The All-in-One Local AI Data Cleaner.

Why Loclean?

📚 Documentation: nxank4.github.io/loclean

Loclean bridges the gap between Data Engineering and Local AI, designed for production pipelines where privacy and stability are non-negotiable.

Privacy-First & Zero Cost

Leverage the power of Small Language Models (SLMs) like Phi-3 and Llama-3 running locally via llama.cpp. Clean sensitive PII, medical records, or proprietary data without a single byte leaving your infrastructure.

Deterministic Outputs

Forget about "hallucinations" or parsing loose text. Loclean uses GBNF Grammars and Pydantic V2 to force the LLM to output valid, type-safe JSON. If it breaks the schema, it doesn't pass.

Structured Extraction with Pydantic

Extract structured data from unstructured text with guaranteed schema compliance:

from pydantic import BaseModel
import loclean

class Product(BaseModel):
    name: str
    price: int
    color: str

# Extract from text
item = loclean.extract("Selling red t-shirt for 50k", schema=Product)
print(item.name)  # "t-shirt"
print(item.price)  # 50000

# Extract from DataFrame (default: structured dict for performance)
import polars as pl
df = pl.DataFrame({"description": ["Selling red t-shirt for 50k"]})
result = loclean.extract(df, schema=Product, target_col="description")

# Query with Polars Struct (vectorized operations)
result.filter(pl.col("description_extracted").struct.field("price") > 50000)

The extract() function ensures 100% compliance with your Pydantic schema through:

• Dynamic GBNF Grammar Generation: Automatically converts Pydantic schemas to GBNF grammars
• JSON Repair: Automatically fixes malformed JSON output from LLMs
• Retry Logic: Retries with adjusted prompts when validation fails

Backend Agnostic (Zero-Copy)

Built on Narwhals, Loclean supports Pandas, Polars, and PyArrow natively.

• Running Polars? We keep it lazy.
• Running Pandas? We handle it seamlessly.
• No heavy dependency lock-in.

Installation

Requirements

• Python 3.10, 3.11, 3.12, or 3.13
• No GPU required (runs on CPU by default)

Basic Installation

Using pip:

pip install loclean

Using uv (recommended for faster installs):

uv pip install loclean

Using conda/mamba:

conda install -c conda-forge loclean
# or
mamba install -c conda-forge loclean

Optional Dependencies

The basic installation includes local inference support (via llama-cpp-python). Loclean uses Narwhals for backend-agnostic DataFrame operations, so if you already have Pandas, Polars, or PyArrow installed, the basic installation is sufficient.

Install DataFrame libraries (if not already present):

If you don't have any DataFrame library installed, or want to ensure you have all supported backends:

pip install loclean[data]

This installs: pandas>=2.3.3, polars>=0.20.0, pyarrow>=22.0.0

For Cloud API support (OpenAI, Anthropic, Gemini):

Cloud API support is planned for future releases. Currently, only local inference is available:

pip install loclean[cloud]

Install all optional dependencies:

pip install loclean[all]

This installs both loclean[data] and loclean[cloud]. Useful for production environments where you want all features available.

Note for developers: If you're contributing to Loclean, use the Development Installation section below (git clone + uv sync --dev), not loclean[all].

Development Installation

To contribute or run tests locally:

# Clone the repository
git clone https://github.com/nxank4/loclean.git
cd loclean

# Install with development dependencies (using uv)
uv sync --dev

# Or using pip
pip install -e ".[dev]"

Model Management

Loclean automatically downloads models on first use, but you can pre-download them using the CLI:

# Download a specific model
loclean model download --name phi-3-mini

# List available models
loclean model list

# Check download status
loclean model status

Available Models

• phi-3-mini: Microsoft Phi-3 Mini (3.8B, 4K context) - Default, balanced
• tinyllama: TinyLlama 1.1B - Smallest, fastest
• gemma-2b: Google Gemma 2B Instruct - Balanced performance
• qwen3-4b: Qwen3 4B - Higher quality
• gemma-3-4b: Gemma 3 4B - Larger context
• deepseek-r1: DeepSeek R1 - Reasoning model

Models are cached in ~/.cache/loclean by default. You can specify a custom cache directory using the --cache-dir option.

Quick Start

Loclean is best learned by example. We provide a set of Jupyter notebooks to help you get started:

• 01-quick-start.ipynb: Core features, structured extraction, and Privacy Scrubbing.
• 02-data-cleaning.ipynb: Comprehensive data cleaning strategies.
• 03-privacy-scrubbing.ipynb: Deep dive into PII redaction.

Check out the examples/ directory for more details.

Contributing

We love contributions! Loclean is strictly open-source under the Apache 2.0 License.

Please read our Contributing Guide for details on how to set up your development environment, run tests, and submit Pull Requests.

Built for the Data Community.