Accelerate Vector Search with Indexing in Oracle 23ai

25 min readSep 15, 2024

In the previous article, I introduced the vector distance-based search. Since then, many articles have provided more comprehensive explanations for implementing RAG with vectors.

Vector distance-based searches can become computationally expensive when the number of vectors (chunks created from the documents in the case of RAG) and vector dimensions increase.

To address this, many vector databases implement “approximate” nearest neighbor search using indexes. Since Oracle Database 23ai also includes such vector indexes, you can observe a new operation in execution plans:

----------------------------------------------------------------------------------------------------
| Id  | Operation                      | Name              | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |                   |     3 |    81 |     2  (50)| 00:00:01 |
|*  1 |  COUNT STOPKEY                 |                   |       |       |            |          |
|   2 |   VIEW                         |                   |     3 |    81 |     2  (50)| 00:00:01 |
|*  3 |    SORT ORDER BY STOPKEY       |                   |     3 |   219 |     2  (50)| 00:00:01 |
|   4 |     TABLE ACCESS BY INDEX ROWID| GALAXIES          |     3 |   219 |     1   (0)| 00:00:01 |
|   5 |      VECTOR INDEX HNSW SCAN    | GALAXIES_HNSW_IDX |     3 |   219 |     1   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------------

We’ll first go over how to use it, and in the second part, experiment with the acceleration of nearest neighbor search!

Allocate memory

Oracle Database 23ai comes with two commonly used vector indexes: Hierarchical Navigable Small World (HNSW) index and Inverted File Flat (IVF) index. Here, we will try the HNSW index, known for its speed and high accuracy.

Since the HNSW index consumes a lot of memory, Oracle Database 23ai makes this index available by allocating a memory area called Vector Pool.

Set the size in the initialization parameters for the CDB.

ALTER SYSTEM SET vector_memory_size=800M SCOPE=SPFILE;

A restart is required to apply the change to this parameter.

shutdown immediate
startup

Confirm the parameter has been configured.

show parameter vector_memory_size

NAME               TYPE        VALUE 
------------------ ----------- ----- 
vector_memory_size big integer 800M

Create vector index

We will create an HNSW index for the EMBEDDINGcolumn (= vector type column) of the GALAXIES table, which we created in the previous article. Since it’s necessary to specify the distance calculation method between vectors, we will use COSINE (= cosine distance) here. In this case, the index will only be used for proximity searches with the VECTOR_DISTANCE function, but only when COSINE is specified.

CREATE VECTOR INDEX galaxies_hnsw_idx ON galaxies (embedding)
ORGANIZATION INMEMORY NEIGHBOR GRAPH DISTANCE COSINE;

For more details on vector indexes, please refer to the documents below.

Exact search without index

First, using the VECTOR_DISTANCE function introduced in the previous article, we will retrieve three vectors that are closest to the galaxy “M31” based on cosine distance.

SELECT name
FROM galaxies
ORDER BY VECTOR_DISTANCE(embedding, '[0.26833928, 0.012467232, -0.48890606, 0.61341953, 0.5590402]', COSINE)
FETCH FIRST 3 ROWS ONLY;

NAME                                              
--------------------------------------------------
M31
M77
M91

The execution plan indicates that the index is not used, and a full table scan is performed. It appears that the cosine distances between the provided vector and the vectors of all items (in this case, nine galaxies) are calculated and then sorted.

EXPLAIN PLAN FOR
SELECT name
FROM galaxies
ORDER BY VECTOR_DISTANCE(embedding, '[0.26833928, 0.012467232, -0.48890606, 0.61341953, 0.5590402]', COSINE)
FETCH FIRST 3 ROWS ONLY;
SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY);

------------------------------------------------------------------------------------
| Id  | Operation               | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT        |          |     3 |    81 |     4  (25)| 00:00:01 |
|*  1 |  COUNT STOPKEY          |          |       |       |            |          |
|   2 |   VIEW                  |          |     9 |   243 |     4  (25)| 00:00:01 |
|*  3 |    SORT ORDER BY STOPKEY|          |     9 |   657 |     4  (25)| 00:00:01 |
|   4 |     TABLE ACCESS FULL   | GALAXIES |     9 |   657 |     3   (0)| 00:00:01 |
------------------------------------------------------------------------------------

Approximate search with index

Next, we add the keyword APPROX to the FETCH clause to perform an approximate search instead of an exact search. This query still returns the same three galaxies.

SELECT name
FROM galaxies
ORDER BY VECTOR_DISTANCE(embedding, '[0.26833928, 0.012467232, -0.48890606, 0.61341953, 0.5590402]', COSINE)
FETCH APPROX FIRST 3 ROWS ONLY;

NAME                                              
--------------------------------------------------
M31
M77
M91

This time, the execution plan shows that the index is used, and only three rows in the GALAXIES table are being accessed.

EXPLAIN PLAN FOR
SELECT name
FROM galaxies
ORDER BY VECTOR_DISTANCE(embedding, '[0.26833928, 0.012467232, -0.48890606, 0.61341953, 0.5590402]', COSINE)
FETCH APPROX FIRST 3 ROWS ONLY;
SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY);

----------------------------------------------------------------------------------------------------
| Id  | Operation                      | Name              | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |                   |     3 |    81 |     2  (50)| 00:00:01 |
|*  1 |  COUNT STOPKEY                 |                   |       |       |            |          |
|   2 |   VIEW                         |                   |     3 |    81 |     2  (50)| 00:00:01 |
|*  3 |    SORT ORDER BY STOPKEY       |                   |     3 |   219 |     2  (50)| 00:00:01 |
|   4 |     TABLE ACCESS BY INDEX ROWID| GALAXIES          |     3 |   219 |     1   (0)| 00:00:01 |
|   5 |      VECTOR INDEX HNSW SCAN    | GALAXIES_HNSW_IDX |     3 |   219 |     1   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------------

In this way, we can see that vector indexes are used for nearest neighbor search.

Experiment with larger data (preparation)

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In the sample above, the data size is too small, so it’s hard to observe the performance improvement brought by vector indexes. To explore this further, we created the following data using 10,000 documents from English Wikipedia. We picked the documents containing the term “Japan” only so that we can easily assess the search accuracy later :)

- Target table: WIKI_EN_10K_EMBEDDING
  - Number of original Wikipedia documents: 10,000
  - Number of extracted chunks: 25,050 (= number of rows in the table)
  - Number of vector dimensions: 1,536

- Query
  - Vector representation of the question "Tell me about Japan's remote islands"
  - Number of vector dimensions: 1,536

You can replicate this experiment with the free Oracle Database 23ai Free Docker image used in the previous article, so try it out if you’re interested.

GRANT create any directory, drop any directory TO myuser;

After preparing an OS directory (/dir01 in this case), create a directory object on the database using myuser.

CREATE DIRECTORY dir01 AS '/dir01';

Download the dump of the wiki_en_10k_embedding table.

cd /dir01
curl -O https://objectstorage.ap-tokyo-1.oraclecloud.com/n/nr92jg8wdcil/b/public/o/wiki_en_10k_embedding.dmp

Import the wiki_en_10k_embedding table.

impdp myuser/password123@freepdb1 \
tables=myuser.wiki_en_10k_embedding directory=dir01 dumpfile=wiki_en_10k_embedding.dmp logfile=wiki_en_10k_embedding.log

Experiment with larger data (result)

Now that the target table is ready, let’s execute queries.

First, we’ll execute an exact search. Since the question “Tell me about Japan’s remote islands” has been transformed into a 1,536 dimensional vector, we split the long string into several parts and included them in the query using the TO_CLOB function. In actual applications, vector type bind variables would be used.

This time, I added the EXACT keyword in the FETCH clause just for clarity. An exact search will be performed whether or not this keyword is present (and APPROX is ignored when no vector index is available).

SET TIMING ON
SELECT JSON_VALUE(metadata, '$.title') title, 
       SUBSTR(text, 0, 100) text
FROM wiki_en_10k_embedding
ORDER BY VECTOR_DISTANCE(
  embedding
, TO_CLOB('[-0.011766872368752956, -0.018229350447654724, 0.003070727689191699, 0.07483755052089691, 0.023605749011039734, -0.0067384992726147175, -0.04313119873404503, 0.03787480667233467, 0.06053248792886734, 0.010782799683511257, -0.00014288559032138437, 0.011874880641698837, -0.005397399887442589, -0.038162827491760254, -0.017149271443486214, -0.027746057137846947, -0.037322767078876495, 0.0020371510181576014, 0.053379956632852554, 0.047955553978681564, 0.00010266385652357712, -0.013837025500833988, -0.011700867675244808, -0.015397141687572002, 0.03881087526679039, 0.0037292763590812683, 0.008874657563865185, 0.02474583499133587, 0.04569938778877258, 0.01458108052611351, 0.02162560261785984, -0.030578266829252243, -0.006924513261765242, -0.01345299743115902, 0.025201868265867233, 0.019189422950148582, 0.02697799913585186, -0.03919490426778793, -0.017593303695321083, -0.028898142278194427, 0.025393882766366005, -0.023161716759204865, 0.02373776026070118, -0.031754352152347565, -0.006618490442633629, 0.04140307009220123, -0.017365287989377975, 0.01681324653327465, -0.0006390473572537303, -0.005085377022624016, 0.01503711473196745, 0.052371881902217865, -0.04502733796834946, -0.028346100822091103, -0.04397125914692879, -0.009114675223827362, -0.00618045823648572, 0.05717223882675171, 0.008130602538585663, -0.02042551338672638, 0.05482006445527077, -0.006336469668895006, -0.0380428209900856, 0.07699770480394363, -0.004869360942393541, 0.016897251829504967, 0.03595466539263725, 0.031970370560884476, -0.015517150051891804, 0.011964886449277401, -0.017809320241212845, -0.0026791985146701336, -0.0001919204805744812, -0.029954221099615097, 0.0040172976441681385, 0.005853434093296528, -0.025441884994506836, -0.018109342083334923, 3.879193900502287e-05, -0.02904215268790722, -0.017521299421787262, 0.0667729526758194, -0.07968590408563614, 0.019549449905753136, 0.04533936083316803, 0.04661145433783531, 0.010674791410565376, 0.012408919632434845, -0.032402403652668, -0.021109564229846, -0.0348505824804306, 0.013152974657714367, -0.07325343042612076, 0.051459815353155136, -0.06408475339412689, -0.00038440347998403013, 0.02906615473330021, 0.04214712232351303, -0.018061337992548943, -0.010614787228405476, -0.0012045892653986812, -0.08525431901216507, -0.018445366993546486, 0.024565821513533592, 0.055588118731975555, 0.006834506522864103, 0.007542558945715427, 0.04452329874038696, -0.004950366914272308, -0.0030452257487922907, -0.0466354563832283, 0.0016216201474890113, 0.0016936255851760507, 0.05976442992687225, -0.00837062019854784, 0.028658123686909676, 0.014773095026612282, -0.000357589015038684, 0.03645870089530945, -0.006828506011515856, -0.056740205734968185, -0.018157346174120903, 0.04437929019331932, 0.06322068721055984, -0.011184829287230968, 0.0033182459883391857, -0.02268168143928051, -0.008604638278484344, -0.036410700529813766, -0.004515334498137236, 0.04675546661019325, -0.027314024046063423, -0.006660493556410074, -0.04754752293229103, 0.03041025437414646, 0.04682747274637222, -0.01141284592449665, -0.013501000590622425, -0.02503385581076145, 0.014845100231468678, 0.010284761898219585, -0.014617083594202995, 0.0365787111222744, -0.010608786717057228, 0.02488984540104866, -0.01212689932435751, -0.018637381494045258, 0.016453219577670097, -0.01786932535469532, -0.014869102276861668, 0.03319445997476578, -0.028514113277196884, -0.0035762651823461056, 0.0025171865709125996, -0.05045174062252045, -0.01821735128760338, 0.021025558933615685, -0.022429661825299263, -0.014641085639595985, 0.073637455701828, -0.021757612004876137, -0.03134632483124733, 0.017221275717020035, 0.009840729646384716, -0.003918290603905916, -0.005403400398790836, 0.008718646131455898, 0.007938588038086891, -0.05861234292387962, -0.00543640274554491, -0.04404326528310776, -0.03120231255888939, 0.04305919259786606, 0.007932588458061218, -0.010944811627268791, -0.046083416789770126, 0.03444255143404007, -0.05093177407979965, -0.04308319464325905, 0.05198785290122032, -')
||TO_CLOB('0.028514113277196884, -0.013152974657714367, -0.09072672575712204, 0.0007328042993322015, 0.005385399330407381, -0.021217573434114456, 0.011514853686094284, 0.00833461806178093, 0.011004815809428692, 0.028490111231803894, 0.012672939337790012, -0.006594488862901926, 0.014797097072005272, 0.03425053879618645, 0.034058526158332825, -0.007416549604386091, -0.015049115754663944, -0.007746574003249407, 0.024193793535232544, -0.025705905631184578, 0.015877176076173782, 0.003540262347087264, 0.015925180166959763, 0.009816727600991726, 0.029426181688904762, -0.009846730157732964, 0.01958545111119747, 0.017365287989377975, 0.031802356243133545, 0.002059652702882886, 0.006288466043770313, 0.012564931064844131, -0.005595414899289608, 0.030770281329751015, 0.02813008427619934, -0.013068968430161476, 0.003861286211758852, 0.00976872444152832, -0.09087073802947998, -0.039290912449359894, 0.013693015091121197, 0.012468923814594746, -0.011832877062261105, 0.02326972410082817, 0.00899466685950756, 0.045843396335840225, 0.015517150051891804, 0.00244218111038208, 0.06466079503297806, 0.04447529837489128, 0.02592192217707634, 0.011100823059678078, -0.0056014154106378555, -0.014305060729384422, 0.009522706270217896, 0.030482258647680283, 0.012828950770199299, -0.006057449150830507, 0.021445589140057564, -0.0209895558655262, 0.0429631844162941, -0.022381659597158432, 0.01801333576440811, -0.016993260011076927, 0.048963628709316254, -0.035042598843574524, -0.000747430429328233, -0.02192562445998192, 0.029426181688904762, -0.03149033337831497, 0.03785080462694168, -0.01629720814526081, 0.023533744737505913, -0.04766753315925598, -0.02383376657962799, -0.009336692281067371, -0.06816505640745163, 0.008202607743442059, -0.013873028568923473, 0.007860582321882248, 0.017689311876893044, 0.02202163264155388, -0.02587391808629036, -0.023065710440278053, -0.01946544274687767, 0.01772531308233738, 0.02901815064251423, 0.025537893176078796, -0.022273650392889977, -0.015625158324837685, 0.00897066481411457, -0.062164608389139175, 0.026545967906713486, -0.03981894999742508, -0.03972294554114342, 0.028490111231803894, -0.059956442564725876, -0.0954790785908699, -0.008058597333729267, -0.027650048956274986, -0.01302096527069807, -0.0030632270500063896, -0.023353731259703636, -0.0038522854447364807, 0.010566783137619495, -0.006396474316716194, 0.01740128919482231, -0.014149048365652561, -0.02496184967458248, 0.008406623266637325, 0.06883710622787476, -0.042603157460689545, -0.01894940435886383, -0.022429661825299263, -0.0257779099047184, -0.03986695408821106, 0.0014828599523752928, -0.00584743358194828, -0.0007346794591285288, -0.007398548536002636, -0.006480480544269085, -0.04673146456480026, 0.0012023390736430883, -0.0015391140477731824, 0.017485296353697777, 0.020329507067799568, -0.004968368448317051, -0.03785080462694168, 0.013753019273281097, 0.011538855731487274, -0.002670197980478406, -0.0407550223171711, 0.027770059183239937, 0.010098748840391636, 0.018277354538440704, -0.0770937129855156, -0.029858212918043137, -0.02608993463218212, 0.01782132126390934, 0.01983747072517872, -0.008106600493192673, -0.030794283375144005, 0.005049374420195818, 0.008442625403404236, -0.06154056265950203, -0.05189184471964836, -0.025129862129688263, -0.030650271102786064, -0.028730129823088646, -0.002575690858066082, -0.03703474625945091, -0.0303142461925745, 0.05851633846759796, 0.02472183294594288, -0.017101267352700233, -0.028850138187408447, -0.044691313058137894, 0.017629306763410568, -0.04106704518198967, 0.02014949358999729, -0.0027587045915424824, 0.030626270920038223, 0.008130602538585663, 0.04308319464325905, 0.0064444774761796, 0.04680347070097923, 0.015553153119981289, -0.01452107634395361, -0.026281947270035744, -0.020857546478509903, 0.007644566707313061, 0.013993036933243275, 0.0064264764077961445, -0.015085117891430855, -0.017017262056469917, -0.014881103299558163, 0.07320542633533478, 0.000981072778813541, -0.08890259265899658, 0.0031472332775592804, -0.067589')
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, COSINE)
FETCH EXACT FIRST 5 ROWS ONLY;

TITLE                     TEXT                                                                            
------------------------- --------------------------------------------------------------------------------
Gotō Islands              The are Japanese islands in the Sea of Japan. They are part of Nagasaki Prefectu
Miyako Islands            The (also Miyako Jima group) are a group of islands in Okinawa Prefecture, Japan
Gotō Islands              There are 140 islands, including five main ones: , , , , and . The northernmost 
Iōjima (Kagoshima)        Shōwa Iōjima, a small uninhabited island, is located just offshore to the north.
Shiretoko National Park   One of the most remote regions in Japan, much of the peninsula is only accessibl

Elapsed: 00:01:26.198

Relevant chunks are being returned, but it takes 1 min 26 seconds. In an RAG-based application, when a question is asked, the system searches for relevant knowledge in a database before generating a response, so we want to reduce the time for this initial data retrieval.

Therefore, we need a vector index.

CREATE VECTOR INDEX idx_wiki_en_10k_embedding ON wiki_en_10k_embedding (embedding)
ORGANIZATION INMEMORY NEIGHBOR GRAPH DISTANCE COSINE;

Vector INDEX created.

Elapsed: 00:02:47.126

This is the same query as before, but this time we will use APPROX to perform an approximate nearest neighbor search.

SET TIMING ON
SELECT JSON_VALUE(metadata, '$.title') title, 
       SUBSTR(text, 0, 100) text
FROM wiki_en_10k_embedding
ORDER BY VECTOR_DISTANCE(
  embedding
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||TO_CLOB('05, 0.019741462543606758, 0.0028757131658494473, -0.013368991203606129, 0.02897014655172825, -0.0033902511931955814, 0.0071225278079509735, -0.021169569343328476, -0.0033092452213168144, 0.004293318372219801, 0.01413704827427864, -0.03230639547109604, 0.010362768545746803, 0.013933032751083374, 0.010146751999855042, -0.012168901972472668, 0.03554663434624672, 0.014665086753666401, 0.0025966924149543047, -0.01838536374270916, -0.02481783926486969, -0.0049953702837228775, -0.0348505824804306, 0.018601378425955772, 0.057652272284030914, 0.026401957497000694, 0.022573674097657204, -0.009786725044250488, 0.009384695440530777, 0.019657457247376442, 0.04226713255047798, -0.004647344350814819, 0.0051363809034228325, -0.022981703281402588, 0.02385776862502098, 0.018025336787104607, -0.00379528128542006, 0.002110656350851059, -0.06797304004430771, 0.008274612948298454, -0.010758797638118267, -0.00016304333985317498, -0.022945700213313103, 0.0326424203813076, 0.020929550752043724, -0.00035327617661096156, -0.03545062616467476, 0.009150678291916847, -0.01723327673971653, -0.0076685682870447636, -0.013705016113817692, -0.02427779883146286, -0.04617942124605179, -0.0009128176607191563, -0.016381213441491127, 0.03417853266000748, 0.015745166689157486, -0.01678924448788166, -0.009822728112339973, -0.015877176076173782, 0.001753630000166595, 0.002026650123298168, 0.04027498513460159, -0.05486806854605675, 0.0180373378098011, -0.02047351747751236, 0.029666198417544365, 0.02901815064251423, 0.0815100446343422, 0.004578339401632547, -0.027290022000670433, -0.02366575412452221, 0.025177866220474243, 0.007152530364692211, -0.009900733828544617, -0.004665345884859562, 0.004296318627893925, 0.025201868265867233, -0.00891066063195467, -0.00611145282164216, 0.056596193462610245, -0.018913401290774345, -0.028802134096622467, -0.037274762988090515, 0.0021286578848958015, 0.0022156641352921724, -0.0037892807740718126, 0.019405437633395195, 0.006144455634057522, -0.04658745229244232, -0.010230758227407932, 0.028562117367982864, -0.032402403652668, -0.012042893096804619, 0.04013097286224365, 0.01169486716389656, -0.0067505002953112125, -0.010938811115920544, -0.025633899495005608, 0.024577822536230087, -0.001253342954441905, 0.02228565141558647, 0.00968471821397543, 0.03533061966300011, 0.045891400426626205, -0.011574857868254185, 0.02312571369111538, 0.019825469702482224, 0.016681237146258354, 0.02913815900683403, -0.036818727850914, 0.011784873902797699, 0.014413068071007729, -0.01850537210702896, -0.024241797626018524, -0.025177866220474243, -0.04824357479810715, 0.025561895221471786, 0.0043203202076256275, -0.02827409654855728, -0.010896807536482811, 0.007848582230508327, 0.018889401108026505, -0.03125031664967537, -0.02364175207912922, 0.0022786688059568405, -0.017317283898591995, -0.002317671896889806, -0.03847485035657883, -0.01674124039709568, 0.03674672544002533, -0.02378576248884201, 0.021937625482678413, -0.02608993463218212, -0.013248981907963753, -0.006996518466621637, 0.036218684166669846, 0.011316838674247265, 0.025105860084295273, -0.004296318627893925, -0.003876287257298827, 0.00039940461283549666, 0.010500778444111347, 0.025633899495005608, -0.04644344374537468, -0.03797081485390663, 0.016033189371228218, -0.026497963815927505, -0.010452774353325367, -0.028658123686909676, 0.039290912449359894, 0.010194756090641022, -0.01882939599454403, 0.0017821320798248053, -0.0412350557744503, -0.023053709417581558, 0.03259441629052162, -0.016201201826334, -0.05405200645327568, -0.032834433019161224, -0.020185496658086777, -0.006678495090454817, 0.014641085639595985, -0.016141196712851524, -0.013116972520947456, 0.00511837936937809, -0.02439780905842781, -0.004515334498137236, 0.00483035808429122, -0.01207889523357153, -0.0466354563832283, 0.023845767602324486, 0.013356990180909634, -0.020653530955314636, -0.00961271207779646, -0.014425069093704224, 0.002413678914308548, 0.018541375175118446, -0.010428773239254951, -0.020209498703479767, 0.036626715213')
||TO_CLOB('06038, -0.0407550223171711, 0.0378028005361557, 0.0002927091845776886, -0.028442109003663063, -0.03871487081050873, 0.016453219577670097, -0.00867664348334074, 0.004938365891575813, 0.008748648688197136, -0.009582710452377796, -0.029618196189403534, -0.018061337992548943, -0.0012473424430936575, 0.012516927905380726, -0.010524780489504337, 0.02596992440521717, -0.015253130346536636, 0.025177866220474243, 0.01075279712677002, 0.0062044598162174225, -0.0034052524715662003, 0.026281947270035744, 0.0015098619041964412, -0.0038102823309600353, -0.0028862138278782368, 0.03372250124812126, 0.0037412773817777634, -0.0227056834846735, 0.041571080684661865, -0.03691473603248596, -0.0063664717599749565, 0.051267798990011215, -0.012612935155630112, -0.026257947087287903, 0.00128709536511451, 0.004131306428462267, 0.0022261650301516056, 0.0026956999208778143, -0.008658641949295998, -0.0217216107994318, 0.014269057661294937, -0.027722055092453957, -0.013104971498250961, -0.008196607232093811, 0.07474154233932495, -0.05174783617258072, -0.0026791985146701336, -0.0012803449062630534, 0.0030497261323034763, -0.024469813331961632, 0.0022171642631292343, 0.016537226736545563, 1.1573123401831253e-06, -0.023437736555933952, 0.04843559116125107, -0.06322068721055984, 0.007296540774405003, -0.020185496658086777, 0.000820560846477747, -0.00901866890490055, -0.006402474362403154, 0.03773079812526703, -0.0064384774304926395, 0.008790651336312294, -0.02068953402340412, -0.048123568296432495, 0.025225870311260223, -0.002817208878695965, -0.023569747805595398, 0.021289577707648277, 0.0019276428502053022, 0.002385176718235016, 0.01951344683766365, 0.024001779034733772, 0.0073445443995296955, 0.0128889549523592, -0.0009585710358805954, 0.03921890631318092, -0.014473073184490204, 0.020329507067799568, 0.0007654317305423319, -0.013741018250584602, 0.004878361709415913, -0.028562117367982864, 0.008868657052516937, -0.011916883289813995, 0.03355448693037033, -0.00040653013275004923, -0.012732943519949913, -0.020269501954317093, 0.018577376380562782, -0.02817808836698532, -0.0002840835659299046, 0.0065764873288571835, -0.0007451802375726402, -0.0035192607901990414, -1.4567485777661204e-05, 0.000659298850223422, 0.01509711891412735, -0.023005705326795578, 0.011496852152049541, -0.03981894999742508, -0.01659722998738289, 0.03662671521306038, -0.00937869492918253, -0.009930736385285854, 0.014197052456438541, -0.026281947270035744, -0.006039447616785765, 0.019237425178289413, -0.038354843854904175, -0.012804948724806309, -0.014185051433742046, 0.04857959970831871, -0.0053193941712379456, -0.04536336287856102, -0.013116972520947456, 0.02160160057246685, 0.025153864175081253, 0.02230965346097946, -0.011502852663397789, -0.02488984540104866, 0.009222683496773243, 0.019105415791273117, -0.003861286211758852, 0.016957256942987442, 0.03645870089530945, 0.0026926996652036905, -0.016165198758244514, 0.015505149029195309, -0.02601792849600315, -0.004539336543530226, 0.015361138619482517, 0.012300911359488964, 0.0016216201474890113, 0.014221054501831532, -0.007206534035503864, 0.030722277238965034, 0.0060544488951563835, 0.030578266829252243, -0.0217216107994318, 0.009774724952876568, -0.014341062866151333, 0.0011828376445919275, -0.01924942620098591, -0.00912067573517561, 0.005445403512567282, -0.013464998453855515, -0.024217795580625534, -0.0025006853975355625, 0.009234684519469738, 0.00942669901996851, -0.023725759238004684, -0.030698275193572044, -0.009852730669081211, 0.00418531009927392, -0.021469591185450554, 0.005766427610069513, -0.008586636744439602, -0.017833322286605835, 0.0016606231220066547, 0.039434924721717834, 0.007338543888181448, -0.023797763511538506, 0.016825247555971146, 0.006900511682033539, 0.010920809581875801, -0.035234611481428146, 0.0009473201935179532, 0.028298096731305122, 0.02199763059616089, -0.014041041024029255, 0.022081635892391205, -0.004728350322693586, 0.027818061411380768, 0.03309845179319382, -0.008580636233091354, 0.009786725044250488,')
||TO_CLOB(' 0.008808652870357037, 0.01132883969694376, -0.023461738601326942, 0.038378845900297165, -0.030578266829252243, -0.004908363800495863, -0.020305505022406578, -0.003957293462008238, -0.006552485749125481, -0.005991443991661072, -0.02410978637635708, -0.020857546478509903, -0.0014423569664359093, 0.025489889085292816, 0.00040465500205755234, -0.004722350277006626, -0.0004905363894067705, -0.01293695904314518, -0.017005261033773422, 0.013501000590622425, 0.019153419882059097, 0.048987630754709244, -0.005595414899289608, -0.0011460849782451987, 0.0019201423274353147, 0.02199763059616089, 0.021025558933615685, 0.03691473603248596, 0.009372694417834282, 0.026305949315428734, 0.0155891552567482, -0.006570486817508936, -0.020737536251544952, 0.028322098776698112, -0.013152974657714367, 0.0011273336131125689, -0.00828661397099495, 0.0010020743357017636, -0.011214830912649632, 0.0412350557744503, 0.0182893555611372, 0.00989473331719637, -0.010740796104073524, -0.0015061115846037865, -0.039530929177999496, 0.023449737578630447, 0.051315803080797195, -0.02717001363635063, -0.014041041024029255, -0.0011145826429128647, -0.06274065375328064, 0.019333433359861374, -0.007524557877331972, -0.01838536374270916, -0.010866805911064148, -0.020953552797436714, -0.008748648688197136, -0.000965321552939713, -0.029642196372151375, 0.0246738288551569, 0.02040151134133339, -0.00021039060084149241, 0.002862212248146534, -0.02390577271580696, 0.0032402402721345425, -0.025753909721970558, -0.018229350447654724, -0.010968812741339207, 0.023629751056432724, -0.016417216509580612, 0.005955441389232874, 0.0025036856532096863, 0.03221038728952408, -0.0028907142113894224, 0.0012615935411304235, 0.009792725555598736, 0.0051453812047839165, -0.012156900949776173, 0.005325394682586193, 0.023053709417581558, -0.05357196927070618, -0.006936514284461737, 0.044739317148923874, 0.028658123686909676, -0.01115482673048973, 0.012432921677827835, 0.006840507034212351, 0.003123231464996934, 0.002172161126509309, 0.012984962202608585, -0.02143358811736107, -0.0012690940639004111, -0.011640862561762333, 0.02155359834432602, 0.013849026523530483, -0.017293281853199005, -0.00867664348334074, 0.019777465611696243]')
, COSINE)
FETCH APPROX FIRST 5 ROWS ONLY;

TITLE                     TEXT                                                                            
------------------------- --------------------------------------------------------------------------------
Gotō Islands              The are Japanese islands in the Sea of Japan. They are part of Nagasaki Prefectu
Miyako Islands            The (also Miyako Jima group) are a group of islands in Okinawa Prefecture, Japan
Gotō Islands              There are 140 islands, including five main ones: , , , , and . The northernmost 
Iōjima (Kagoshima)        Shōwa Iōjima, a small uninhabited island, is located just offshore to the north.
Shiretoko National Park   One of the most remote regions in Japan, much of the peninsula is only accessibl

Elapsed: 00:00:00.033

The execution time was significantly reduced to 0.033 seconds!

In this case, the results are identical to the exact nearest neighbor search, but approximate search results can sometimes differ. If the accuracy is acceptable, vector indexes provide significant performance improvements. When the volume of data grows, approximate searches will likely become essential.

The process of creating the wiki_en_10k_embedding table and how to use vector indexing with LangChain will be explained in another article. Please take a look if you're interested.

Appendix: Obtain the question vector

The vector representation of the question used in the query above was obtained using OpenAI API as shown below. Since the generated result (in this case, the embeddings) is considered public domain, I have made the embeddings publicly available as a table dump.

from langchain_openai import OpenAIEmbeddings
import os

query = "Please tell me about Japan's remote islands."

os.environ["OPENAI_API_KEY"] = "<openai_api_key>"
model = OpenAIEmbeddings(model="text-embedding-3-small")
embedding_vector = model.embed_query(query)

def split_text(text, chunk_size=4000):
    return [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]

chunks = split_text(str(embedding_vector))

for i, chunk in enumerate(chunks):
    print("||TO_CLOB('" + chunk + "')")

||TO_CLOB('[-0.011766872368752956, -0.018229350447654724, 0.003070727689191699, 0.07483755052089691, 0.023605749011039734, -0.0067384992726147175, -0.04313119873404503, 0.03787480667233467, 0.06053248792886734, 0.010782799683511257, -0.00014288559032138437, 0.011874880641698837, -0.005397399887442589, -0.038162827491760254, -0.017149271443486214, -0.027746057137846947, -0.037322767078876495, 0.0020371510181576014, 0.053379956632852554, 0.047955553978681564, 0.00010266385652357712, -0.013837025500833988, -0.011700867675244808, -0.015397141687572002, 0.03881087526679039, 0.0037292763590812683, 0.008874657563865185, 0.02474583499133587, 0.04569938778877258, 0.01458108052611351, 0.02162560261785984, -0.030578266829252243, -0.006924513261765242, -0.01345299743115902, 0.025201868265867233, 0.019189422950148582, 0.02697799913585186, -0.03919490426778793, -0.017593303695321083, -0.028898142278194427, 0.025393882766366005, -0.023161716759204865, 0.02373776026070118, -0.031754352152347565, -0.006618490442633629, 0.04140307009220123, -0.017365287989377975, 0.01681324653327465, -0.0006390473572537303, -0.005085377022624016, 0.01503711473196745, 0.052371881902217865, -0.04502733796834946, -0.028346100822091103, -0.04397125914692879, -0.009114675223827362, -0.00618045823648572, 0.05717223882675171, 0.008130602538585663, -0.02042551338672638, 0.05482006445527077, -0.006336469668895006, -0.0380428209900856, 0.07699770480394363, -0.004869360942393541, 0.016897251829504967, 0.03595466539263725, 0.031970370560884476, -0.015517150051891804, 0.011964886449277401, -0.017809320241212845, -0.0026791985146701336, -0.0001919204805744812, -0.029954221099615097, 0.0040172976441681385, 0.005853434093296528, -0.025441884994506836, -0.018109342083334923, 3.879193900502287e-05, -0.02904215268790722, -0.017521299421787262, 0.0667729526758194, -0.07968590408563614, 0.019549449905753136, 0.04533936083316803, 0.04661145433783531, 0.010674791410565376, 0.012408919632434845, -0.032402403652668, -0.021109564229846, -0.0348505824804306, 0.013152974657714367, -0.07325343042612076, 0.051459815353155136, -0.06408475339412689, -0.00038440347998403013, 0.02906615473330021, 0.04214712232351303, -0.018061337992548943, -0.010614787228405476, -0.0012045892653986812, -0.08525431901216507, -0.018445366993546486, 0.024565821513533592, 0.055588118731975555, 0.006834506522864103, 0.007542558945715427, 0.04452329874038696, -0.004950366914272308, -0.0030452257487922907, -0.0466354563832283, 0.0016216201474890113, 0.0016936255851760507, 0.05976442992687225, -0.00837062019854784, 0.028658123686909676, 0.014773095026612282, -0.000357589015038684, 0.03645870089530945, -0.006828506011515856, -0.056740205734968185, -0.018157346174120903, 0.04437929019331932, 0.06322068721055984, -0.011184829287230968, 0.0033182459883391857, -0.02268168143928051, -0.008604638278484344, -0.036410700529813766, -0.004515334498137236, 0.04675546661019325, -0.027314024046063423, -0.006660493556410074, -0.04754752293229103, 0.03041025437414646, 0.04682747274637222, -0.01141284592449665, -0.013501000590622425, -0.02503385581076145, 0.014845100231468678, 0.010284761898219585, -0.014617083594202995, 0.0365787111222744, -0.010608786717057228, 0.02488984540104866, -0.01212689932435751, -0.018637381494045258, 0.016453219577670097, -0.01786932535469532, -0.014869102276861668, 0.03319445997476578, -0.028514113277196884, -0.0035762651823461056, 0.0025171865709125996, -0.05045174062252045, -0.01821735128760338, 0.021025558933615685, -0.022429661825299263, -0.014641085639595985, 0.073637455701828, -0.021757612004876137, -0.03134632483124733, 0.017221275717020035, 0.009840729646384716, -0.003918290603905916, -0.005403400398790836, 0.008718646131455898, 0.007938588038086891, -0.05861234292387962, -0.00543640274554491, -0.04404326528310776, -0.03120231255888939, 0.04305919259786606, 0.007932588458061218, -0.010944811627268791, -0.046083416789770126, 0.03444255143404007, -0.05093177407979965, -0.04308319464325905, 0.05198785290122032, -')
||TO_CLOB('0.028514113277196884, -0.013152974657714367, -0.09072672575712204, 0.0007328042993322015, 0.005385399330407381, -0.021217573434114456, 0.011514853686094284, 0.00833461806178093, 0.011004815809428692, 0.028490111231803894, 0.012672939337790012, -0.006594488862901926, 0.014797097072005272, 0.03425053879618645, 0.034058526158332825, -0.007416549604386091, -0.015049115754663944, -0.007746574003249407, 0.024193793535232544, -0.025705905631184578, 0.015877176076173782, 0.003540262347087264, 0.015925180166959763, 0.009816727600991726, 0.029426181688904762, -0.009846730157732964, 0.01958545111119747, 0.017365287989377975, 0.031802356243133545, 0.002059652702882886, 0.006288466043770313, 0.012564931064844131, -0.005595414899289608, 0.030770281329751015, 0.02813008427619934, -0.013068968430161476, 0.003861286211758852, 0.00976872444152832, -0.09087073802947998, -0.039290912449359894, 0.013693015091121197, 0.012468923814594746, -0.011832877062261105, 0.02326972410082817, 0.00899466685950756, 0.045843396335840225, 0.015517150051891804, 0.00244218111038208, 0.06466079503297806, 0.04447529837489128, 0.02592192217707634, 0.011100823059678078, -0.0056014154106378555, -0.014305060729384422, 0.009522706270217896, 0.030482258647680283, 0.012828950770199299, -0.006057449150830507, 0.021445589140057564, -0.0209895558655262, 0.0429631844162941, -0.022381659597158432, 0.01801333576440811, -0.016993260011076927, 0.048963628709316254, -0.035042598843574524, -0.000747430429328233, -0.02192562445998192, 0.029426181688904762, -0.03149033337831497, 0.03785080462694168, -0.01629720814526081, 0.023533744737505913, -0.04766753315925598, -0.02383376657962799, -0.009336692281067371, -0.06816505640745163, 0.008202607743442059, -0.013873028568923473, 0.007860582321882248, 0.017689311876893044, 0.02202163264155388, -0.02587391808629036, -0.023065710440278053, -0.01946544274687767, 0.01772531308233738, 0.02901815064251423, 0.025537893176078796, -0.022273650392889977, -0.015625158324837685, 0.00897066481411457, -0.062164608389139175, 0.026545967906713486, -0.03981894999742508, -0.03972294554114342, 0.028490111231803894, -0.059956442564725876, -0.0954790785908699, -0.008058597333729267, -0.027650048956274986, -0.01302096527069807, -0.0030632270500063896, -0.023353731259703636, -0.0038522854447364807, 0.010566783137619495, -0.006396474316716194, 0.01740128919482231, -0.014149048365652561, -0.02496184967458248, 0.008406623266637325, 0.06883710622787476, -0.042603157460689545, -0.01894940435886383, -0.022429661825299263, -0.0257779099047184, -0.03986695408821106, 0.0014828599523752928, -0.00584743358194828, -0.0007346794591285288, -0.007398548536002636, -0.006480480544269085, -0.04673146456480026, 0.0012023390736430883, -0.0015391140477731824, 0.017485296353697777, 0.020329507067799568, -0.004968368448317051, -0.03785080462694168, 0.013753019273281097, 0.011538855731487274, -0.002670197980478406, -0.0407550223171711, 0.027770059183239937, 0.010098748840391636, 0.018277354538440704, -0.0770937129855156, -0.029858212918043137, -0.02608993463218212, 0.01782132126390934, 0.01983747072517872, -0.008106600493192673, -0.030794283375144005, 0.005049374420195818, 0.008442625403404236, -0.06154056265950203, -0.05189184471964836, -0.025129862129688263, -0.030650271102786064, -0.028730129823088646, -0.002575690858066082, -0.03703474625945091, -0.0303142461925745, 0.05851633846759796, 0.02472183294594288, -0.017101267352700233, -0.028850138187408447, -0.044691313058137894, 0.017629306763410568, -0.04106704518198967, 0.02014949358999729, -0.0027587045915424824, 0.030626270920038223, 0.008130602538585663, 0.04308319464325905, 0.0064444774761796, 0.04680347070097923, 0.015553153119981289, -0.01452107634395361, -0.026281947270035744, -0.020857546478509903, 0.007644566707313061, 0.013993036933243275, 0.0064264764077961445, -0.015085117891430855, -0.017017262056469917, -0.014881103299558163, 0.07320542633533478, 0.000981072778813541, -0.08890259265899658, 0.0031472332775592804, -0.067589')
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||TO_CLOB('05, 0.019741462543606758, 0.0028757131658494473, -0.013368991203606129, 0.02897014655172825, -0.0033902511931955814, 0.0071225278079509735, -0.021169569343328476, -0.0033092452213168144, 0.004293318372219801, 0.01413704827427864, -0.03230639547109604, 0.010362768545746803, 0.013933032751083374, 0.010146751999855042, -0.012168901972472668, 0.03554663434624672, 0.014665086753666401, 0.0025966924149543047, -0.01838536374270916, -0.02481783926486969, -0.0049953702837228775, -0.0348505824804306, 0.018601378425955772, 0.057652272284030914, 0.026401957497000694, 0.022573674097657204, -0.009786725044250488, 0.009384695440530777, 0.019657457247376442, 0.04226713255047798, -0.004647344350814819, 0.0051363809034228325, -0.022981703281402588, 0.02385776862502098, 0.018025336787104607, -0.00379528128542006, 0.002110656350851059, -0.06797304004430771, 0.008274612948298454, -0.010758797638118267, -0.00016304333985317498, -0.022945700213313103, 0.0326424203813076, 0.020929550752043724, -0.00035327617661096156, -0.03545062616467476, 0.009150678291916847, -0.01723327673971653, -0.0076685682870447636, -0.013705016113817692, -0.02427779883146286, -0.04617942124605179, -0.0009128176607191563, -0.016381213441491127, 0.03417853266000748, 0.015745166689157486, -0.01678924448788166, -0.009822728112339973, -0.015877176076173782, 0.001753630000166595, 0.002026650123298168, 0.04027498513460159, -0.05486806854605675, 0.0180373378098011, -0.02047351747751236, 0.029666198417544365, 0.02901815064251423, 0.0815100446343422, 0.004578339401632547, -0.027290022000670433, -0.02366575412452221, 0.025177866220474243, 0.007152530364692211, -0.009900733828544617, -0.004665345884859562, 0.004296318627893925, 0.025201868265867233, -0.00891066063195467, -0.00611145282164216, 0.056596193462610245, -0.018913401290774345, -0.028802134096622467, -0.037274762988090515, 0.0021286578848958015, 0.0022156641352921724, -0.0037892807740718126, 0.019405437633395195, 0.006144455634057522, -0.04658745229244232, -0.010230758227407932, 0.028562117367982864, -0.032402403652668, -0.012042893096804619, 0.04013097286224365, 0.01169486716389656, -0.0067505002953112125, -0.010938811115920544, -0.025633899495005608, 0.024577822536230087, -0.001253342954441905, 0.02228565141558647, 0.00968471821397543, 0.03533061966300011, 0.045891400426626205, -0.011574857868254185, 0.02312571369111538, 0.019825469702482224, 0.016681237146258354, 0.02913815900683403, -0.036818727850914, 0.011784873902797699, 0.014413068071007729, -0.01850537210702896, -0.024241797626018524, -0.025177866220474243, -0.04824357479810715, 0.025561895221471786, 0.0043203202076256275, -0.02827409654855728, -0.010896807536482811, 0.007848582230508327, 0.018889401108026505, -0.03125031664967537, -0.02364175207912922, 0.0022786688059568405, -0.017317283898591995, -0.002317671896889806, -0.03847485035657883, -0.01674124039709568, 0.03674672544002533, -0.02378576248884201, 0.021937625482678413, -0.02608993463218212, -0.013248981907963753, -0.006996518466621637, 0.036218684166669846, 0.011316838674247265, 0.025105860084295273, -0.004296318627893925, -0.003876287257298827, 0.00039940461283549666, 0.010500778444111347, 0.025633899495005608, -0.04644344374537468, -0.03797081485390663, 0.016033189371228218, -0.026497963815927505, -0.010452774353325367, -0.028658123686909676, 0.039290912449359894, 0.010194756090641022, -0.01882939599454403, 0.0017821320798248053, -0.0412350557744503, -0.023053709417581558, 0.03259441629052162, -0.016201201826334, -0.05405200645327568, -0.032834433019161224, -0.020185496658086777, -0.006678495090454817, 0.014641085639595985, -0.016141196712851524, -0.013116972520947456, 0.00511837936937809, -0.02439780905842781, -0.004515334498137236, 0.00483035808429122, -0.01207889523357153, -0.0466354563832283, 0.023845767602324486, 0.013356990180909634, -0.020653530955314636, -0.00961271207779646, -0.014425069093704224, 0.002413678914308548, 0.018541375175118446, -0.010428773239254951, -0.020209498703479767, 0.036626715213')
||TO_CLOB('06038, -0.0407550223171711, 0.0378028005361557, 0.0002927091845776886, -0.028442109003663063, -0.03871487081050873, 0.016453219577670097, -0.00867664348334074, 0.004938365891575813, 0.008748648688197136, -0.009582710452377796, -0.029618196189403534, -0.018061337992548943, -0.0012473424430936575, 0.012516927905380726, -0.010524780489504337, 0.02596992440521717, -0.015253130346536636, 0.025177866220474243, 0.01075279712677002, 0.0062044598162174225, -0.0034052524715662003, 0.026281947270035744, 0.0015098619041964412, -0.0038102823309600353, -0.0028862138278782368, 0.03372250124812126, 0.0037412773817777634, -0.0227056834846735, 0.041571080684661865, -0.03691473603248596, -0.0063664717599749565, 0.051267798990011215, -0.012612935155630112, -0.026257947087287903, 0.00128709536511451, 0.004131306428462267, 0.0022261650301516056, 0.0026956999208778143, -0.008658641949295998, -0.0217216107994318, 0.014269057661294937, -0.027722055092453957, -0.013104971498250961, -0.008196607232093811, 0.07474154233932495, -0.05174783617258072, -0.0026791985146701336, -0.0012803449062630534, 0.0030497261323034763, -0.024469813331961632, 0.0022171642631292343, 0.016537226736545563, 1.1573123401831253e-06, -0.023437736555933952, 0.04843559116125107, -0.06322068721055984, 0.007296540774405003, -0.020185496658086777, 0.000820560846477747, -0.00901866890490055, -0.006402474362403154, 0.03773079812526703, -0.0064384774304926395, 0.008790651336312294, -0.02068953402340412, -0.048123568296432495, 0.025225870311260223, -0.002817208878695965, -0.023569747805595398, 0.021289577707648277, 0.0019276428502053022, 0.002385176718235016, 0.01951344683766365, 0.024001779034733772, 0.0073445443995296955, 0.0128889549523592, -0.0009585710358805954, 0.03921890631318092, -0.014473073184490204, 0.020329507067799568, 0.0007654317305423319, -0.013741018250584602, 0.004878361709415913, -0.028562117367982864, 0.008868657052516937, -0.011916883289813995, 0.03355448693037033, -0.00040653013275004923, -0.012732943519949913, -0.020269501954317093, 0.018577376380562782, -0.02817808836698532, -0.0002840835659299046, 0.0065764873288571835, -0.0007451802375726402, -0.0035192607901990414, -1.4567485777661204e-05, 0.000659298850223422, 0.01509711891412735, -0.023005705326795578, 0.011496852152049541, -0.03981894999742508, -0.01659722998738289, 0.03662671521306038, -0.00937869492918253, -0.009930736385285854, 0.014197052456438541, -0.026281947270035744, -0.006039447616785765, 0.019237425178289413, -0.038354843854904175, -0.012804948724806309, -0.014185051433742046, 0.04857959970831871, -0.0053193941712379456, -0.04536336287856102, -0.013116972520947456, 0.02160160057246685, 0.025153864175081253, 0.02230965346097946, -0.011502852663397789, -0.02488984540104866, 0.009222683496773243, 0.019105415791273117, -0.003861286211758852, 0.016957256942987442, 0.03645870089530945, 0.0026926996652036905, -0.016165198758244514, 0.015505149029195309, -0.02601792849600315, -0.004539336543530226, 0.015361138619482517, 0.012300911359488964, 0.0016216201474890113, 0.014221054501831532, -0.007206534035503864, 0.030722277238965034, 0.0060544488951563835, 0.030578266829252243, -0.0217216107994318, 0.009774724952876568, -0.014341062866151333, 0.0011828376445919275, -0.01924942620098591, -0.00912067573517561, 0.005445403512567282, -0.013464998453855515, -0.024217795580625534, -0.0025006853975355625, 0.009234684519469738, 0.00942669901996851, -0.023725759238004684, -0.030698275193572044, -0.009852730669081211, 0.00418531009927392, -0.021469591185450554, 0.005766427610069513, -0.008586636744439602, -0.017833322286605835, 0.0016606231220066547, 0.039434924721717834, 0.007338543888181448, -0.023797763511538506, 0.016825247555971146, 0.006900511682033539, 0.010920809581875801, -0.035234611481428146, 0.0009473201935179532, 0.028298096731305122, 0.02199763059616089, -0.014041041024029255, 0.022081635892391205, -0.004728350322693586, 0.027818061411380768, 0.03309845179319382, -0.008580636233091354, 0.009786725044250488,')
||TO_CLOB(' 0.008808652870357037, 0.01132883969694376, -0.023461738601326942, 0.038378845900297165, -0.030578266829252243, -0.004908363800495863, -0.020305505022406578, -0.003957293462008238, -0.006552485749125481, -0.005991443991661072, -0.02410978637635708, -0.020857546478509903, -0.0014423569664359093, 0.025489889085292816, 0.00040465500205755234, -0.004722350277006626, -0.0004905363894067705, -0.01293695904314518, -0.017005261033773422, 0.013501000590622425, 0.019153419882059097, 0.048987630754709244, -0.005595414899289608, -0.0011460849782451987, 0.0019201423274353147, 0.02199763059616089, 0.021025558933615685, 0.03691473603248596, 0.009372694417834282, 0.026305949315428734, 0.0155891552567482, -0.006570486817508936, -0.020737536251544952, 0.028322098776698112, -0.013152974657714367, 0.0011273336131125689, -0.00828661397099495, 0.0010020743357017636, -0.011214830912649632, 0.0412350557744503, 0.0182893555611372, 0.00989473331719637, -0.010740796104073524, -0.0015061115846037865, -0.039530929177999496, 0.023449737578630447, 0.051315803080797195, -0.02717001363635063, -0.014041041024029255, -0.0011145826429128647, -0.06274065375328064, 0.019333433359861374, -0.007524557877331972, -0.01838536374270916, -0.010866805911064148, -0.020953552797436714, -0.008748648688197136, -0.000965321552939713, -0.029642196372151375, 0.0246738288551569, 0.02040151134133339, -0.00021039060084149241, 0.002862212248146534, -0.02390577271580696, 0.0032402402721345425, -0.025753909721970558, -0.018229350447654724, -0.010968812741339207, 0.023629751056432724, -0.016417216509580612, 0.005955441389232874, 0.0025036856532096863, 0.03221038728952408, -0.0028907142113894224, 0.0012615935411304235, 0.009792725555598736, 0.0051453812047839165, -0.012156900949776173, 0.005325394682586193, 0.023053709417581558, -0.05357196927070618, -0.006936514284461737, 0.044739317148923874, 0.028658123686909676, -0.01115482673048973, 0.012432921677827835, 0.006840507034212351, 0.003123231464996934, 0.002172161126509309, 0.012984962202608585, -0.02143358811736107, -0.0012690940639004111, -0.011640862561762333, 0.02155359834432602, 0.013849026523530483, -0.017293281853199005, -0.00867664348334074, 0.019777465611696243]')

In the experiment above, this output was embedded into the SQL query.

Appendix: Memory usage

In the procedure mentioned above, the vector pool size is configured for the CDB, and this will apply the same value to all PDBs. If you’d like to set a specific limit for each PDB, adjust the value of this parameter.

alter session set container=freepdb1;
show parameter vector_memory_size

NAME               TYPE        VALUE 
------------------ ----------- ----- 
vector_memory_size big integer 304M

When checking the v$vector_memory_pool view, we can see that 556MB out of 560MB has been allocated in the 1MB pool, where the index is stored.

select
  con_id, pool,
  alloc_bytes/1024/1024 as alloc_bytes_mb,
  used_bytes/1024/1024 as used_bytes_mb
from v$vector_memory_pool order by 1,2;

    CON_ID POOL                       ALLOC_BYTES_MB USED_BYTES_MB
---------- -------------------------- -------------- -------------
         3 1MB POOL                              560           556
         3 64KB POOL                             224        2.0625
         3 IM POOL METADATA                       16            16

Due to the high memory usage of the HNSW index, it seems important to estimate the requirements and decide on the memory capacity accordingly.

Oracle Developers

Accelerate Vector Search with Indexing in Oracle 23ai

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