Metadata-Version: 2.3
Name: axetract
Version: 0.1.3
Summary: Low-Cost Cross-Domain Web Structured Information Extraction using specialized LoRA adapters.
Keywords: web-extraction,llm,structured-data,html-pruning,lora,vllm
Author: Abdelrahman Mansour, Khaled Alshaer
Author-email: Abdelrahman Mansour <abdelrahman.f.mansour@gmail.com>, Khaled Alshaer <khaled.w.alshaer@gmail.com>
License: MIT License
         
         Copyright (c) [2026] [Abdelrahman Mansour, Khaled Alshaer]
         
         Permission is hereby granted, free of charge, to any person obtaining a copy
         of this software and associated documentation files (the "Software"), to deal
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         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: fastapi>=0.135.1
Requires-Dist: html-chunking>=0.0.4
Requires-Dist: htmlrag>=0.1.1
Requires-Dist: json-repair>=0.58.1
Requires-Dist: peft>=0.18.1
Requires-Dist: pydantic>=2.12.5
Requires-Dist: torch>=2.9.1
Requires-Dist: transformers>=4.57.6
Requires-Dist: uvicorn>=0.41.0
Requires-Dist: vllm>=0.16.0 ; extra == 'vllm'
Requires-Python: >=3.12
Project-URL: Repository, https://github.com/abdo-Mansour/axetract
Project-URL: Documentation, https://abdo-mansour.github.io/axetract/
Project-URL: Issue Tracker, https://github.com/abdo-Mansour/axetract/issues
Provides-Extra: vllm
Description-Content-Type: text/markdown

    AXEtract Logo

AXEtract
  
Low-Cost Cross-Domain Web Structured Information Extraction

***

**AXEtract** is a high-performance, low-cost framework for extracting structured data from web pages. Based on the paper [**"AXE: Low-Cost Cross-Domain Web Structured Information Extraction"**](https://www.arxiv.org/abs/2602.01838), it optimizes the extraction pipeline by using specialized LoRA adapters for pruning and query-specific extraction, enabling state-of-the-art results with small models (e.g., Qwen3-0.6B).

## 🚀 Key Features

- **🎯 Specialized LoRA Adapters**: Uses task-specific adapters for DOM pruning and structured extraction, achieving high accuracy with minimal token overhead.
- **✂️ Smart DOM Pruning**: Classifies and prunes irrelevant HTML nodes before passing them to the extractor, significantly reducing context window usage and costs.
- **📍 Grounded XPath Resolution (GXR)**: Automatically maps extracted JSON fields back to their original source XPaths in the DOM for verification and grounding.
- **⚡ High-Throughput Pipeline**: Built-in support for multiple LLM engines, including **vLLM** for production-grade serving and **HuggingFace** for local research.
- **🌐 Cross-Domain Versatility**: Designed to generalize across various web domains (e-commerce, real estate, listings) without needing domain-specific rules.

## 🛠️ Architecture

AXEtract follows a three-part decoupled pipeline for maximum efficiency:

1. **Preprocessor**: Fetches raw HTML and chunks it into manageable, token-aware fragments.
2. **AI Extractor**: Divided into two stages:
   - **Pruner**: A lightweight LLM (LoRA-powered) filters out noise and selects only relevant HTML chunks.
   - **Extractor**: A task-specific LLM maps the pruned HTML content directly to a structured JSON schema or natural language answer.
3. **Postprocessor**: Validates the output and resolves source XPaths via Grounded XPath Resolution (GXR).

## 📦 Installation

```bash
# Install from PyPI
uv pip install axetract

# Or install from source
git clone https://github.com/abdo-Mansour/axetract.git
cd axetract
uv sync
```

## 🚥 Quick Start

```python
from pydantic import BaseModel
from axetract.pipeline import AXEPipeline

# 1. Initialize the pipeline with default LoRA adapters
# (Automatically downloads adapters from HuggingFace Hub)
pipeline = AXEPipeline.from_config(use_vllm=False)

# 2. Define your desired extraction schema
class Product(BaseModel):
    name: str
    price: str
    rating: float

# 3. Extract from a URL or raw HTML
url = "https://example.com/item/12345"
result = pipeline.extract(url, schema=Product)

# 4. Access your structured data
print(f"Status: {result.status}")
print(f"Prediction: {result.prediction}")
print(f"Source XPaths: {result.xpaths}")
```

## 🌐 API Server

AXEtract includes a built-in FastAPI server for high-throughput serving. After installing the package, start it with the installed CLI entry point:

```bash
axe-server
```

Or via `python -m` for development installs:

```bash
python -m axetract.server
```

Configuration is done via environment variables:

| Variable | Default | Description |
|---|---|---|
| `AXE_USE_VLLM` | `false` | Set to `true` to use vLLM backend |
| `AXE_PORT` | `8000` | Port to listen on |
| `AXE_HOST` | `0.0.0.0` | Host to bind to |
| `AXE_LOG_FILE` | _(stderr)_ | Optional path to a log file |

See `axe_server/client_example.py` for examples of interacting with the API via `requests`.

## 📝 Citation

If you use AXEtract in your research, please cite our paper:

```bibtex
@misc{mansour2026axe,
      title={AXE: Low-Cost Cross-Domain Web Structured Information Extraction}, 
      author={Abdelrahman Mansour and Khaled W. Alshaer and Moataz Elsaban},
      year={2026},
      eprint={2602.01838},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2602.01838}, 
}
```

## 📜 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
