LitRev

LitRev is an AI-powered software for automating and streamlining the literature review process using ChatGPT. It helps researchers generate high-quality reviews by assisting with search query formulation, article retrieval, content analysis, and structured report generation — all in one workflow.

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✨ Key Features

• Smart Query Generation
  Automatically generates precise and domain-specific search queries from your research question using advanced language models.

• Corpus Selection and Management
  Suggests relevant publication sources (e.g., PubMed, ArXiv, CrossRef), with the ability to include or exclude sources manually.

• Automated Article Retrieval
  Searches selected databases using generated queries and fetches metadata, abstracts, and full-text documents where accessible.

• Screening and Selection
  Displays retrieved articles for manual review and filtering, helping you select only the most relevant works for your literature review.

• Methodology-Based Review Generation
  Supports multiple research methodologies (e.g., systematic, scoping, narrative) to structure and generate well-formed literature reviews.

• Integrated Document Editor
  Includes a built-in word processor with support for editing, adding figures, and receiving suggestions for improving flow and structure.

• Multi-format Export
  Export your final literature review in .docx, .pdf, and other popular formats.

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🚀 Getting Started

Installation

git clone https://github.com/eng-james-o/LitRev.git
cd LitRev
pip install -r requirements.txt

Running the App

python litrev/main.py

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How It Works

1. Input Research Question Users begin by providing their research question.

2. AI-Powered Query Generation The system crafts optimized search queries using ChatGPT.

3. Corpus Suggestion & Customization LitRev suggests appropriate journals and databases. Users can modify the corpus.

4. Article Search & Retrieval Articles are fetched from APIs (e.g., PubMed, ArXiv, CrossRef) with metadata, abstracts, and full texts where possible.

5. Article Screening An interface allows users to browse and select articles for the review.

6. Structured Review Generation The system assembles a coherent literature review using selected methodology.

7. Editing & Exporting Users can edit the review and export it to their desired format.

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🧩 Project Structure (Planned)

LitRev/
├── litrev/               # Core modules: AI, retrieval, writing
├── assets/               # SVG, Png, jpg assets
├── ui/                   # Graphical interface 
├── tests/                # Unit and integration tests
├── data/                 # Temporary data, cached articles
├── examples/             # Usage demos and workflows
├── docs/                 # Developer & user documentation
├── requirements.txt
├── README.md
└── setup.py

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🛠️ Tech Stack

• Python 3.10+
• OpenAI GPT (via API)
• Requests, Pandas, Langchain
• python-docx or similar for document generation
• PySide2 QML for GUI

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📚 Use Cases

• Academic researchers preparing literature reviews for publications
• Graduate students writing theses or dissertations
• Policy analysts compiling evidence-based reports
• R&D professionals exploring existing knowledge on technical topics

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📈 Roadmap

• Initial CLI prototype
• Implement multi-source article retrieval
• Build screening interface
• Integrate review generation engine
• Add editor and export options
• GUI for end-to-end workflow
• Add support for citation management and bibliography generation

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🤝 Contribution

Contributions, issues, and feature requests are welcome. Feel free to check the issues page.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.