RAGnet is an AI-powered document question-answering system that uses Retrieval Augmented Generation (RAG) to provide accurate responses based on the content of uploaded PDF documents.

• PDF document ingestion and processing
• Semantic chunking for optimal text splitting
• Vector storage using Qdrant for efficient retrieval
• Hugging Face language model integration (google/gemma-2-27b quantized to fp4)
• Reranking and chain filtering options for improved accuracy
• Session-based chat history management

1. Clone the repository:

   git clone https://github.com/Vinnu124/RAGnet
   cd RAGnet

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   • Get the QDrant URL to your cluster using https://qdrant.to/cloud
   • Create a .env file in the project root which will contain the QDrant Link.
   • Add QDRANT_URL variable pointing to the project directory.

Run the main application:

python main.py path/to/pdf1.pdf path/to/pdf2.pdf

To exit the application, type exit or quit.

Adjust settings in config.py:

• Model parameters (embeddings, reranker, language model)
• Retriever settings (use of reranker, chain filter)
• Database and file paths

• main.py: Entry point of the application
• config.py: Configuration settings
• ingestor.py: Document processing and vector store creation
• retriever.py: Document retrieval logic
• model.py: Language model and embedding configurations
• chain.py: Question-answering chain setup
• session_history.py: Chat history management

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License - see the LICENSE file for details.