Knowledge-Retriever-AI 📚🤖

A powerful Retrieval-Augmented Generation (RAG) application that lets you upload PDF documents and have intelligent conversations about their content. Built with Streamlit, LangChain, and Google Gemini AI.

✨ Features

• 📄 PDF Upload & Processing - Upload any PDF document through the web interface
• 🧠 Intelligent Q&A - Ask questions about your document content in natural language
• 💬 Conversational Memory - Maintains context across questions (example - remembers "she", "it", "that company")
• ⚡ Real-time Chat Interface - Modern chat UI
• 🗑️ Easy Reset - Clear conversation history or upload new documents anytime
• 🔒 Local Processing - Your documents are processed locally for privacy

🚀 Quick Start

Prerequisites

• Python 3.8+
• Google API Key (for Gemini AI)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/Knowledge-Retriever-AI.git
   cd Knowledge-Retriever-AI

2. Create virtual environment

   python -m venv venv
   
   # Windows
   venv\Scripts\activate
   
   # macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables

   # Create .env file
   echo "GOOGLE_API_KEY=your_google_api_key_here" > .env
   echo "HUGGINGFACE_API_TOKEN=your_huggingface_token_here" >> .env

5. Run the application

   streamlit run rag_app.py

6. Open your browser to http://localhost:8501

🔑 Getting API Keys

Google Gemini API Key

1. Go to Google AI Studio
2. Create a new API key
3. Copy the key to your .env file

HuggingFace Token (Optional)

1. Go to HuggingFace Settings
2. Create a new token
3. Copy the token to your .env file

📖 How to Use

1. Upload a PDF - Click "Choose a PDF file" in the sidebar
2. Process Document - Click "Process PDF" to analyze the document
3. Start Chatting - Ask questions in the chat input at the bottom
4. Continue Conversation - Ask follow-up questions using pronouns like "she", "it", "that"

Happy Knowledge Retrieving! 🎉