InterSystems IRIS Vector Search

This year, we're adding a powerful Vector Search capability to the InterSystems IRIS Data Platform, to help you innovate faster and build intelligent applications powered by Generative AI. At the center of the new capability is a new VECTOR native datatype for IRIS SQL, along with similarity functions that leverage optimized chipset instructions (SIMD).

Demos

In the demos folder, sql_demo.ipynb, langchain_demo.ipynb, llama_demo.ipynb are built on the community sqlalchemy python package

sql_dbapi_demo.ipynb uses the official dbapi package.

The python .whl file is provided in the repo. It can also be downloaded here.

InterSystems IRIS Vector Search Quickstart

1. Clone the repo

   git clone https://github.com/intersystems-community/hackathon-2024.git

2. Install IRIS Community Edtion in a container:

   docker run -d --name iris-comm -p 1972:1972 -p 52773:52773 -e IRIS_PASSWORD=demo -e IRIS_USERNAME=demo intersystemsdc/iris-community:latest

   ℹ️ After running the above command, you can access the System Management Portal via http://localhost:52773/csp/sys/UtilHome.csp. Please note you may need to configure your web server separately when using another product edition.

3. Create a Python environment and activate it (conda, venv or however you wish) For example:

   conda:

   conda create --name iris-env python=3.10
   conda activate

   or

   venv (Windows):

   python -m venv iris-env
   .\iris-env\Scripts\Activate

   or

   venv (Unix):

   python -m venv iris-env
   source ./iris-env/bin/activate

4. Install packages for all demos:

   pip install -r requirements.txt

5. Install Intersystem's DB API connector (run this from the root of the repo):

   pip install intersystems_irispython-3.2.0-py3-none-any.whl 

6. 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
   

Using the Management Portal

1. Navigate to http://localhost:52773/csp/sys/UtilHome.csp, login with username: demo, password: demo (or whatever you configured)
2. Change the namespace (on the top left) from %SYS to USER
3. On the left navigation pane, click 'System Explorer'
4. Click 'SQL' -> 'Go'
5. Here, you can execute SQL queries. You can also view the tables by clicking the relevant table on the left, under 'Tables', and then clicking 'Open Table' (above the SQL query box)

Basic Demos

sql_demo.ipynb

IRIS SQL now supports vector search (with other columns)! 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 search with filters, use IRIS SQL. This is the most flexible way to build RAG.

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_index.

The fastest and easiest way to contact any InterSystems Mentor is via Slack or Discord - feel free to ask any questions about our technology, or about your project in general!

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