Vectors
Using Oracle Vector Search to find Similar Wines – Part 1
Vector search lets you find items that are semantically similar rather than just matching keywords, by representing text as high-dimensional numeric vectors. We’ve seen lots of examples of using Vector Search to perform various different types of Similarity Searches. In this post and the following posts, I’ll walk through a little example of using Vector Search to find similar wines.
This is Part-1 and it is all about the Set-up and what we need.
There are a few parts to this process and these include:
- Finding a suitable dataset. Yes that is always a challenge, but in this example I’m going to use the Wines 130K dataset from Kaggle. Just like most datasets, it isn’t perfect but is enough to illustrate a real world application of Vector Search.
- We also need a Wine that we will use for testing. In this case I’m going to select a locally grown and produced wine here in Ireland. I’m going to use the Lusca Cabernet Merlot wine. The vines are grown in Lusk, in North County Dublin. They produce a small amount of bottles and these can be typically bought at local Craft/Farmers markets and a few online retailers also stock it.
- We need a suitable ML model we can use for creating Vectors. There are lots out there, but because we are going to illustrate this using the Vector Search feature an Oracle Database, we can use one of the pre-approved or recommended models. See table below. By using one of these we can embed the AI model in the database. That means we don’t need to access and Gen AI models locally, or in out data centre, or via an API to the Gen AI providers. Yes you can do that, but some of those options there are some security and data protection concerns. By using the model in the Database, we don’t have any data movement and everything is secure. For other AI models, check out what is available on Hugging Face.
- The last thing we need is an Oracle Database. For this I’m using Oracle 23.26.2 running in a Docker container. I’m also using Oracle SQL Command Line (SQLcl) for my interface to the Database. It’s a simple command line tool, and yes it keeps things simple. There are a few steps that need to be performed as Administrator of the Database and at the command line in the Docker container.
Let’s get started. I’m assuming you have Oracle Database using somewhere. In my case it is Docker Container and you have downloaded the dataset and the all_MiniLM_L12_v2 model.
Step-1: Load the Data
I’ve created a new schema for this example called vec_user. All the data and work will be performed in this schema.
SQLcl has a very useful Import feature that allows us to import data is a variety of formats. The most common of these is CSV format and the dataset downloaded from Kaggle is in CSV format.
Before we can import the CSV data, we need to create a table for the data. This isn’t strictly needed but it can be useful step to do manually as you can ensure the data types you want to use for each column in the CSV data.
drop table if exists winereviews;CREATE TABLE WINEREVIEWS ("ID" NUMBER(38,0), "COUNTRY" VARCHAR2(300), "DESCRIPTION" VARCHAR2(3000), "DESIGNATION" VARCHAR2(2000), "POINTS" NUMBER(38,0), "PRICE" VARCHAR2(50), "PROVINCE" VARCHAR2(100), "REGION_1" VARCHAR2(200), "REGION_2" VARCHAR2(100), "TASTER_NAME" VARCHAR2(200), "TASTER_TWITTER_HANDLE" VARCHAR2(200), "TITLE" VARCHAR2(2000), "VARIETY" VARCHAR2(300), "WINERY" VARCHAR2(300));
With the table created in our vec_user schema, we can now use the SQLcl IMPORT feature.
-- Load Wine dataset using SQLcl Load featureload winereviews winemag-data-130k-v2.csv
The CSV file needs to be on the Command Line Search Path. If it isn’t then you’ll need to add the path in front for the file name. The data will be loaded into the table in a few seconds.
We can verify the number of records imported and have a look at one of the records.
SQL> select count(*) as num_records from winereviews; NUM_RECORDS ______________ 129971 1 row selected. SQL> -- display one recordSQL> select * from winereviews where rownum = 1; ID COUNTRY DESCRIPTION DESIGNATION POINTS PRICE PROVINCE REGION_1 REGION_2 TASTER_NAME TASTER_TWITTER_HANDLE TITLE VARIETY WINERY _____ __________ _______________________________________________________________________________________________________________________________________________________________________________ _______________ _________ ________ ____________________ ___________ ___________ ________________ ________________________ ____________________________________ ______________ __________ 0 Italy Aromas include tropical fruit, broom, brimstone and dried herb. The palate isn't overly expressive, offering unripened apple, citrus and dried sage alongside brisk acidity. Vulkà Bianco 87 Sicily & Sardinia Etna Kerin O’Keefe @kerinokeefe Nicosia 2013 Vulkà Bianco (Etna) White Blend Nicosia 1 row selected.
Step-2: Download the AI Model and Move it to the Database Container
Download the AI model you are going to use to generate the Vectors. Hugging Face has a collection of these. Oracle provides a subset of these that are compatible with the Machine Learning engine in the Database. That doesn’t mean some of the other AI models won’t work. The table given above lists these supported AI models. I’m going to use the first one, ‘ALL_MINILM_L12_V2’ as it’s general purpose model. Download the AI model ZIP file and unzip it. The ONNX configuration file will be extracted.
To make this AI model accessible to the the Database, we need to move it to the Database Server. As I’m using a Docker Container, I can use the following command to move it to the /tmp directory on the container.
docker cp ./all_MiniLM_L12_v2.onnx <container_name> :/tmp
You’ll need to connect to the SYS database user to grant our VEC_USER read access to the DB_IMPORT directory object with points to the /tmp OS director.
SQL> connect sys/oracle as sysdbaSQL> select * from all_directories;OWNER DIRECTORY_NAME DIRECTORY_PATH ORIGIN_CON_ID ________ ___________________________ _________________________________________________________________ ________________ SYS SDO_DIR_ADMIN /opt/oracle/product/26ai/dbhomeFree/md/admin 1 SYS XMLDIR /opt/oracle/product/26ai/dbhomeFree/rdbms/xml 1 SYS XSDDIR /opt/oracle/product/26ai/dbhomeFree/rdbms/xml/schema 1 SYS ORACLE_BASE /opt/oracle 1 SYS ORACLE_HOME /opt/oracle/product/26ai/dbhomeFree 1 SYS OPATCH_INST_DIR /opt/oracle/product/26ai/dbhomeFree/OPatch 1 SYS DATA_PUMP_DIR /opt/oracle/admin/FREE/dpdump/53103647BA9E10B0E0636402000A338A 1 SYS DBMS_OPTIM_LOGDIR /opt/oracle/product/26ai/dbhomeFree/cfgtoollogs 1 SYS DBMS_OPTIM_ADMINDIR /opt/oracle/product/26ai/dbhomeFree/rdbms/admin 1 SYS OPATCH_SCRIPT_DIR /opt/oracle/product/26ai/dbhomeFree/QOpatch 1 SYS OPATCH_LOG_DIR /opt/oracle/product/26ai/dbhomeFree/rdbms/log 1 SYS JAVA$JOX$CUJS$DIRECTORY$ /opt/oracle/product/26ai/dbhomeFree/javavm/admin/ 1 SYS DB_IMPORT /tmp 3 13 rows selected. SQL> grant read on DB_IMPORT to vec_user;SQL> -- reconnect to the vec_userSQL> conn vec_user/Password1SQL> select * from all_directories;OWNER DIRECTORY_NAME DIRECTORY_PATH ORIGIN_CON_ID ________ _________________ _________________ ________________ SYS DB_IMPORT /tmp 3 1 row selected.
The Database already has a DIRECTORY defined in the Database to point to the ‘/tmp’ directory on the operating system. If I wanted to use a different directory, I’d have to connect to SYS, create a new directory and grant my user read access to this dictionary object.
-- connect to SYScreate directory MY_DIR as '/my_dir';grant read on directory MY_DIR to vec_user;-- connect to VEC_USER-- you should be able to read from the MY_DIR directory
Step-3: Import the AI Model
At this stage we have the ONNX AI model downloaded and unzipped, and we have it copied to the Database server. We should be good to Import the ONNX model to the database.
Using our VEC_USER, we can run the following to import the model. We should have all the necessary privileges (mining mode, read on directory, etc)
begin--- dbms_vector.drop_onnx_model (--- model_name => 'ALL_MINILM_L12_V2',--- force => true); dbms_vector.load_onnx_model ( directory => 'DB_IMPORT', file_name => 'all_MiniLM_L12_v2.onnx', model_name => 'ALL_MINILM_L12_V2');end;/
The above code allows for reproducibility of this import. If the model exists in the database, then drop it and then import it. If you need to rerun the above after importing the model, just uncomment the drop_onnx_model command.
We can check what machine learning models we have access to by running:
-- List available modelselect model_name, algorithm, mining_functionfrom user_mining_models;MODEL_NAME ALGORITHM MINING_FUNCTION ____________________ ____________ __________________ ALL_MINILM_L12_V2 ONNX EMBEDDING 1 row selected.
Step-4: Run some Similarity Searches to see it the AI Model works
Before we move on with processing our Wine dataset with the AI model with generating vectors and performing similarity search, let’s have a look at how we can call or use the imported AI model. The AI model sits in the database and can be executed or run using the in-database machine learning engine. This allows us to keep the data within the database and nothing is sent externally to an LLM or other type of AI engine. Let’s try some basic phrases to see what the AI model creates.
select vector_embedding(all_minilm_l12_v2 using 'A simple test to create a vector using the ONNX model' as data) AS my_vector;MY_VECTOR---------------------------------------------------------------------------------------------------------[-1.1862169E-002,-6.19494878E-002,-5.25733493E-002,3.32576432E-003,-2.87775719E-003,-2.79027559E-002,-5.32365367E-002,3.25470306E-002,-8.14431012E-002,3.27502415E-002,5.79739586E-002,-1.00366184E-002,1.51149062E-002,2.53521837E-002,-5.58732748E-002,-6.06239261E-003,-1.15927346E-002,7.10275471E-002,7.80366957E-002,3.07483934E-002,-5.52542396E-002,-5.33219278E-002,-4.31507863E-002,-3.5467267E-002,1.39131406E-002,7.7143237E-003,9.57045406E-002,1.35890581E-002,2.19696667E-002,-6.06945306E-002,4.66698483E-002,-3.37560987E-003,2.96193287E-002,5.36179841E-002,-2.75602993E-002,-1.70154907E-002,-2.81273387E-002,-1.42529653E-002,-2.23667752E-002,-1.17503572E-002,5.50950877E-002,4.63315211E-002,8.00665375E-003,-2.82223243E-002,-1.89246144E-002,1.13920778E-001,-2.17806976E-002,-6.69694021E-002,6.43107593E-002,2.43396256E-002,-1.84283648E-002,-5.22144362E-002,1.11321002E-001,-3.84567901E-002,1.48841748E-002,4.28449102E-002,2.02198476E-002,6.35366216E-002,-2.03748588E-002,-9.1745086E-002,-8.33614692E-002,-2.99174688E-003,-2.25598831E-002,-5.42856799E-003,4.0825054E-002,4.43562046E-002,-4.10938412E-002,6.19381368E-002,3.28762978E-002,2.11752895E-002,-5.37039116E-002,1.83419809E-002,1.16077727E-002,2.11831694E-003,-3.11171841E-002,-2.77244276E-003,1.74682252E-002,1.13666626E-002,5.24466932E-002,-1.75879449E-002,-2.90773176E-002,5.41730374E-002,5.01469802E-003,-7.33102672E-003,4.24308293E-002,7.88432807E-002,5.29912785E-002,-1.8913582E-002,1.15211476E-002,-3.07812896E-002,2.33124965E-003,4.3144837E-002,-7.58033525E-003,8.82296637E-002,1.10972993E-001,3.85747999E-002,1.52123887E-002,-5.74417002E-002,1.47257121E-002,6.02892786E-002,4.0903978E-002,-2.08789371E-002,-6.04964346E-002,4.23973799E-002,-1.74141806E-002,-4.4276759E-002,4.97439178E-003,-4.89390902E-002,-2.68593691E-002,-1.52082881E-002,-2.85744667E-003,6.27590865E-002,-6.41179234E-002,4.83581051E-002,1.5746966E-002,-1.4682156E-002,6.62061851E-003,3.40981483E-002,-9.90032963E-003,4.55836803E-002,-1.47245992E-002,-5.88329025E-002,-1.0270042E-001,-8.16448033E-002,-4.14121784E-002,-6.95772767E-002,4.12401259E-002,-1.61944497E-002,-9.75468084E-002,1.38579784E-002,1.82065554E-002,-6.35358021E-002,1.58151966E-002,7.94038549E-002,3.20437029E-002,6.94024786E-002,7.46046379E-002,4.92931195E-002,-7.57167637E-002,-2.05017347E-002,-5.73285967E-002,7.74050951E-002,5.69546558E-002,-6.23143315E-002,3.39660607E-002,-4.40245308E-003,-1.35002598E-001,-7.20189959E-002,2.87418142E-002,-1.20503353E-002,2.79477853E-002,-9.56245437E-002,-2.564409E-002,-6.19855663E-003,-3.21824998E-002,1.13457099E-001,-2.09106058E-002,-3.15456204E-002,-6.97289631E-002,6.69988468E-002,-2.67115571E-002,2.73290519E-002,1.22783668E-002,2.11374066E-003,1.38278455E-002,2.11777049E-003,-8.01261291E-002,8.91891345E-002,7.21878186E-002,-5.92943802E-002,4.89146076E-002,3.57595533E-002,-1.38857201E-001,1.44773936E-002,5.73108084E-002,-7.75126275E-003,2.44468488E-002,2.46164459E-003,9.98818874E-003,-3.0055875E-003,-9.52457916E-003,-7.23954439E-002,1.76559724E-002,1.79478135E-002,-9.02936384E-002,-5.99887483E-002,-3.25217471E-002,-1.58283859E-002,-9.94548053E-002,-3.76612768E-002,8.24269354E-002,7.44124316E-003,1.66539177E-002,4.12631407E-002,-2.89798323E-002,-1.37990773E-001,3.47260088E-002,-7.23604709E-002,2.70092972E-002,8.97826441E-003,-7.28023574E-002,-3.08126006E-002,5.07898182E-002,5.59227653E-002,-2.85209883E-002,2.41481867E-002,-3.15275975E-002,2.04243325E-003,2.82701571E-002,8.80170688E-002,-8.94553289E-002,-2.65875738E-002,3.5613589E-002,-5.49732335E-002,-6.30540997E-002,-1.85488667E-002,-1.16195783E-001,-2.97640134E-002,2.53809579E-002,-3.2125283E-002,-1.30758146E-002,-6.06578253E-002,9.31177568E-003,9.08785189E-033,-1.25198409E-001,-2.00347472E-002,9.08883568E-003,4.97741885E-002,5.51343746E-002,-7.35867098E-002,1.44137442E-001,6.84367195E-002,-1.2948221E-001,4.10339199E-002,1.78874657E-002,-2.63292734E-002,-7.93097839E-002,7.32451677E-002,2.88251378E-002,-8.10253341E-003,-7.11302757E-002,4.18880805E-002,4.53449823E-002,-6.01221062E-003,6.79178983E-002,-5.35881892E-003,-5.63834794E-002,4.40003574E-002,2.11469959E-002,4.8730094E-002,1.53696025E-003,3.78657617E-002,-6.92071095E-002,-1.17045911E-002,-1.05027348E-001,5.24917506E-002,-1.25820618E-002,9.27475989E-002,4.75268848E-002,5.95951229E-002,1.38896152E-001,-3.34764384E-002,-2.01537088E-002,5.81902498E-003,7.59647712E-002,3.10984701E-002,-7.96632618E-002,-1.16957002E-003,-3.50703001E-002,-2.83128452E-002,3.17137204E-002,1.92918144E-002,7.28997216E-003,2.61563938E-002,-6.830169E-004,-2.75356174E-002,2.88278628E-002,-2.410769E-003,-2.82850419E-003,-8.91539548E-003,4.09550266E-004,-5.20184785E-002,3.48629914E-002,-9.83022037E-004,-4.82109301E-002,-2.87288055E-002,3.62203047E-002,9.2427142E-002,2.53859498E-002,-7.6168038E-002,4.09572721E-002,2.00462174E-002,3.91589068E-002,-7.17473477E-002,3.18682045E-002,2.80438233E-002,8.44237283E-002,-2.13520881E-003,-1.13178026E-002,-1.36510096E-002,2.30457578E-002,-3.59750614E-002,-2.97435429E-002,3.11401524E-002,-6.6576466E-002,-1.00944526E-001,5.22288866E-002,6.99869469E-002,5.41537106E-002,3.89597155E-002,1.14934787E-001,8.27853009E-002,-6.11951686E-002,6.26713559E-002,5.42582478E-003,1.17276028E-001,-3.35108154E-002,7.20500539E-004,-6.92384914E-002,2.23888651E-032,-8.25942382E-002,1.0263361E-002,4.60346043E-002,2.90820021E-002,3.49532031E-002,-5.21782693E-003,1.33043546E-002,-1.56213745E-001,-3.79306041E-002,-1.71216168E-002,-2.44396739E-002,2.15015635E-002,-3.68581414E-002,4.4896391E-005,4.91990009E-003,6.02074899E-003,-2.57451404E-002,1.79552678E-002,-2.89816465E-002,-9.44156721E-002,4.04688381E-002,5.10384142E-002,-2.82051954E-002,9.17229578E-002,2.05879752E-002,1.91760119E-002,-1.45426579E-002,3.70379053E-002,-1.38869872E-002,3.54876891E-002,3.84136215E-002,3.25987786E-002,9.86970961E-003,2.94941887E-002,-9.09914002E-002,7.22922683E-002,-9.27466433E-003,1.34579856E-002,-3.04076672E-002,6.80207014E-002,-1.35811288E-002,8.4245922E-003,5.90760447E-002,-5.11229271E-003,3.90975364E-002,2.50931103E-002,-2.91317422E-002,-8.91669989E-002,-5.16400263E-002,-2.60937382E-002,3.58074605E-002,3.03873625E-002,7.83548225E-003,7.71026313E-002,6.23333715E-002,9.71396361E-003,-7.08995908E-002,1.22748474E-002,4.20468524E-002,2.13721655E-002,-6.48246184E-002,-1.57039538E-002,-9.04715285E-002,-3.41060348E-002]
The vector contains 384 dimensions.
Let’s have a look at another example.
select vector_embedding(all_minilm_l12_v2 using 'What is a good quality wine' as data) AS my_vector;MY_VECTOR---------------------------------------------------------------------------------------------------------[8.48758966E-003,-8.6496979E-002,-2.83764433E-002,-2.10696012E-002,-8.53050649E-002,-1.40911127E-002,-6.18022904E-002,1.69754717E-002,-1.2954114E-002,-6.90694749E-002,-3.53608765E-002,-2.13439725E-002,-8.13569427E-002,-1.57823078E-002,-2.76144315E-002,3.59890647E-002,9.48644057E-002,5.47600761E-002,3.18817794E-002,3.72690633E-002,-5.204181E-002,-5.40845245E-002,4.28323895E-002,6.13691173E-002,1.27978669E-002,4.7162503E-002,1.46299815E-002,-6.53423294E-002,-3.95153426E-002,5.93141234E-003,-5.01540564E-002,-5.14247902E-002,4.01225425E-002,-4.10377346E-002,-9.46420953E-002,-6.99803531E-002,-3.19304764E-002,-1.17475994E-001,6.24287277E-002,-5.8764508E-003,6.15919828E-002,2.38833278E-002,3.07811219E-002,5.97841777E-002,-7.12923259E-002,-9.45204869E-002,6.4850254E-003,8.36489648E-002,4.84998412E-002,1.07569546E-001,-2.75600981E-002,8.6291872E-002,-4.40733321E-002,-4.95140068E-002,6.01537004E-002,3.07337046E-002,-2.51231454E-002,3.81110869E-002,-7.99637437E-002,3.69612239E-002,-2.88316864E-003,-8.649664E-003,3.24146152E-002,-4.19037882E-003,-1.77016796E-003,3.3910118E-002,-3.95271741E-002,9.23643187E-002,-1.47576213E-001,2.29765121E-002,-5.87550811E-002,-3.02798003E-002,1.85213704E-002,-9.45490692E-003,-5.74432872E-002,-6.74801767E-002,5.22300042E-002,-6.02966174E-002,-9.77811515E-002,1.71270877E-001,5.46088479E-002,-7.74407014E-002,-1.7115524E-002,-6.18977053E-003,9.67943855E-003,-3.82515304E-002,-3.43274884E-002,-5.3462307E-003,-2.57053934E-002,3.8461946E-002,4.52922611E-003,2.56689675E-002,-6.83755474E-003,3.23626362E-002,4.57450151E-002,1.62628904E-001,3.15850265E-002,3.74837965E-002,1.45565858E-002,2.91972514E-002,-3.35903913E-002,7.65944421E-002,7.86963031E-002,-6.14850484E-002,6.81868345E-002,3.69149223E-002,1.64239109E-002,8.27361643E-002,-1.08057745E-002,8.46561268E-002,-7.56146479E-003,-1.76091399E-002,-3.67957875E-002,-9.44054592E-003,-3.34670162E-003,8.53082538E-002,5.10462336E-002,3.06608751E-002,-6.38281256E-002,-6.82414044E-003,1.30195748E-002,6.00180821E-003,1.14564523E-001,-3.65287834E-003,-1.59793515E-002,-1.84295438E-002,2.22749095E-002,4.40397859E-002,-5.9329503E-004,4.17956263E-002,-2.21824367E-002,-1.75340977E-002,4.82484289E-002,2.34650262E-002,-5.26955724E-002,-2.57514659E-002,-2.05297023E-002,-2.17089459E-004,-6.76679239E-002,-2.2591617E-002,-2.69411188E-002,3.98312521E-004,1.20914824E-001,9.40298475E-003,-2.64402013E-002,-3.41484919E-002,-4.44080308E-002,-1.56795774E-002,-6.17562085E-002,-4.11389805E-002,-3.31432815E-003,1.0424844E-002,5.59891351E-002,-5.74715957E-002,6.47083893E-002,5.04113324E-002,3.12482826E-002,-4.80899215E-002,-1.97987147E-002,3.99696231E-002,-3.41557562E-002,-3.5732843E-002,6.90332204E-002,4.48493008E-003,-1.37494877E-001,2.34414525E-002,-2.29555909E-002,1.88858993E-002,1.79906562E-002,1.01220138E-001,1.2918289E-001,6.68326393E-002,-3.34317982E-002,-4.04514605E-003,1.90632418E-003,5.17417826E-002,-1.01933286E-001,7.3630102E-002,-2.04305276E-002,1.10038137E-002,2.08075661E-002,7.53787085E-002,-8.32989961E-002,1.54158296E-002,-1.94807146E-002,5.8314044E-002,-1.37357572E-002,2.32190676E-002,8.57607722E-002,-9.80165671E-004,9.28820577E-003,-6.93036057E-003,-5.53022996E-002,8.65026042E-002,1.02880057E-002,-3.40889171E-002,5.97957894E-003,-4.56773154E-002,-5.15860878E-003,8.13246816E-002,-3.78845744E-002,-1.05865337E-002,7.26584792E-002,-4.63496782E-002,4.93186451E-002,5.03463782E-002,6.83160052E-002,-9.38946009E-003,-1.13420062E-001,4.88068983E-002,-7.79750943E-003,2.02954076E-002,-8.23409483E-002,-8.23013559E-002,-5.31551428E-002,-5.37308864E-003,3.18441242E-002,-5.12061827E-002,-6.79519922E-002,7.09872693E-003,6.45572469E-002,-2.81210393E-002,4.86408733E-002,4.74716928E-033,9.62270573E-002,5.59062883E-002,8.48388672E-002,5.9035521E-002,-1.44827394E-002,-3.57090309E-002,-9.54525992E-002,-1.30636785E-002,2.18042005E-002,4.4574175E-002,1.08921984E-002,6.3118306E-003,-9.96271614E-003,-3.34643945E-002,5.40529564E-002,-4.80750985E-002,5.24901673E-002,8.59874953E-003,-1.92004927E-002,-5.47123253E-002,7.70864496E-003,1.08891085E-001,-4.08197641E-002,1.29449191E-002,-2.16392428E-002,-1.19118195E-003,-4.54484634E-002,-6.9629699E-002,2.56183483E-002,-3.89085859E-002,4.77184393E-002,-2.72601563E-002,-4.19324711E-002,3.15431729E-002,-3.43257301E-002,6.97843134E-002,3.4660209E-002,-3.60485502E-002,-2.67435778E-002,6.15297183E-002,-8.34145583E-003,4.61625531E-002,4.38644784E-003,-3.40693668E-002,-5.74661754E-002,2.0197548E-002,1.72714908E-002,-3.5297893E-002,2.9808145E-002,7.18882531E-002,-4.65194695E-002,-7.09975883E-002,2.10589427E-003,6.8706125E-002,1.89182116E-003,-1.32000968E-002,-5.56618944E-002,7.22503057E-003,-8.22117329E-002,-4.39907163E-002,-3.44051681E-002,7.23620923E-003,3.23290266E-002,1.84718259E-002,2.26995163E-002,-5.46125658E-002,4.14239103E-003,6.37765825E-002,-2.6696356E-002,-4.74399291E-002,2.87484415E-002,-1.2054625E-002,-6.55562803E-003,3.3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There is a very important part missing from these two examples. We are not performing a Similarity Search, to see how two vectors (and the text it is representing) are similar to each other in 384 dimensional space. I’ll have a look at how to do this and the additional steps needed in the next post.
In my next blog post on this topic, I’ll explore using the AI Model to add Vector data to each Wine in our dataset, to the data and vectors with some Similarity searches, adding vector indexes and how these impact on the queries, how we can automatically update or add new vectors are data is added or update, and we can finish with a comparison with our Lusca wine to find similar wines for our collection.
Ora-lytics 
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