InterSystems @ treehacks-2024

InterSystems is proud to be a sponsor of Treehacks 2024!

We are excited to introduce the hordes of hackers gathered to InterSystems IRIS and we're offering great cash prizes for the brave souls that take up the InterSystems Treehacks Challenge

What is InterSystems IRIS and IRIS Vector Search?

InterSystems IRIS is a rock-solid wicked-fast swiss-army-knife data platform that you can run anywhere - on-premises, your cloud, or use our Cloud Services! We are launching new Vector Search capabilities to seamlessly power your unstructured data search and Gen AI Retrieval Augmented Generation (RAG) applications in the same platform where you manage the rest of your data -- transactions, documents, analytics, etc.!

InterSystems IRIS Vector Search Quickstart

1. Clone the repo

   https://github.com/alvin-isc/treehacks-2024.git
   

   In the following steps, replace 'PATH-TO-REPO' with the path to the repo.

2. Install IRIS (Community Edtion) in a container:

   docker run -d --name iris-comm -p 1972:1972 -p 52773:52773 intersystemsdc/iris-community:2024.1-preview
   

   • Access the System Management Portal via http://localhost:52773/csp/sys/UtilHome.csp (user: SUPERUSER, pw: SYS)
   • Change the password as prompted. The demo notebooks use a password of 'SYS2'.

3. Create a python environment (conda, venv or however you wish) For example:

   conda create --name treehacks-iris python=3.10
   

4. Install packages for all demos:

   pip install -r requirements.txt
   

5. For langchain_demo.ipynb and llama_demo.ipynb , you need an OpenAI API Key. Create a .env file in this repo to store the key:

   OPENAI_API_KEY=xxxxxxxxx
   

Basic Demos

sql_demo.ipynb

IRIS SQL now supports vector search (with filters)! In this demo, we're searching a whiskey dataset for whiskeys that are priced < $100 and have a taste description similar to "earthy and creamy taste".

langchain_demo.ipynb

IRIS now has a langchain integration as a VectorDB! In this demo, we use the langchain framework with IRIS to ingest and search through a document.

llama_demo.ipynb

IRIS now has a llama_index integration as a VectorDB! In this demo, we use the llama_index framework with IRIS to ingest and search through a document.

Which to use?

If you need to use hybrid search (similarity search with filters), use IRIS SQL.

If you're building a genAI app that uses a variety of tools (agents, chained reasoning, api calls), go for langchain.

If you're building a RAG app, go for llama_indx.

Feel free to contact Alvin / Thomas if you have any questions!

More Demos / References:

NLP Queries on Youtube Audio Transcription

Uses langchain-iris to search Youtube Audio transcriptions

langchain-iris demo

Original IRIS langhain demo, that runs the containerized IRIS in the notebook

llama-iris demo

Original IRIS llama_index demo, that runs the containerized IRIS in the notebook

InterSystems Documentation

Official page for InterSystems Documentation