📄 Chat with Multiple PDFs using LangChain & Google Gemini

Build an AI-powered chatbot that can intelligently answer your questions from multiple PDF documents using Google Gemini, FAISS vector storage, and LangChain — all wrapped in a beautiful Streamlit interface.

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🚀 Features

• 🔍 Multi-PDF Support – Upload and query across multiple PDF documents
• 💡 Google Gemini Integration – Accurate, intelligent answers from a powerful LLM
• ⚡ FAISS Vector Store – Fast document similarity search
• 🧠 LangChain QA Chain – Efficient retrieval-augmented generation
• 🌐 Streamlit Interface – Simple, user-friendly front-end to interact with the chatbot

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📋 Project Flow

Below is the structured flow of the project:

📁 Project Structure

├── streamlit_app.py           # Streamlit app entry point
├── requirements.txt           # Dependencies
├── .streamlit/config.toml     # Streamlit theme config (optional) (In your local drive)
├── faiss_index/               # Local vector index (In your local drive)
├── docenv/                    # Virtual Env (In your local drive)
├── .env                       # .env {For loading all keys and nessessary} (In your local drive)
└── README.md

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🛠️ Installation

1. Clone the Repository

git clone https://github.com/AritraOfficial/Chat_With_Multiple_PDFs.git
cd Chat_With_Multiple_PDFs

2. Set Up Environment

Make sure you’re using Python 3.9+. {MAKE SURE THE PY Version I Used 3.13.1}

pip install -r requirements.txt

3. Configure API Key

Set your Google Gemini API key:

export GOOGLE_API_KEY="your_api_key_here"

Or create a .env file:

GOOGLE_API_KEY=your_api_key_here

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▶️ Run the App

streamlit run app.py

The app will open in your browser. Upload PDFs and ask questions!

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📸 Computer's View

Upload PDFs	Ask Questions	Get Answers

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📸 Mobile's View

Upload PDFs	Ask Questions	Get Answers

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🔧 Key Technologies

• Google Generative AI (Gemini Pro or Flash)
• LangChain
• FAISS
• Streamlit
• Python

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🧠 Use Cases

• 📚 Academic research assistant
• 🏥 Clinical document analysis
• ⚖️ Legal contract understanding
• 📊 Business document summarization

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💡 Future Improvements

• Chat history & memory (Already Have But not in this version)
• Support for other document types (Word, CSV, etc.)
• Upload via cloud storage

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📜 License

This project is licensed under the MIT License. Feel free to use, modify, and share.

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🙌 Acknowledgments

Special thanks to the creators of Google, Streamlit, and Github for the powerful tools that made this project possible.

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📧 Contact

For queries or collaborations, feel free to connect:

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