Metadata-Version: 2.4
Name: arxiv-export-documents
Version: 0.1.0
Summary: Export arxiv papers to pdf formats
Author-email: Giuseppe Zileni <giuseppe.zileni@gmail.com>
Project-URL: Homepage, https://gzileni.github.io/arxiv-export-documents
Project-URL: Repository, https://github.com/gzileni/arxiv-export-documents.git
Keywords: arxiv,export,papers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.12
Description-Content-Type: text/markdown
License-File: LICENSE.md
Dynamic: license-file

# Arxix Export

**Arxiv Export** is a Python library that allows you to search, download, and manage scientific articles from [arXiv.org](https://arxiv.org/). It is useful for automating paper downloads and obtaining structured information about articles.

## Installation

```bash
pip install arxiv-export
```

## Usage Example

```python
from arxiv_export import export_papers

def main():
    search_query = "quantum computing"
    download_path = "./arxiv_papers"
    max_results = 5

    papers = export_papers(
        search=search_query,
        path_download=download_path,
        max_results=max_results
    )

    for paper in papers:
        print(f"Title: {paper.title}")
        print(f"Authors: {', '.join(paper.authors)}")
        print(f"Summary: {paper.summary}")
        print(f"Link: {paper.link}")
        print(f"Path: {paper.path}")
        print(f"Documents: {len(paper.documents)}")
        print("-" * 80)

if __name__ == "__main__":
    main()
```

## Features

- Search for articles on arXiv using keywords.
- Automatically download article PDFs.
- Access metadata such as title, authors, abstract, link, and local path.
- Manage multiple results with a single command.

## Main Parameters

- `search`: search string (e.g., `"quantum computing"`).
- `path_download`: path to save the PDFs.
- `max_results`: maximum number of articles to download.

### Vector Database for LLMs

The `documents` property provides a list of `Document` files intended for ingestion into a vector database. These files are commonly used to supply structured data to language models (LLMs), supporting semantic search and advanced analysis.

## License

This library is distributed under the MIT license.
