RAG Question Answering System

A powerful Retrieval Augmented Generation (RAG) application that combines real-time web search, Wikipedia knowledge, and local language models to provide comprehensive answers to user questions.

🚀 Features

• Multi-Source Knowledge Retrieval: Integrates Google Search and Wikipedia for comprehensive information gathering
• Local LLM Processing: Uses Ollama with Llama 3.1 for intelligent response generation
• Vector Database Storage: Implements Chroma DB with NVIDIA embeddings for semantic search
• Real-time Streaming: Provides live response generation with custom UI feedback
• Persistent Knowledge Base: Maintains conversation history and learned information
• User-Friendly Interface: Clean Streamlit web application with expandable output containers

🛠️ Prerequisites

Before running this application, ensure you have:

• Python 3.8 or higher
• Anaconda distribution installed
• Ollama installed and running
• Google Serper API key
• NVIDIA API key
• At least 8GB RAM (recommended for local LLM)

📦 Installation

1. Clone the Repository

git clone https://github.com/yourusername/rag-qa-system.git
cd rag-qa-system

2. Create a conda Environment

conda create -n env_name python=3.8
conda activate env_name

3. Install Dependencies

pip install -r requirements.txt

4. Install and Setup Ollama

# Install Ollama (visit https://ollama.ai/ for OS-specific instructions)
# After installation on your machine, run
ollama --version
# Pull the required model
ollama pull llama3.1

5. Setup API Keys

Edit the file named id.py in the project root directory:

# id.py
SERPER_API_KEY = "your_serper_api_key_here"
nvapi_key = "your_nvidia_api_key_here"

🔑 API Key Setup

Google Serper API

1. Visit Serper.dev
2. Sign up for a free account
3. Get your API key from the dashboard
4. Add it to your id.py file

NVIDIA API

1. Visit NVIDIA AI Foundation Models
2. Sign up and get your API key
3. Add it to your id.py file

🚀 Usage

1. Start the Application

streamlit run RAG.py

2. Access the Interface

Open your browser and navigate to http://localhost:8501

3. Ask Questions

• Enter your question in the text input field
• Watch as the system retrieves information and generates answers in real-time
• Expand the output container to see the complete response

📁 Project Structure

rag-qa-system/
├── app.py                 # Main application file
├── id.py                  # API keys configuration
├── requirements.txt       # Python dependencies
├── README.md             # Project documentation
├── chroma_db/            # Vector database storage (created automatically)
└── .gitignore           # Git ignore file

🔧 Configuration

Customizing the LLM

To use a different Ollama model, modify the llm initialization in RAG.py:

llm = ChatOllama(model="your-preferred-model", temperature=0, streaming=True)

Adjusting Text Chunking

Modify the text splitter parameters for different chunk sizes:

splitter = CharacterTextSplitter(chunk_size=1500, chunk_overlap=300)

Vector Database Location

Change the persistence directory for the Chroma database:

persist_directory = "./your-custom-db-path"

🐛 Troubleshooting

Common Issues

1. Ollama Connection Error

# Ensure Ollama is running
ollama serve
# Pull the required model
ollama pull llama3.1

2. API Key Errors

• Verify your API keys are correctly set in id.py
• Check API key quotas and permissions

3. ChromaDB Persistence Issues

• Delete the chroma_db folder and restart the application
• Ensure proper write permissions in the project directory

4. Memory Issues

• Reduce chunk size in the text splitter
• Close other memory-intensive applications

🔒 Security Notes

• Never commit your id.py file to version control
• Add id.py to your .gitignore file
• Use environment variables in production deployments

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

🙏 Acknowledgments

• LangChain for the RAG framework
• Ollama for local LLM hosting
• Streamlit for the web interface
• ChromaDB for vector storage
• NVIDIA for embedding models

Note: Make sure to keep your API keys secure and never share them publicly!