Get a foundational Retrieval Augmented Generation pipeline with rag, a reference solution for startup founders, built with Python.
652 stars278 forksPythonUpdated 6/2/2026100% free ยท open source
What it does
Rag provides a foundational pipeline for Retrieval Augmented Generation, allowing startup founders to generate text based on relevant information retrieved from a database or knowledge base.
When to use it
โขYou need to generate user manuals or guides based on existing documentation
โขYou want to create chatbots that provide accurate and up-to-date information
โขYou're building a content generation tool that requires contextual understanding
Quick start
1Clone the NVIDIA RAG repository from GitHub using the command `git clone https://github.com/NVIDIA-AI-Blueprints/rag.git`
2Install the required dependencies by running `pip install -r requirements.txt` in the cloned repository
3Configure the pipeline by modifying the `config.json` file to point to your dataset and knowledge base
4Run the pipeline using the command `python main.py` to generate text based on your configured dataset and knowledge base
Ready-to-paste prompt
python main.py --config config.json --input "What is the capital of France?" --output generated_text.txt
Saves to your device
Topics
blueprint
nim
rag
retrieval-augmented-generation
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