DataFlow — LLM Data Prep Pipelines + WebUI

Practical Notes

• Quant: the README shows dataflow -v output with open-dataflow codebase version: 1.0.0 (example).
• Quant: WebUI was announced in README news as 2026-02-02, making it a recent workflow surface to standardize on.

Where DataFlow fits in an agent stack

If your team is already doing RAG or fine-tuning, DataFlow is useful when you want repeatable data quality loops:

1. Generate candidates (from PDFs, logs, Q/A dumps).
2. Refine with operator transforms.
3. Evaluate + filter to keep only high-signal items.

A minimal first pipeline

• Pick one narrow domain (e.g., “customer support → product X”).
• Build a 100–500 sample dataset and run it through the same pipeline weekly.
• Track two numbers: acceptance rate after filtering, and model quality delta after training or RAG updates.

The WebUI helps teams collaborate on pipeline structure without everyone editing code.

FAQ

Q: Do I need GPUs to start? A: No. The README describes optional GPU/vLLM installs, but you can validate the CLI and pipeline structure first.

Q: Why use uv? A: The README recommends uv for faster installs and reproducible environments.

Q: What should I measure? A: Dataset acceptance rate and downstream model quality deltas across weekly pipeline runs.