dd-parser-cleaner 0.4.6

A private, local LLM-powered data dictionary parser and entity mapper with automated cleaning.

dd-parser-cleaner: AI-Driven Data Preparation & Documentation

A specialized framework for automating data preparation documentation and preparing datasets for machine learning through AI-driven metadata discovery.

🌍 The Big Picture

KMDS is an initiative focused on developing documented, maintainable data science and ML projects using open-source tools and knowledge graphs. The kmds-data-helper was the first step in this journey—automatically building knowledge graphs from standard repository structures.

dd-parser-cleaner is a further refinement of this ecosystem. It targets data preparation, historically the most difficult and detail-loaded segment of any data science project. This tool provides:

1. An AI-driven framework to generate comprehensive cleaning documentation.
2. An agent-driven interface for developing datasets for ML featurization and analytics.

By capturing metadata during the cleaning phase, we create the foundation upon which future featurization logic is built.

🚀 Status & Roadmap

• Current State: dd-parser-cleaner is feature-complete (v0.4.2).
• Short Term: We will be releasing public examples of dataset migrations shortly.
• Future: Development of specialized featurization modules for ML and analytics projects will begin once the migration examples are public.

📑 Documentation Strategy (Agent-First)

This project uses a Markdown-Native documentation architecture rather than traditional external sites.

• Why? Keeping technical guides and design contracts as Markdown within the repo allows AI Agents (like your Migration Assistant) to "read" the documentation and provide better code suggestions.
• Where to look: Human users should consult the documents/ directory for methodology, and USER_GUIDE.md for quick-start instructions.

🛠️ Technical Constraints

• Offline First: Optimized for batch processing without external streaming dependencies.
• Deterministic: Ensures that running the same config on the same data yields the same results.
• Privacy-Centric: All processing and LLM grounding (via local models) stay within your local environment.

Core Capability Matrix

Capability	Operational Impact
AI Recommendations	Saves Hours: Replaces manual data profiling with LLM-generated cleaning_recommendations.md.
Clean Bucket Policy	De-risks Models: Prevents "ghost" data and undocumented noise from leaking into ML training sets.
Handshake Protocol	Audit-Ready: Creates a formal, documented bridge between Raw Data and Logic Implementation.
Agent Interface	AI-Native: Designed for AI Assistants to autonomously implement complex, vectorized domain logic.
Metadata Discovery API	Faster Featurization: Programmatic access to semantic tags (Geographic, Risk, Financial) for ML pipelines.

🚀 The 12-Step Operational Recipe

The core value of this framework is the reduction of messy data prep into a predictable, 12-step sequence. This workflow moves you from raw, undocumented data to a high-integrity analytical baseline:

1. Install: pip install dd-parser-cleaner
2. Initialize: Run init-workspace to build the KMDS directory structure.
3. Locate: Run location-helper for placement guidance - where to put the data files and documents?
4. Populate: Move source files to data/, data_dictionary/, and documents/.
5. Bootstrap: Run bootstrap-config to generate a provisional_config.yaml. (Save as config.yaml).
6. Classify: Run classify-entities to synchronize metadata and tag entities.
7. Clean: Run clean-dataset --action full to execute the diagnostic pipeline.
8. Handshake: Review the parser_cleaner_handshake.md for schema verification.
9. Baseline: Review the Null Profile to understand raw data conditions.
10. Recommendations: Review cleaning_recommendations.md for AI-driven insights.
11. Access: Use the example notebook to load the "Clean Baseline" dataset.
12. Modify: Implement domain-specific cleaning/featurization in your notebook.

⚙️ Installation

Standard Installation (CLI Only)

pip install dd-parser-cleaner

Installation with Notebook Support (Migration Assistant)

pip install "dd-parser-cleaner[notebook]"

🚀 Quick Start

1. Bootstrap Your Workspace

Initialize and configure your project without writing a single line of YAML:

uv run init-workspace ./my_project
# ... move your CSV files to ./my_project/data/ ...
uv run bootstrap-config ./my_project

2. Classification (The Handshake)

Synchronize metadata and execute semantic classification:

classify-entities

2. Cleaning (The Pipeline)

Run the cleaner to apply types, filters, and transformations grounded in the parser's metadata:

uv run clean-dataset --action full --workspace ./tests

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For detailed documentation and custom logic implementation, see the documents/ directory and USER_GUIDE.md.