RAG System with OpenAI and Streamlit 🤖📄

Welcome to the RAG System (Retrieve and Generate) — an innovative AI-powered chatbot that leverages the OpenAI Assistant API and Streamlit to provide real-time, context-aware answers based on user-uploaded documents.

Features ✨

• Multiformat Document Support: Upload and process various file types including PDFs, DOCX, PPTX, TXT, and script files. 📂
• Real-time Responses: Enjoy seamless and interactive responses fetched via WebSocket connections. 🔄
• Context Aware: Provides answers based on the content of the uploaded documents, making the interactions more meaningful and personalized. 🔍

Installation ⚙️

To get started with the RAG system, follow these steps:

1. Clone the Repository:

   git clone https://github.com/SuperGalaxy0901/Streamlit-OpenAI-Chatbot.git
   cd rag-system

2. Set up a Virtual Environment (recommended):

   python -m venv env
   source env/bin/activate  # On Windows use `env\Scripts\activate`

3. Install the Required Packages:

   pip install -r requirements.txt

4. Set Up Environment Variables:

   • Create a .env file to safely store your API keys and configuration settings.

     OPENAI_API_KEY=your_openai_api_key
     

Usage 🚀

1. Run the Streamlit App:

   streamlit run app.py

2. Interact with the Chatbot:

   • Upload documents via the application interface. 📤
   • Engage with the chatbot as it generates insightful responses based on your document contents. 💬

Architecture Overview 🏗️

• Streamlit: Provides the front-end interface where users can upload documents and interact with the chatbot. 🌐
• OpenAI Assistant API: Powers the natural language comprehension and generation. 🧠
• WebSockets: Enables real-time, efficient communication between the front-end and back-end services. 📡

Acknowledgements 🙏

• OpenAI for their incredible API.
• Streamlit for the easy-to-use app framework.