets suitable for pre-training, supervised fine-tuning, or RAG workflows.
Main Features
- Modular operators: combine rule-based methods, deep models and large language models (LLM) to build diverse data-processing units.
- Reusable pipelines: orchestrate operators to deliver end-to-end flows from extraction to quality evaluation.
- Data quality scoring: multi-dimensional evaluation and filtering to improve downstream model performance and reduce noise.
Use Cases
DataFlow is suitable for scenarios that require improved domain model performance — e.g., data cleaning and labeling in healthcare, finance, and legal domains; constructing SFT/fine-tuning datasets; building high-quality knowledge entries for RAG; or embedding automated training pipelines into MLOps workflows.
Technical Characteristics
Implemented primarily in Python, the project provides a broad operator library (text processing, format extraction, generation verification), supports Docker deployment and GPU acceleration, and interoperates with vLLM and Hugging Face dataset ecosystems; the repository is Apache-2.0 licensed for research and engineering use.