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--- |
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title: Academic Research Paper Assistant |
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emoji: 🚀 |
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colorFrom: gray |
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colorTo: yellow |
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sdk: streamlit |
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sdk_version: 1.40.1 |
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app_file: app.py |
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pinned: false |
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short_description: Academic-Research-Paper-Assistant |
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--- |
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# Academic Research Paper Assistant |
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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. |
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[Watch the Project Demonstration on YouTube](http://www.youtube.com/watch?v=DpunojtYDFE) |
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## Features |
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- **Paper Search**: Search and retrieve relevant research papers from Arxiv |
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- **Question Answering**: Get answers about specific papers or content with source citations |
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- **Summarization**: Extract key findings and trends from multiple papers |
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- **Future Works Generation**: Generate research directions and improvement plans |
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- **Interactive UI**: User-friendly Streamlit interface for paper browsing and chat |
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## Architecture |
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The application uses a multi-agent system with the following components: |
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- **Intent Agent**: Classifies user queries to route to appropriate specialized agents |
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- **Search Agent**: Retrieves and processes papers from Arxiv |
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- **QA Agent**: Handles specific questions about paper content |
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- **Summarization Agent**: Synthesizes information across multiple papers |
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- **Future Works Agent**: Generates research directions and review papers |
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## Technology Stack |
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- **Frontend**: Streamlit |
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- **Backend**: FastAPI |
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- **LLM Integration**: Google Generative AI |
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- **Vector Store**: FAISS |
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- **Document Processing**: LangChain |
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- **PDF Processing**: PDFMiner |
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## Installation |
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1. Clone the repository: |
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```bash |
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git clone https://github.com/Sanket-Kathrotiya/Academic-Research-Paper-Assistant.git |
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``` |
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2. Create and activate a virtual environment: |
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```bash |
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conda create --name venv |
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conda activate venv |
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``` |
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3. Install dependencies: |
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```bash |
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pip install -r requirements.txt |
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``` |
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4. Set up environment variables: |
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```bash |
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# Create .env file |
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GOOGLE_API_KEY=your_api_key_here |
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``` |
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## Usage |
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1. Start the application: |
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```bash |
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cd src |
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streamlit run app.py |
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``` |
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2. Open your browser and navigate to `http://localhost:8501` |
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3. Enter your research topic or question in the chat interface |
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4. Select papers of interest from the timeline view |
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5. Interact with the assistant through natural language queries |
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## Example Queries |
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- "Find papers about text-to-SQL" |
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- "What are the key findings in Paper X?" |
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- "Summarize the advancements in this field over the last 5 years" |
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- "What are potential future research directions?" |
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- "Generate a review paper outline" |
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## Project Structure |
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``` |
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src |
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├── agents |
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│ ├── __init__.py |
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│ ├── dummy |
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│ ├── future_works_agent.py |
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│ ├── intent_agent.py |
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│ ├── qa_agent.py |
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│ ├── search_agent.py |
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│ └── summarization_agent.py |
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├── config |
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│ └── config.py |
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├── app.py |
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├── router.py |
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├── README.md |
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└── requirements.txt |
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``` |
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## Acknowledgments |
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- [Streamlit](https://streamlit.io/) for the web interface framework |
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- [LangChain](https://python.langchain.com/) for LLM integration |
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- [Google Generative AI](https://ai.google.dev/) for the language model |
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- [Arxiv](https://arxiv.org/) for the research paper database |
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## Requirements |
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See `requirements.txt` for a full list of dependencies. |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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