Precision issue while querying the vector on the Qdrant vector benchmark test

[donhardman]

While trying to integrate Manticore Search into the Qdrant vector database benchmark, I discovered that we have very low mean precision.

In the current test that I have attached here, Manticore Search's mean precision equals 0.16, while Qdrant's is 0.99.

We should investigate this behavior and try to understand why it happens.

I have also attached the original test set in the attachments to initialize the database.

I checked also it with https://newtum.com/calculators/maths/cosine-similarity-calculator

So Manticore top 1 result gives 0.89 similarity while the expected one is 0.91.

Path to the dump: /tmp/bench.tar.gz on dev2.

The original Request vector used for search [k=100, ef=128]

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0.03929999843239784, 0.0697999969124794, 0.10480000078678131, 0.16290000081062317, 0.04520000144839287, 0.09139999747276306, 0.1193000003695488, 0.11640000343322754, 0.007199999876320362, 0.03400000184774399, 0.07320000231266022, 0.030899999663233757, 0.005400000140070915, 0.008500000461935997, 0.01979999989271164, 0.013899999670684338, 0.030700000002980232, 0.02160000056028366, 0.10180000215768814, 0.07959999889135361, 0.03449999913573265, 0.04540000110864639, 0.0957999974489212, 0.0869000032544136, 0.009600000455975533, 0.01889999955892563, 0.026399999856948853, 0.03610000014305115, 0.014700000174343586, 0.011699999682605267, 0.00839999970048666, 0.03440000116825104, 0.032499998807907104, 0.03269999846816063, 0.054999999701976776, 0.06849999725818634, 0.04899999871850014, 0.03020000085234642, 0.04089999943971634, 0.13590000569820404, 0.01590000092983246, 0.016200000420212746, 0.012799999676644802, 0.05389999970793724, 0.015399999916553497, 0.013000000268220901, 0.015200000256299973, 0.06840000301599503, 0.03099999949336052, 0.02889999933540821, 0.049300000071525574, 0.1177000030875206, 0.046799998730421066, 0.0560000017285347, 0.0674000009894371, 0.20250000059604645, 0.026900000870227814, 0.015799999237060547, 0.018400000408291817, 0.06769999861717224, 0.013299999758601189, 0.009700000286102295, 0.009600000455975533, 0.03319999948143959, 0.023499999195337296, 0.024700000882148743, 0.06599999964237213, 0.04699999839067459, 0.022299999371170998, 0.06840000301599503, 0.08169999718666077, 0.12200000137090683, 0.010700000450015068, 0.0142000000923872, 0.01860000006854534, 0.041999999433755875, 0.009200000204145908, 0.006599999964237213, 0.01549999974668026, 0.01730000041425228, 0.02410000003874302, 0.02810000069439411, 0.10459999740123749, 0.07100000232458115, 0.020400000736117363, 0.09690000116825104, 0.15240000188350677, 0.09470000118017197, 0.009499999694526196, 0.013299999758601189, 0.039400000125169754, 0.03909999877214432, 0.007000000216066837, 0.012900000438094139, 0.01360000018030405, 0.00989999994635582, 0.05000000074505806, 0.10570000112056732, 0.09459999948740005, 0.08479999750852585, 0.0430000014603138, 0.1177000030875206, 0.09070000052452087, 0.08169999718666077, 0.008799999952316284, 0.018200000748038292, 0.06419999897480011, 0.04859999939799309, 0.011699999682605267, 0.022700000554323196, 0.05469999834895134, 0.03720000013709068, 0.06459999829530716, 0.093299999833107, 0.15809999406337738, 0.15289999544620514, 0.0494999997317791, 0.05950000137090683, 0.12929999828338623, 0.11240000277757645, 0.011300000362098217, 0.03680000081658363, 0.13169999420642853, 0.07530000060796738, 0.026799999177455902, 0.013000000268220901, 0.021800000220537186, 0.03460000082850456, 0.027499999850988388, 0.04529999941587448, 0.06939999759197235, 0.12280000001192093, 0.05299999937415123, 0.08389999717473984, 0.11110000312328339, 0.06790000200271606, 0.021400000900030136, 0.05640000104904175, 0.07660000026226044, 0.023800000548362732, 0.03959999978542328, 0.01640000008046627, 0.02319999970495701, 0.055799998342990875, 0.03889999911189079, 0.0357000008225441, 0.08150000125169754, 0.10930000245571136, 0.04780000075697899, 0.06700000166893005, 0.10379999876022339, 0.14419999718666077, 0.03739999979734421, 0.021299999207258224, 0.019700000062584877, 0.060100000351667404, 0.0142000000923872, 0.01730000041425228, 0.03280000016093254, 0.02070000022649765, 0.029999999329447746, 0.05119999870657921, 0.11420000344514847, 0.05050000175833702, 0.03370000049471855, 0.07190000265836716, 0.1242000013589859, 0.10620000213384628, 0.023399999365210533, 0.012400000356137753, 0.06030000001192093, 0.08659999817609787 ```

Expected result with doc IDS

``` [786559, 624520, 726827, 478228, 910443, 562031, 836073, 350515, 81360, 926288, 284987, 238816, 325973, 386465, 222086, 672007, 681296, 118458, 514474, 124745, 841134, 147438, 497229, 638357, 615210, 326242, 870358, 65659, 115992, 782607, 72752, 960557, 347002, 10531, 712060, 592139, 550209, 836076, 848622, 47315, 95832, 928103, 794886, 377281, 928097, 377926, 736827, 235455, 84976, 103598, 639964, 154680, 679251, 497199, 321677, 124164, 916930, 313472, 318892, 736856, 831947, 696595, 940406, 741999, 722818, 103505, 37825, 155950, 345985, 270368, 957972, 130411, 552560, 686710, 144534, 773781, 167832, 254296, 737310, 294368, 938387, 935201, 937942, 836077, 341838, 757955, 289394, 509877, 23353, 359941, 952767, 976170, 810146, 133944, 765305, 723422, 307262, 562022, 809944, 230737] ```

Original response in format (id, knn_dist) by manticoresearch

``` [(81360, 0.66139686), (325973, 0.68221766), (497229, 0.69750249), (47315, 0.71973938), (736827, 0.72894084), (84976, 0.72937477), (736856, 0.74145496), (741999, 0.74830496), (37825, 0.75178444), (270368, 0.75606418), (144534, 0.762124), (254296, 0.76440591), (737310, 0.76475453), (289394, 0.7720077), (23353, 0.7731936), (230737, 0.778316), (55327, 0.78544134), (280393, 0.78602827), (268684, 0.78796375), (58782, 0.79785007)] ```

Original response with score (looks like knn dist) in QDRant

``` [(786559, 0.7532910108566284), (624520, 0.7597317099571228), (726827, 0.7852178812026978), (478228, 0.7859099507331848), (910443, 0.786249577999115), (562031, 0.7894607782363892), (836073, 0.8059011697769165), (350515, 0.8092456459999084), (81360, 0.8132631182670593), (926288, 0.815800666809082), (284987, 0.820436954498291), (238816, 0.8224855065345764), (325973, 0.8259646892547607), (386465, 0.8286159634590149), (222086, 0.8301216959953308), (672007, 0.8306664228439331), (681296, 0.832095742225647), (118458, 0.8321948051452637), (514474, 0.8323566317558289), (124745, 0.8334718942642212), (841134, 0.8341284990310669), (147438, 0.8341652154922485), (497229, 0.8351660966873169), (638357, 0.8358363509178162), (615210, 0.8368045687675476), (326242, 0.8372406959533691), (870358, 0.8387916088104248), (65659, 0.8389719724655151), (115992, 0.8411566615104675), (782607, 0.8415353894233704), (72752, 0.841549813747406), (960557, 0.8426862359046936), (347002, 0.8431869149208069), (10531, 0.8434194326400757), (712060, 0.8434333205223083), (592139, 0.8443025350570679), (550209, 0.8459287285804749), (836076, 0.8469060063362122), (848622, 0.8479349613189697), (95832, 0.8483744263648987), (47315, 0.8483744263648987), (928103, 0.850817084312439), (794886, 0.8522862792015076), (377281, 0.8523092269897461), (928097, 0.8528671860694885), (377926, 0.8536683917045593), (736827, 0.8537802696228027), (235455, 0.8538074493408203), (84976, 0.854034423828125), (103598, 0.8544076085090637), (639964, 0.8554280996322632), (154680, 0.856099545955658), (679251, 0.856232762336731), (497199, 0.8566629886627197), (321677, 0.8578798770904541), (124164, 0.8584803938865662), (916930, 0.8585527539253235), (313472, 0.8589867353439331), (318892, 0.8608410358428955), (736856, 0.8610777854919434), (831947, 0.8626039028167725), (696595, 0.8629926443099976), (940406, 0.86502605676651), (741999, 0.8650462031364441), (722818, 0.866686999797821), (103505, 0.8668213486671448), (37825, 0.8670549988746643), (155950, 0.8678973913192749), (345985, 0.868622362613678), (270368, 0.8695195913314819), (957972, 0.8696350455284119), (130411, 0.8705007433891296), (552560, 0.8711114525794983), (686710, 0.8723277449607849), (144534, 0.8729971647262573), (773781, 0.8730197548866272), (167832, 0.8733871579170227), (254296, 0.8743031024932861), (737310, 0.874502420425415), (938387, 0.8764157295227051), (935201, 0.8766061663627625), (937942, 0.8767733573913574), (836077, 0.8782885670661926), (341838, 0.8783756494522095), (757955, 0.8783957958221436), (289394, 0.8786397576332092), (509877, 0.8788956999778748), (23353, 0.8793143033981323), (359941, 0.8800841569900513), (952767, 0.8800858855247498), (976170, 0.880486786365509), (810146, 0.8805068731307983), (133944, 0.8808507323265076), (765305, 0.8810558319091797), (723422, 0.8815526962280273), (307262, 0.881637692451477), (562022, 0.8817274570465088), (809944, 0.8817701935768127), (230737, 0.8822223544120789), (192664, 0.8826940059661865)] ```

doc id 81360 vector

``` 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```

doc id 786559 vector

``` 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```

[donhardman]

We tested the response on just two documents inserted into the table, and it worked fine. The expected document ID 786559 was coming before 81360, as expected.

MRE-2-doc.txt

We also tried executing the request with max_matches=1000000, which produced the correct response. However, without setting max_matches to the maximum number of documents in the table, we were still unable to see the required document in the first position.

The table showed 29 chunks, so we ran an optimize operation to reduce it to a single chunk. After that, we got results even worse than before when not changing the max_matches parameter.

It seems like there's something wrong with the max_matches parameter, and it's affecting the precision of the results. 🤔

The two screenshots show two different queries. The first one has max_matches disabled and is working with a single disk chunk, while the second query has max_matches enabled. Both queries are using k=1000000, which is the maximum database size.

[glookka]

I had some buddy-related issues reproducing that behavior locally. If you have a pre-built index (preferably with one chunk), please post a link.

[sanikolaev]

If you have a pre-built index

@glookka pls find it here /home/snikolaev/issue_56/bench_table.tgz on dev2. I didn't run merging just in case it may be import to have multiple chunkg for further investigations.

[sanikolaev]

The fixed dump is here /home/snikolaev/issue_56/bench_dump.tgz on dev2.

[glookka]

I optimized the index to a single chunk and ran queries with max_matches=1000000 and with default max_matches. I don't see a difference in query results.

select id, knn_dist() from bench where knn (vector, 1000000, (0.011699999682605267, 0.011500000022351742, 0.008700000122189522, 0.009999999776482582, 0.07850000262260437, 0.10000000149011612, 0.07840000092983246, 0.05299999937415123, 0.052400000393390656, 0.08190000057220459, 0.0658000037074089, 0.057999998331069946, 0.01590000092983246, 0.017000000923871994, 0.04610000178217888, 0.02419999986886978, 0.00839999970048666, 0.006399999838322401, 0.007199999876320362, 0.010200000368058681, 0.030400000512599945, 0.06790000200271606, 0.05889999866485596, 0.057100001722574234, 0.0333000011742115, 0.07859999686479568, 0.08919999748468399, 0.04230000078678131, 0.013799999840557575, 0.013299999758601189, 0.028999999165534973, 0.021900000050663948, 0.008999999612569809, 0.012199999764561653, 0.010700000450015068, 0.01080000028014183, 0.026599999517202377, 0.03849999979138374, 0.057100001722574234, 0.052000001072883606, 0.03550000116229057, 0.04879999905824661, 0.06920000165700912, 0.05310000106692314, 0.014399999752640724, 0.014299999922513962, 0.01489999983459711, 0.02500000037252903, 0.017100000753998756, 0.016100000590085983, 0.010599999688565731, 0.03240000084042549, 0.02710000053048134, 0.04580000042915344, 0.05310000106692314, 0.06239999830722809, 0.031599998474121094, 0.06080000102519989, 0.06610000133514404, 0.08129999786615372, 0.01860000006854534, 0.022700000554323196, 0.011599999852478504, 0.03370000049471855, 0.024700000882148743, 0.011800000444054604, 0.010700000450015068, 0.06390000134706497, 0.039500001817941666, 0.040300000458955765, 0.05249999836087227, 0.0957999974489212, 0.05510000139474869, 0.06759999692440033, 0.08579999953508377, 0.17489999532699585, 0.024399999529123306, 0.02810000069439411, 0.008700000122189522, 0.05119999870657921, 0.01489999983459711, 0.00860000029206276, 0.012400000356137753, 0.035599999129772186, 0.03280000016093254, 0.03869999945163727, 0.046300001442432404, 0.048900000751018524, 0.04919999837875366, 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+--------+------------+
| id     | knn_dist() |
+--------+------------+
| 568969 | 1.13936853 |
| 568975 | 1.62225807 |
| 568976 | 2.13362741 |
| 568970 | 2.76673746 |
| 568984 | 2.87248778 |
| 568978 | 3.29825449 |
| 568972 | 3.30782843 |
| 568985 | 3.39168644 |
| 568971 | 3.67472219 |
| 568983 | 3.88973951 |
| 568980 | 4.22463274 |
| 568981 | 4.55146933 |
| 568974 |   4.772573 |
| 568986 |   5.721325 |
| 568973 |   6.276696 |
| 568987 | 6.43626547 |
| 568968 |   7.522899 |
| 568982 | 7.60857964 |
| 568979 |   7.693015 |
| 568977 |   8.359688 |
+--------+------------+
20 rows in set (4.16 sec)

select id, knn_dist() from bench where knn (vector, 1000000, (0.011699999682605267, 0.011500000022351742, 0.008700000122189522, 0.009999999776482582, 0.07850000262260437, 0.10000000149011612, 0.07840000092983246, 0.05299999937415123, 0.052400000393390656, 0.08190000057220459, 0.0658000037074089, 0.057999998331069946, 0.01590000092983246, 0.017000000923871994, 0.04610000178217888, 0.02419999986886978, 0.00839999970048666, 0.006399999838322401, 0.007199999876320362, 0.010200000368058681, 0.030400000512599945, 0.06790000200271606, 0.05889999866485596, 0.057100001722574234, 0.0333000011742115, 0.07859999686479568, 0.08919999748468399, 0.04230000078678131, 0.013799999840557575, 0.013299999758601189, 0.028999999165534973, 0.021900000050663948, 0.008999999612569809, 0.012199999764561653, 0.010700000450015068, 0.01080000028014183, 0.026599999517202377, 0.03849999979138374, 0.057100001722574234, 0.052000001072883606, 0.03550000116229057, 0.04879999905824661, 0.06920000165700912, 0.05310000106692314, 0.014399999752640724, 0.014299999922513962, 0.01489999983459711, 0.02500000037252903, 0.017100000753998756, 0.016100000590085983, 0.010599999688565731, 0.03240000084042549, 0.02710000053048134, 0.04580000042915344, 0.05310000106692314, 0.06239999830722809, 0.031599998474121094, 0.06080000102519989, 0.06610000133514404, 0.08129999786615372, 0.01860000006854534, 0.022700000554323196, 0.011599999852478504, 0.03370000049471855, 0.024700000882148743, 0.011800000444054604, 0.010700000450015068, 0.06390000134706497, 0.039500001817941666, 0.040300000458955765, 0.05249999836087227, 0.0957999974489212, 0.05510000139474869, 0.06759999692440033, 0.08579999953508377, 0.17489999532699585, 0.024399999529123306, 0.02810000069439411, 0.008700000122189522, 0.05119999870657921, 0.01489999983459711, 0.00860000029206276, 0.012400000356137753, 0.035599999129772186, 0.03280000016093254, 0.03869999945163727, 0.046300001442432404, 0.048900000751018524, 0.04919999837875366, 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+--------+------------+
| id     | knn_dist() |
+--------+------------+
| 568969 | 1.13936853 |
| 568975 | 1.62225807 |
| 568976 | 2.13362741 |
| 568970 | 2.76673746 |
| 568984 | 2.87248778 |
| 568978 | 3.29825449 |
| 568972 | 3.30782843 |
| 568985 | 3.39168644 |
| 568971 | 3.67472219 |
| 568983 | 3.88973951 |
| 568980 | 4.22463274 |
| 568981 | 4.55146933 |
| 568974 |   4.772573 |
| 568986 |   5.721325 |
| 568973 |   6.276696 |
| 568987 | 6.43626547 |
| 568968 |   7.522899 |
| 568982 | 7.60857964 |
| 568979 |   7.693015 |
| 568977 |   8.359688 |
+--------+------------+
20 rows in set (4.16 sec)

[glookka]

Also CREATE TABLE looks like this:

CREATE TABLE `bench` (                                                                                                                                                                                                                        `id` bigint,
`vector` float_vector knn_type='hnsw' knn_dims='960' hnsw_similarity='L2' hnsw_m='32' hnsw_ef_construction='128'
);

It uses L2 distance functions, so cosine similarity calculator doesn't seem relevant.

[glookka]

I identified and fixed the issue in 694cbe5

[sanikolaev]

@glookka how severe was the issue? In what circumstances could it affect the search results relevancy?

[glookka]

@glookka how severe was the issue? In what circumstances could it affect the search results relevancy?

It affected any search with k>limit. So it was quite severe.