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RAG & Knowledge Systems

LLM Augmentation

LLM Augmentation is a public repository of tutorial Jupyter notebooks for experimenting with custom-data retrieval and function calling in LLM applications. Its lessons cover embeddings, vector databases, RAG and LangChain, with later project material on Streamlit front ends.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

LLM Augmentation — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the repository when you want notebook-based learning or a starting point for experimenting with retrieval and function-calling patterns in LLM applications.

Who it is for

  • Learners exploring embeddings, vector databases and retrieval-augmented generation.
  • Developers prototyping LLM applications that use custom information or invoke functions.

Documented uses

  • Follow hands-on lessons about embeddings, vector search and RAG.
  • Explore how an LLM application can retrieve relevant passages from custom data.
  • Prototype function-calling patterns and related LangChain lessons.
  • Use the project material as a starting point for a Streamlit front end.
Before you choose

Requirements & limits

The repository is public. The README describes local notebook use and names Google Colab and Kaggle as cloud alternatives. The notebooks use OpenAI API keys; the README describes key handling but does not state a price.

  • The repository provides instructional notebooks rather than a hosted end-user application.
  • OpenAI API keys are part of the documented workflow, and some deliverables may require other keys.
  • The README recommends local use, with Google Colab or Kaggle as alternatives when local setup is challenging.
  • Repository metadata does not state a software license.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up API keys for the tutorial notebooks

Prepare an OpenAI API key for use from a local notebook environment.

Before you start

  • A local development environment for the notebooks
  • An OpenAI API key
  • Python and the dotenv library

Steps

  1. Clone the repository and open its notebooks in your local development environment.

  2. Set API key environment variables directly or place them in a local .env file.

  3. Use python-dotenv's load_dotenv() to load the .env values into the notebook environment.

  4. Read the key from an environment variable where needed; do not hardcode it or commit the .env file.

Expected outcome

The notebook environment can read the configured API key through an environment variable.

Guide limitations

  • The README does not provide a complete environment installation procedure.
  • Additional API keys may be needed for some deliverables.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
18 October 2026
Original website title
MichiganDataScienceTeam/llm-augmentation · Official homepage title · retained in its source language
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