Research Article Assistant

A web application that helps users summarize and search scientific articles from ArXiv and HAL archives. It provides automatic summarization of scientific articles and keyword extraction to help users quickly understand research papers.

Screenshots

Article Summary View

Example of an article summary showing details, generated summary, and key concepts

Search Results View

Search interface showing results from HAL archives

API view

Features

• Article Summarization: Generate concise summaries of scientific papers
• Key Concept Extraction: Identify the most important concepts in research papers
• Research Search: Search for articles on ArXiv and HAL archives
• Clean User Interface: Modern, responsive design for easy navigation

Project Structure

The project is divided into two main components:

1. Backend (Flask API): Handles article extraction, summarization, and search
2. Frontend (React): Provides user interface for interacting with the API

Technologies Used

Backend

• Python 3.11+
• Flask (Web framework)
• LangChain (LLM integration)
• Groq (LLM provider)
• Pinecone (Vector database)

Frontend

• React 19
• JavaScript/JSX
• CSS
• Vite (Build tool)

Setup and Installation

Prerequisites

• Python 3.11+
• Node.js 18+ (for frontend)
• pnpm or npm
• Groq API Key
• Pinecone API Key and environment

Backend Setup

1. Clone the repository

   git clone https://github.com/Hamza-cpp/research-assistant.git
   cd research-assistant

2. Create a Python virtual environment

   python -m venv .venv
   source .venv/bin/activate  # On Windows: venv\\Scripts\\activate

3. Install backend dependencies

   pip install -e .

4. Configure environment variables by creating a .env file in the root directory:

   # Flask settings
    FLASK_APP=main.py
    FLASK_DEBUG=True
    PORT=5000
   
    # API Keys
    GROQ_API_KEY=your_groq_api_key_here
    PINECONE_API_KEY=your_pinecone_api_key_here
    PINECONE_ENVIRONMENT=your_pinecone_environment_here

5. Run the backend server

   python main.py

   The API will be available at http://localhost:5000

Frontend Setup

1. Navigate to the web-ui directory

   cd web-ui

2. Install frontend dependencies using pnpm

   pnpm install

   (Alternatively, you can use npm: npm install)

3. Start the development server

   pnpm run dev # or npm run dev

4. The frontend application will be available at http://localhost:5173

API Endpoints

The backend provides the following API endpoints:

• GET /api/health: Health check endpoint

• POST /api/summarize: Summarize an article from its URL

  // Request Body
  {
  "article_url": "https://arxiv.org/abs/2303.08774"
  }

• GET /api/search: Search for articles

  // Query Parameters
  ?q=machine+learning&source=arxiv&max_results=10