📄 DocuQuery – Smart Document Q&A Chatbot

DocuQuery is an AI-powered chatbot that allows users to interact with documents by uploading them and asking questions directly. It supports various file formats, uses large language models (LLMs) for intelligent answers, and presents everything in a ChatGPT-style interface.

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🚀 Features

• 📎 Upload PDF, DOCX, or TXT files
• ✨ Summarize uploaded documents
• 🧠 Extract key entities using NLP
• 💬 Ask questions and get real-time answers
• 🔁 Retains full chat history during session
• 👥 Secure Sign Up / Sign In system
• 📊 Select between LLM models (LLaMA, Bart, etc.)
• 🎨 Clean ChatGPT-like user interface

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🧰 Tech Stack

• Frontend: Streamlit
• Backend: Python
• NLP/LLMs: LangChain, LLaMA, Bart
• Vector Store: FAISS
• Document Parsing: PyMuPDF, python-docx, textract
• Auth: Basic JSON file-based
• Deployment: Streamlit Cloud / Local

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⚙️ Installation

1. Clone the Repository

git clone https://github.com/dakshgoti14/DocuQuery.git
cd DocuQuery

2. Create & Activate Virtual Environment

python3 -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the App

streamlit run app.py

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📁 Project Structure

DocuQuery/
│
├── app.py                      # Main Streamlit app
├── requirements.txt
├── README.md
├── data/
│   └── users.json              # User credentials
├── utils/
│   ├── summarizer.py           # Document summarization logic
│   ├── ner.py                  # Named entity recognition
│   ├── qa_engine.py            # Vector store & QA logic
│   ├── file_handler.py         # File parsing methods
│   └── auth.py                 # Auth functions

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🌐 Deployment Guide

🚀 Deploy to Streamlit Cloud (Free)

Follow these simple steps to deploy your project using Streamlit Cloud:

1. Push your project to GitHub
   Make sure your latest code is on GitHub in a public or private repository.

2. Go to Streamlit Cloud
   Log in with your GitHub account.

3. Click "New App"

   • Select your GitHub repository
   • Choose the correct branch
   • Set app.py as the entry file

4. Click "Deploy"

⚠️ Important Notes: Do NOT commit your venv/ folder. It should be excluded via .gitignore.

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🛠 Tips for GitHub

If you get this error: remote: error: File ... exceeds GitHub’s file size limit

Run:

git rm -r --cached venv/
echo "venv/" >> .gitignore

Then recommit and push:

git add .
git commit -m "Clean up venv"
git push origin main

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📌 Future Roadmap

• 🌍 OAuth or Google login
• 🧾 Multi-document support
• 📤 Export chat history
• 🧠 Add support for OpenAI, Claude, Mistral
• 🖼️ Document content visualization

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🤝 Contributing

1. Fork this repo 2. Create a new branch (git checkout -b feature-name) 3. Make your changes 4. Commit and push (git push origin feature-name) 5. Open a pull request

📄 License

• MIT License – free to use and modify for personal or commercial use.

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👨‍💻 Author

Daksh Goti
GitHub: @dakshgoti14

Feel free to connect or contribute!

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Let me know if you'd like to:

• 🎥 Add a demo video or GIF of the project in action
• 🏷️ Add badges (e.g., Python version, license, framework)
• 🚀 Add a "Deploy to Streamlit" one-click button for easier deployment