---
title: Academic Research Paper Assistant
emoji: 🚀
colorFrom: gray
colorTo: yellow
sdk: streamlit
sdk_version: 1.40.1
app_file: app.py
pinned: false
short_description: Academic-Research-Paper-Assistant
---


# Academic Research Paper Assistant

An intelligent assistant that helps researchers search, analyze, and synthesize academic papers using Large Language Models (LLMs). The application provides multi-agent capabilities for paper search, question answering, summarization, and future research direction generation.

[Watch the Project Demonstration on YouTube](http://www.youtube.com/watch?v=DpunojtYDFE)

![Front-End of app](imgs/Frontend.png)
## Features

- **Paper Search**: Search and retrieve relevant research papers from Arxiv
- **Question Answering**: Get answers about specific papers or content with source citations
- **Summarization**: Extract key findings and trends from multiple papers
- **Future Works Generation**: Generate research directions and improvement plans
- **Interactive UI**: User-friendly Streamlit interface for paper browsing and chat

## Architecture

The application uses a multi-agent system with the following components:

- **Intent Agent**: Classifies user queries to route to appropriate specialized agents
- **Search Agent**: Retrieves and processes papers from Arxiv
- **QA Agent**: Handles specific questions about paper content
- **Summarization Agent**: Synthesizes information across multiple papers
- **Future Works Agent**: Generates research directions and review papers

## Technology Stack

- **Frontend**: Streamlit
- **Backend**: FastAPI
- **LLM Integration**: Google Generative AI
- **Vector Store**: FAISS
- **Document Processing**: LangChain
- **PDF Processing**: PDFMiner

## Installation

1. Clone the repository:
```bash
git clone https://github.com/Sanket-Kathrotiya/Academic-Research-Paper-Assistant.git
```

2. Create and activate a virtual environment:
```bash
conda create --name venv
conda activate venv
```

3. Install dependencies:
```bash
pip install -r requirements.txt
```

4. Set up environment variables:
```bash
# Create .env file
GOOGLE_API_KEY=your_api_key_here
```

## Usage

1. Start the application:
```bash
cd src
streamlit run app.py
```

2. Open your browser and navigate to `http://localhost:8501`

3. Enter your research topic or question in the chat interface

4. Select papers of interest from the timeline view

5. Interact with the assistant through natural language queries

## Example Queries

- "Find papers about text-to-SQL"
- "What are the key findings in Paper X?"
- "Summarize the advancements in this field over the last 5 years"
- "What are potential future research directions?"
- "Generate a review paper outline"

## Project Structure

```
src
├── agents
│   ├── __init__.py
│   ├── dummy
│   ├── future_works_agent.py
│   ├── intent_agent.py
│   ├── qa_agent.py
│   ├── search_agent.py
│   └── summarization_agent.py
├── config
│   └── config.py
├── app.py
├── router.py
├── README.md
└── requirements.txt
```



## Acknowledgments

- [Streamlit](https://streamlit.io/) for the web interface framework
- [LangChain](https://python.langchain.com/) for LLM integration
- [Google Generative AI](https://ai.google.dev/) for the language model
- [Arxiv](https://arxiv.org/) for the research paper database

## Requirements

See `requirements.txt` for a full list of dependencies.







Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference