workshop-meetup-rag

Workshop to create a RAG application using LLM model and Streamlit.

This workshop is developed in Python 🐍 (Streamlit) and InterSystems IRIS.

The main purpose is to show you the main steps to create a RAG application using an LLM and a vector database.

You can find more in-depth information in https://learning.intersystems.com.

What do you need to install?

• Git
• Docker (if you are using Windows, make sure you set your Docker installation to use "Linux containers").
• Docker Compose
• Visual Studio Code + InterSystems ObjectScript VSCode Extension

Setup

Build the image we will use during the workshop:

Clone the repository:

git clone https://github.com/intersystems-ib/workshop-meetup-rag
cd workshop-meetup-rag

Build the image:

docker compose build

Run the containers:

docker compose up -d

After running the containers, you should be able to access to:

• InterSystems IRIS Management Portal. You can login using superuser / SYS
• Jupyter Notebook

Create Streamlit application

There is one example you can try to build and modify in your local environment.

First of all, create a new environment and install some requirements:

# create a local venv environment
python3 -m venv .venv

# activate venv
source .venv/bin/activate

# install dependencies
pip3 install -r requirements.txt

Create an .env file for storing API keys for OpenAI / MistralAI. They will be used in the applications.

OPENAI_API_KEY="your-api"
MISTRAL_API_KEY="your-api"

## Streamlit Assistant There is also a great example of a langchain / streamlit chatbot assitant in https://alejandro-ao.com/how-to-use-streaming-in-langchain-and-streamlit/

You can play with it here as well:

cd python/assitant
streamlit run chatbot.py

Then open http://localhost:8501 and have a look at it.

Are you able to add the logic to reproduce the Medicine Leaflet example in the assitant ?