🤖 Indra AI — RAG Chatbot using Streamlit, Qdrant & OpenAI

Indra AI is a Retrieval-Augmented Generation (RAG) chatbot that allows users to:

• Ask natural language questions
• Upload PDF/Text files for context
• Retrieve Wikipedia summaries
• Get accurate answers using OpenAI embeddings + vector search

Built with Streamlit UI, Qdrant Vector DB, and OpenAI embeddings.

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🚀 Features

• RAG-based question answering
• PDF/Text document support
• Wikipedia knowledge fetching
• Vector search using Qdrant/FAISS
• Clean Streamlit interface
• Secure .env API key handling

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📁 Project Structure

Indra_AI/
│── rag_backend/
│   ├── app.py
│   ├── main.py
│   ├── embedder.py
│   ├── generator.py
│   ├── evaluation.py
│   ├── pdf_loader.py
│   ├── vector.py
│   ├── wikipedia_loader.py
│   ├── requirements.txt
│
├── .env.example
├── .gitignore
├── README.md

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⚙️ Installation

1. Clone the repository

git clone https://github.com/arshdeepsarsh/Indra_AI.git
cd Indra_AI

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2. Create a virtual environment

python -m venv venv

Activate:

Windows

venv\Scripts\activate

Mac/Linux

source venv/bin/activate

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3. Install dependencies

pip install -r rag_backend/requirements.txt

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4. Add your API key

Create a .env file:

OPENAI_API_KEY=your_api_key_here

(Or copy from .env.example)

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▶️ Run the Application

Streamlit UI

streamlit run rag_backend/app.py

CLI Mode

python rag_backend/main.py

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🧠 How It Works (RAG Pipeline)

User Query / Document Upload
          ↓
Text Chunking
          ↓
Embedding Generation (OpenAI)
          ↓
Vector Storage (Qdrant / FAISS)
          ↓
Similarity Search
          ↓
Response Generation

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🛣️ Roadmap

• Chat history
• Qwen/Gemini model support
• Voice input
• Deployment on Streamlit Cloud
• Multi-document support

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🤝 Contributing

Pull requests are welcome.
Please open an issue to discuss new features or improvements.

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License

MIT License

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⭐ Support

If you find this project useful, consider giving it a ⭐ on GitHub!