LLMS Workshop
Workshop Notebooks

Workshop Notebooks

Notebooks for this workshop are available in the repository https://github.com/nishad/llm-workshop-notebooks.

Notebooks

1. 01_using-ollama-with-python.ipynb

This notebook introduces the Ollama Python library and provides a step-by-step guide on:

  • Installing and setting up Ollama.
  • Interacting with models for text generation and embedding creation.
  • Retrieving and understanding metadata of available models.
  • Streaming dynamic Markdown responses.

2. 02_introduction-to-rag-with-ollama.ipynb

This notebook demonstrates how to implement a Retrieval-Augmented Generation (RAG) pipeline using:

  • ChromaDB for embedding storage and retrieval.
  • Jinja2 for creating structured prompts.
  • Ollama for embedding generation and context-aware responses.

Topics covered include:

  • Setting up a persistent vector database to avoid re-indexing data.
  • Retrieving relevant paragraphs for context-aware prompts.
  • Comparing model outputs with and without external knowledge augmentation.

How to Use

  1. Clone the repository: 
    git clone https://github.com/nishad/llm-workshop-notebooks
  2. Navigate to the repository: 
    cd llm-workshop-notebooks
  3. Install required packages: 
    pip install jupyter
  4. Open the notebooks in Jupyter Notebook or JupyterLab: 
    jupyter notebook
  5. Follow the notebooks:
    • Start with 01_using-ollama-with-python.ipynb for basic concepts.
    • Proceed to 02_introduction-to-rag-with-ollama.ipynb for advanced RAG workflows.

Resources

License

This repository is licensed under the MIT License. The dataset used in the examples is sourced from “Made with Creative Commons” and is licensed under Creative Commons Attribution.

RAG in LLMs