Vector-inside-IRIS

This is an attempt to run a vector search demo completely in IRIS
There are no external tools and all you need is a Terminal / Console and the management portal.
Special thanks to Alvin Ryanputra
as his package iris-vector-search that was the base
of inspiration and the source for test data.
My package is based on IRIS 2024.1 release and requires attention to your processor capabilities.

I attempted to write the demo in pure ObjectScript.
Only the calculation of the description_vectoris is done in embedded Python.
Calculation of a vector with 384 dimensions over 2247 records takes time.
In my Docker container, it was running 01:53:14 to generate them completely.
So I adjusted this step to be reentrant to allow pausing generation.
Every 50 records you get an offer to have a stop.

Any suggestions for enhancements are very welcome,

Prerequisites

Make sure you have git and Docker desktop installed.

Installation

Clone/git pull the repo into any local directory

$ git clone https://github.com/rcemper/Vector-inside-IRIS.git

To build and start the container run:

$ docker compose up -d && docker compose logs -f

To open IRIS Terminal do:

$ docker-compose exec iris iris session iris
USER>

or using WebTerminal

http://localhost:42773/terminal/

To access IRIS System Management Portal

http://localhost:42773/csp/sys/UtilHome.csp

How to use it

From terminal just start

USER>do ^A.DemoV
 Test Vector Search

=============================

1 - Initialize Tables

2 - Generate Data

3 - VECTOR_COSINE

4 - VECTOR_DOT_PRODUCT

5 - Create Scotch

6 - Load Scotch.csv

7 - generate VECTORs

8 - VECTOR search

Select Function or * to exit : 8
 Default search:

Let's look for a scotch that costs less than $100,

and has an earthy and creamy taste

change price limit [100]: 50

change phrase [earthy and creamy taste]: earthy
calculating search vector
 Total below $50: 222

ID      price   name

1990    40      Wemyss Vintage Malts 'The Peat Chimney,' 8 year old, 40%

1785    39      The Famous Jubilee, 40%

1868    40      Tomatin, 15 year old, 43%

2038    45      Glen Grant, 10 year old, 43%

1733    29      Isle of Skye, 8 year old, 43%
5 Rows(s) Affected

You see the basic functionalities of Vectors in steps 1..4
Steps 5..8 are related to the search example I borrowed from Alvin
Step 6 (import of test data) is straight ObjectScript
SQL LOAD DATA was far too sensible for irregularities in the input CSV

I suggest following the examples also in MGMT portal to watch how Vectors operate.

Article in DC

GitHub