advanced_rag_team_twain

Branches

main : for data science development

deployment-path : for deployment

Setup

1. Set up environment and packages

$ python -m venv ragenv

$ source ragenv/bin/activate

$ pip install -r requirements.txt

2. Download dataset

# Make sure you have git-lfs installed (https://git-lfs.com)

$ git lfs install

$ git clone https://huggingface.co/datasets/neural-bridge/rag-dataset-12000

3. Set up Ollama

Download Ollama (see https://ollama.com/)

Download Llama3 $ ollama pull llama3

4. While we recommend using Llama3 via Ollama, if you wish to use HuggingFace models, please set your HuggingFace API token. os.environ['HUGGINGFACEHUB_API_TOKEN'] = 'YOUR TOKEN'

5. Build system and evaluate

python eval.py This will chunk the data, build a Chroma vector store (if using Chroma), and evaluate the RAG system. In the main function, predefined default options for Chroma Dense Retrieval, ES Sparse Retrieval, and ES Dense Retrieval are set up.

6. Walk through examples and demos given in presentation in PresentationExamples.ipynb