Project Structure

I refered this article: https://medium.com/aingineer/a-comprehensive-guide-to-implementing-modular-rag-for-scalable-ai-systems-3fb47c46dc8e

modular_rag/
├── src/
│   ├── retrieval/      # Document retrieval module
│   ├── reasoning/      # Reasoning module
│   ├── generation/     # Response generation module
│   ├── pipeline/       # Pipeline orchestration
│   └── utils/          # Utility functions
├── data/
│   ├── documents/      # Input documents
│   └── vector_store/   # Vector store for document retrieval
├── config/             # Configuration files
├── tests/              # Test files
├── scripts/            # Utility scripts
├── Dockerfile          # Docker configuration
├── requirements.txt    # Python dependencies
└── README.md           # Project documentation

Modular RAG

A scalable implementation of Retrieval-Augmented Generation (RAG) based on modular architecture.

Setup

1. Install dependencies: pip install -r requirements.txt
2. Run the pipeline: python scripts/run_pipeline.py

Structure

• src/: Core modules (retrieval, reasoning, generation, pipeline)
• data/: Documents and vector store
• config/: Configuration files
• tests/: Unit tests
• scripts/: Utility scripts