📄 PDF-Based Information Retrieval System (Gemini + Streamlit)

This is an interactive web application built with Streamlit that allows users to upload one or more PDF files and ask questions about their content using Google's Gemini large language model. It performs document chunking, semantic embedding, and conversational retrieval to give accurate answers based on the uploaded documents.

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🚀 Features

• Upload multiple PDF files.
• Extract and chunk document text.
• Embed content using GoogleGenerativeAIEmbeddings (Gemini).
• Use a conversational interface powered by Gemini (ChatGoogleGenerativeAI).
• Memory-enabled Q&A for multi-turn conversations.

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🛠️ Technologies Used

• Python 3.9
• Streamlit
• Langchain
• Google Generative AI (Gemini)
• FAISS (Vector Store)
• PyPDF2

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📦 Setup Instructions

1. Clone the repository

git clone https://github.com/your-username/inf-retrieval-gemini.git
cd inf-retrieval-gemini

2. Create and activate a Conda environment (Python 3.9)

conda create -n genai python=3.9
conda activate genai

3. Install dependencies

pip install -r requirements.txt

4. Set your API key

GOOGLE_API_KEY=your_google_genai_api_key

You can get your API key from: https://makersuite.google.com/app/apikey

▶️ How to Run

streamlit run app.py

🖥️ How to Use

Open the web UI in your browser.

Upload one or more PDF files using the sidebar.

Click "Submit & Process" to extract, chunk, and embed the content.

Ask your questions in the input box and get responses powered by Gemini.

📜 License

MIT License. You are free to use, share, and adapt the code.