Skip to content

README_EN.md

Latest commit

 

History

History
148 lines (116 loc) · 5.75 KB

README_EN.md

File metadata and controls

148 lines (116 loc) · 5.75 KB

Paper Assistant

A simple personal paper analysis assistant tool that helps automatically analyze academic paper content and generate structured reports.

Features

1. Single Paper Analysis (analyze)

Analyze a specific PDF paper, extract key information, and generate a structured Markdown report.

Features:

  • Automatically extract full text content from PDF papers
  • Use DeepSeek API for intelligent analysis
  • Generate structured reports containing paper summary, main contributions, technical highlights, etc.
  • Save analysis results in Markdown format

Usage:

python main.py analyze --paper_path "/path/to/your/paper.pdf"

Note: If the file path contains spaces, please enclose it in double quotes.

2. Batch Paper Analysis (batch_analyze)

Batch analyze all PDF papers in a specified folder, suitable for processing multiple paper files.

Features:

  • Recursively scan all PDF files in the specified folder
  • Automatically analyze and generate reports for each paper
  • Provide analysis statistics (total files, successful, failed)
  • Automatically record the list of files that failed to analyze

Usage:

python main.py batch_analyze --folder_path "/path/to/papers/folder" --output_dir "/path/to/output/folder"

Parameter Description:

  • --folder_path: Path to the folder containing PDF papers
  • --output_dir: Path to the directory where analysis results will be saved

3. Direct Analysis from URL (analyze_from_url)

Analyze paper content directly from a URL without downloading the PDF file, especially useful when normal downloads fail.

Features:

  • Fetch and process PDF content directly from URL
  • No need to save intermediate PDF files locally
  • Support various academic paper URLs (such as ArXiv)
  • Generate structured reports in the same format as normal analysis

Usage:

python main.py analyze_from_url --url "https://arxiv.org/pdf/2510.06557v1" --output_dir "./outputs"

Parameter Description:

  • --url: The PDF URL link of the paper
  • --output_dir: Path to the directory where analysis results will be saved (optional, defaults to outputs)

4. Download and Analyze from URL (analyze_from_url_download)

Download the PDF paper from URL first, then analyze it, saving both the paper file and the analysis report.

Features:

  • Download PDF papers from URL to local storage
  • Automatically analyze papers after download
  • Save both the original PDF file and analysis report
  • Support various academic paper URLs (such as ArXiv)

Usage:

python main.py analyze_from_url_download --url "https://arxiv.org/pdf/2510.06557v1" --output_dir "./outputs" --overwrite False

Parameter Description:

  • --url: The PDF URL link of the paper
  • --output_dir: Path to the directory where downloaded files and analysis results will be saved (optional, defaults to outputs)
  • --overwrite: Whether to overwrite existing files (optional, defaults to False)

5. Paper Download (download)

Download specified academic papers to local storage, supporting downloads via URL or arXiv ID.

Features:

  • Support downloading papers via URL
  • Support downloading papers via arXiv ID
  • Optionally automatically rename downloaded files using paper titles
  • Support custom download directories

Usage:

# Download via URL
python main.py download --url "https://arxiv.org/pdf/2510.06557v1"

# Download via arXiv ID
python main.py download --arxiv_id "2510.06557"

# Disable title renaming
python main.py download --url "https://arxiv.org/pdf/2510.06557v1" --no_title_rename

# Specify download directory
python main.py download --url "https://arxiv.org/pdf/2510.06557v1" --output_dir "./my_papers"

Parameter Description:

  • --url: URL link to the paper (either --url or --arxiv_id is required)
  • --arxiv_id: arXiv paper ID (either --url or --arxiv_id is required)
  • --output_dir: Path to the download directory (optional, defaults to downloads)
  • --no_title_rename: Disable automatic renaming of downloaded files using paper titles

6. Notion Import (notion_import)

Import paper analysis reports to a specified Notion page for easy organization and reference of paper analysis results.

Features:

  • Read local Markdown format paper analysis reports
  • Convert report content to Notion format and add to the specified page
  • Automatically parse structured content such as paper title, abstract, research background, etc.
  • Support proxy configuration to solve connection issues

Usage:

# Basic usage
python main.py notion_import --report_path "/path/to/report.md"

# Disable proxy
python main.py notion_import --report_path "/path/to/report.md" --no_proxy

Parameter Description:

  • --report_path: Path to the paper analysis report in Markdown format (required)
  • --api_key: Notion API key (optional, defaults to value from environment variables)
  • --database_id: Notion database ID (optional, defaults to value from environment variables)
  • --no_proxy: Disable proxy (to resolve proxy connection issues)
  • --as_page: Create a standalone page instead of a database entry

Environment Requirements

  • Python 3.7+
  • Install dependencies: pip install -r requirements.txt
  • DeepSeek API key (needs to be configured in the .env file)
  • Notion API key (for using Notion import functionality)

Notes

  • Ensure the DeepSeek API key is correctly configured
  • Use quotes when handling file paths containing spaces
  • The batch analysis feature will create a separate output directory for each paper
  • Analysis results are saved in the outputs directory by default
  • When using the Notion import functionality, ensure the API key is valid and has been correctly shared with relevant pages

Future Plans

  • Optimize paper search functionality
  • Improve report generation functionality

Hope this tool helps you read and analyze academic papers more efficiently!