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,
Make sure you have git and Docker desktop installed.
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
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.