📂 RAG Application with Langchain

A Retrieval-Augmented Generation (RAG) application built with Streamlit and Langchain, powered by Google's Gemini AI models. This application allows users to upload documents and interact with them through intelligent Q&A.

🌟 Features

• Document Upload: Support for PDF, DOCX, and TXT file formats
• Smart Document Processing: Automatic text extraction and processing
• AI-Powered Q&A: Chat with your documents using Google's Gemini 1.5 Flash model
• Vector Embeddings: Efficient document search using Google's embedding models
• User-Friendly Interface: Clean and intuitive Streamlit web interface

🚀 Technologies Used

• Streamlit: Web application framework
• Langchain: LLM application framework
• Google Gemini AI: Large language model (gemini-1.5-flash)
• Google Generative AI Embeddings: Document embedding model (embedding-001)
• Python: Programming language

📋 Prerequisites

Before running this application, make sure you have:

• Python 3.7 or higher
• Google API key for Gemini AI
• Required Python packages (see Installation)

🛠️ Installation

1. Clone the repository

git clone https://github.com/PTHARRISH/rag_chatbot.git
cd rag-langchain-app

2. Install required packages

pip install streamlit langchain google-generativeai

3. Set up your Google API key

- Get your API key from [Google AI Studio](https://makersuite.google.com/app/apikey)
- Set it as an environment variable or add it to your code

🔧 Usage

1. Run the application

streamlit run app.py

2. Upload a document

• Click on the file uploader
• Select a PDF, DOCX, or TXT file
• Wait for the file to be processed

3. Start chatting

• Ask questions about your uploaded document
• Get AI-powered answers based on the document content

📁 Project Structure

rag-langchain-app/
│
├── app.py # Main Streamlit application
├── requirements.txt # Python dependencies
├── README.md # Project documentation
└── temp # Temporary uploaded files (auto-generated)

🔑 Environment Variables

Create a `.env` file in the root directory:

GOOGLE_API_KEY=your_google_api_key_here

📝 Code Overview

The application uses:

• ChatGoogleGenerativeAI for conversational AI capabilities
• GoogleGenerativeAIEmbeddings for document vectorization
• Streamlit for the web interface
• File handling for document processing

Note: This is a learning project created following a YouTube tutorial. Feel free to fork and enhance it further!