📚 Researchify

Welcome to the AI Researcher project! This nifty tool harnesses the power of Large Language Models (LLMs) and advanced web searching to help you craft top-notch reports on any topic. 🤓✨

🌟 Features

• Dynamic Topic Input: Just type in your topic, and let the magic begin! 🧙‍♂️
• Web Search Integration: Uses Tavily to scour the web for the best sources. 🌐🔍
• LLM-Powered Reports: Employs Groq's LLM to generate comprehensive, human-like reports. 📝🧠
• Streamlit Interface: User-friendly interface for seamless interaction. 🖥️✨

🚀 Getting Started

Prerequisites

• Python 3.8+
• Streamlit
• dotenv
• Gemini API Key
• Tavily API Key

Installation

Clone this repository and navigate to the project directory:

git clone https://github.com/bharathajjarapu/researchify.git
cd researchify

Install the required packages:

pip install -r requirements.txt

Create a .env file and add your API keys:

GOOGLE_API_KEY=your_gemini_api_key
TAVILY_API_KEY=your_tavily_api_key

Running the App

Fire up the Streamlit app:

streamlit run app.py

🛠️ How It Works

1. User Input: Enter your topic in the text box.
2. Web Search: The app uses Tavily to perform an in-depth web search on the topic.
3. PubMed Search: The app uses PubMed Research Documents to cite the Relavent Information.
4. Wikipedia Search: The app uses Wikipedia Look up things for people, movies, series and information.
5. Report Generation: Groq's LLM takes the search results and crafts a well-structured, NYT-worthy report.
6. Display: The final report is displayed beautifully in the Streamlit interface.

Code Breakdown

• Environment Setup: Loads API keys from .env file.
• Streamlit Config: Sets up the page title and icon.
• Main Interface: Provides an input box for the topic and a button to generate the report.
• Web Search: Uses TavilyClient to fetch search results.
• PubMed Search: The app uses PubMed Research Documents to cite the Relavent Information.
• Wikipedia Search: The app uses Wikipedia Look up things for people, movies, series and information.
• Report Generation: Groq's LLM processes the results and generates the report.
• Output: Displays the final report in Markdown format.

📖 Example Report Structure

Here's what a generated report looks like:

## Title

- **Overview** Brief introduction of the topic.

- **Importance** Why is this topic significant now?

### Section 1
- **Detail 1**
- **Detail 2**
- **Detail 3**

### Section 2
- **Detail 1**
- **Detail 2**
- **Detail 3**

### Section 3
- **Detail 1**
- **Detail 2**
- **Detail 3**

## Conclusion
- **Summary of report:** Recap of the key findings from the report.
- **Implications:** What these findings mean for the future.

## References
- [Reference 1](Link URL to Source)
- [Reference 2](Link URL to Source)

📬 Contributions & Issues

Feel free to contribute! Fork the repo, make your changes, and send a pull request. For issues or questions, open a ticket or hit me up directly.

Acknowledgement

Use this project for learning not just copy pasting the repo for a hackathon, though you might win the hackathon but sure will never learn how it works.

📜 License

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

Happy researching! 🎓🧐