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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Build a RAG (Retrieval Augmented Generation) pipeline from scratch and have it all run locally.
| Date | Stars |
|---|---|
| 2026-07-31 | 1003 |
| 2026-08-06 | 1006 |
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# Simple Local RAG Tutorial Local RAG pipeline we're going to build:  All designed to run locally on a NVIDIA GPU. All the way from PDF ingestion to "chat with PDF" style features. All using open-source tools. In our specific example, we'll build NutriChat, a RAG workflow that allows a person to query a 1200 page PDF version of a Nutrition Textbook and have an LLM generate responses back to the query based on passages of text from the textbook. PDF source: https://pressbooks.oer.hawaii.edu/humannutrition2/ You can also run notebook `00-simple-local-rag.ipynb` directly in [Google Colab](https://colab.research.google.com/github/mrdbourke/simple-local-rag/blob/main/00-simple-local-rag.ipynb). TODO: - [ ] Finish setup instructions - [x] Make header image of workflow - [ ] Add intro to RAG info in README? - [ ] Add extensions to README - [x] Record video of code writing/walkthrough - DONE, follow along with each line of code on YouTube: https://youtu.be/qN_2fnOPY-M ## Getting Started Two main options: 1. If you have a local NVIDIA GPU with 5GB+ VRAM, follow the steps below to have this pipeline run locally on your machine. 2. If you don’t have a local NVIDIA GPU, you can follow along in Google Colab and have it run on a NVIDIA GPU there. ## Prerequisites - Comfortable writing Python code. - 1-2 beginner machine learning/deep learning courses. - Familiarity with PyTorch, see my [beginner PyTorch video](https://youtu.be/Z_ikDlimN6A?si=NIkrslkvHaNdlYgx) for more. ## Setup Note: Tested in Python 3.11, running on Windows 11 with a NVIDIA RTX 4090 with CUDA 12.1. ### Clone repo ``` git clone https://github.com/mrdbourke/simple-local-rag.git ``` ``` cd simple-local-rag ``` ### Create environment ``` python -m venv venv ``` ### Activate environment Linux/macOS: ``` source venv/bin/activate ``` Windows: ``` .\venv\Scripts\activate ``` ### Install requirements ``` pip install -r requirements.txt ``` **Note:** I found I had to install `torch` manually (`torch` 2.1.1+ is required for newer versions of attention for faster inference) with CUDA, see: https://pytorch.org/get-started/locally/ On Windows I used: ``` pip3 install -U torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 ``` ### Launch notebook VS Code: ``` code . ``` Jupyter Notebook ``` jupyter notebook ``` **Setup notes:** * If you run into any install/setup troubles, please leave an issue. * To get access to the Gemma LLM models, you will have to [agree to the terms & conditions](https://huggingface.co/google/gemma
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Daniel Bourke
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f067a55179b4ae9e, desc:retrieval-augmented generation, desc:retrieval augmented