nn-rag 2.2.4

Neural Retrieval-Augmented Generation for GitHub code blocks

LLM Retrieval Augmented Generation

_{short alias lrag}

The original version of the NN RAG project was created by Waleed Khalid at the Computer Vision Laboratory, University of Würzburg, Germany.

📖 Overview

A minimal Retrieval-Augmented Generation (RAG) pipeline for code and dataset details.
This project aims to provide LLMs with additional context from the internet or local repos, then optionally fine-tune the LLM for specific tasks.

Requirements

• Python 3.8+ recommended
• Pip or Conda for installing dependencies
• (Optional) GPU with CUDA if you plan to do large-scale training

Installing Dependencies

1. Create and activate a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate   # Linux/Mac
   venv\Scripts\activate      # Windows
   

2. Latest Development Version

Install the latest version directly from GitHub:

pip install git+https://github.com/ABrain-One/nn-rag --upgrade

Usage

Command Line Interface

The package provides a command-line interface for extracting neural network blocks:

# Correct way to run (recommended)
python3 -m ab.rag --help

# Extract a specific block
python3 -m ab.rag --block ResNet

# Extract multiple blocks
python3 -m ab.rag --blocks ResNet VGG DenseNet

# Extract from JSON file (default)
python3 -m ab.rag

# Note: Avoid running 'python3 -m ab.rag.extract_blocks' as it may show warnings

Python API

from ab.rag import BlockExtractor, BlockValidator

# Initialize extractor
extractor = BlockExtractor()

# Warm up the index (clones repos and indexes if needed)
extractor.warm_index_once()

# Extract a single block
result = extractor.extract_single_block("ResNet")

# Extract multiple blocks
results = extractor.extract_multiple_blocks(["ResNet", "VGG"])

# Extract from JSON file (uses default nn_block_names.json)
results = extractor.extract_blocks_from_file()

# Extract with limit
results = extractor.extract_blocks_from_file(limit=10)

# Extract with custom JSON file
results = extractor.extract_blocks_from_file("custom_blocks.json")

# Extract with start_from parameter
results = extractor.extract_blocks_from_file(start_from="ResNet", limit=5)

Citation

If you find this pipeline to be useful for your research, please consider citing our articles for extraction of algorithmic logic and architecture design with LLMs:

@article{ABrain.NN-RAG,
  title={A Retrieval-Augmented Generation Approach to Extracting Algorithmic Logic from Neural Networks},
  author={Khalid, Waleed and Ignatov, Dmitry and Timofte, Radu},
  journal={arXiv preprint},
  volume  = {arXiv:2512.04329},
  url = {https://arxiv.org/pdf/2512.04329}, 
  year={2025}
}

@article{ABrain.Architect,
	title={From Memorization to Creativity: LLM as a Designer of Novel Neural-Architectures},
	author={Khalid, Waleed and Ignatov, Dmitry and Timofte, Radu},
	journal={arXiv preprint},
	volume  = {arXiv:2601.02997},
	url = {https://arxiv.org/pdf/2601.02997}, 
	year={2026}
}

The idea and leadership of Dr. Ignatov