Near_vector Not return the expected item

Support
1

I’m running into a very strange issue:

Our collection contains nearly 30,000 records. When I perform a near_vector query with a filter that matches a specific condition, it correctly returns one record (let’s call it Record A), and its certainty score is 0.99 (, actually the same vector).

However, if I run the same near_vector query without any filter, even with limit=1000, Record A doesn’t appear in the results at all—the 1000th result only has a certainty around 0.87.

Is this a bug?

The code is:

rslt = await collection.query.near_vector(
    filters=filters,
    near_vector=same_vector,
    certainty=0,
    limit=1000,
    offset=0,
    return_metadata=MetadataQuery(distance=True, certainty=True, score=True)
)

The collection info

{
  "class": "PtAi_embedding_39fa06cf_4178_e8bc_8172_9869630559c1_productsSpPicNew",
  "invertedIndexConfig": {
    "bm25": {
      "b": 0.75,
      "k1": 1.2
    },
    "cleanupIntervalSeconds": 60,
    "stopwords": {
      "additions": null,
      "preset": "en",
      "removals": null
    },
    "usingBlockMaxWAND": true
  },
  "multiTenancyConfig": {
    "autoTenantActivation": false,
    "autoTenantCreation": false,
    "enabled": false
  },
  "properties": [
    {
      "dataType": [
        "text"
      ],
      "description": "Text property",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": true,
      "name": "text",
      "tokenization": "word"
    },
    {
      "dataType": [
        "text"
      ],
      "description": "The ref_doc_id of the Node",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": true,
      "name": "ref_doc_id",
      "tokenization": "word"
    },
    {
      "dataType": [
        "text"
      ],
      "description": "node_info (in JSON)",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": true,
      "name": "node_info",
      "tokenization": "word"
    },
    {
      "dataType": [
        "text"
      ],
      "description": "The relationships of the node (in JSON)",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": true,
      "name": "relationships",
      "tokenization": "word"
    },
    {
      "dataType": [
        "text"
      ],
      "description": "This property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": true,
      "name": "inputs_image",
      "tokenization": "word"
    },
    {
      "dataType": [
        "text"
      ],
      "description": "This property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": true,
      "name": "inputs_image_type",
      "tokenization": "word"
    },
    {
      "dataType": [
        "object"
      ],
      "description": "This property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": false,
      "name": "metadata",
      "nestedProperties": [
        {
          "dataType": [
            "text"
          ],
          "description": "This nested property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
          "indexFilterable": true,
          "indexRangeFilters": false,
          "indexSearchable": true,
          "name": "model",
          "tokenization": "word"
        },
        {
          "dataType": [
            "uuid"
          ],
          "description": "This nested property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
          "indexFilterable": true,
          "indexRangeFilters": false,
          "indexSearchable": false,
          "name": "_id"
        },
        {
          "dataType": [
            "text[]"
          ],
          "description": "This nested property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
          "indexFilterable": true,
          "indexRangeFilters": false,
          "indexSearchable": true,
          "name": "unsupported_types",
          "tokenization": "word"
        }
      ]
    },
    {
      "dataType": [
        "object"
      ],
      "description": "This property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": false,
      "name": "inputs",
      "nestedProperties": [
        {
          "dataType": [
            "text"
          ],
          "description": "This nested property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
          "indexFilterable": true,
          "indexRangeFilters": false,
          "indexSearchable": true,
          "name": "text",
          "tokenization": "word"
        },
        {
          "dataType": [
            "text"
          ],
          "description": "This nested property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
          "indexFilterable": true,
          "indexRangeFilters": false,
          "indexSearchable": true,
          "name": "image",
          "tokenization": "word"
        },
        {
          "dataType": [
            "text"
          ],
          "description": "This nested property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
          "indexFilterable": true,
          "indexRangeFilters": false,
          "indexSearchable": true,
          "name": "image_type",
          "tokenization": "word"
        }
      ]
    },
    {
      "dataType": [
        "text[]"
      ],
      "description": "This property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": true,
      "name": "metadata_unsupported_types",
      "tokenization": "word"
    },
    {
      "dataType": [
        "text"
      ],
      "description": "This property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": true,
      "name": "metadata_model",
      "tokenization": "word"
    },
    {
      "dataType": [
        "uuid"
      ],
      "description": "This property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": false,
      "name": "metadata__id"
    },
    {
      "dataType": [
        "text"
      ],
      "description": "This property was generated by Weaviate's auto-schema feature on Mon Dec 15 09:17:12 2025",
      "indexFilterable": true,
      "indexRangeFilters": false,
      "indexSearchable": true,
      "name": "inputs_text",
      "tokenization": "word"
    }
  ],
  "replicationConfig": {
    "asyncEnabled": false,
    "deletionStrategy": "NoAutomatedResolution",
    "factor": 1
  },
  "shardingConfig": {
    "actualCount": 1,
    "actualVirtualCount": 128,
    "desiredCount": 1,
    "desiredVirtualCount": 128,
    "function": "murmur3",
    "key": "_id",
    "strategy": "hash",
    "virtualPerPhysical": 128
  },
  "vectorIndexConfig": {
    "bq": {
      "enabled": false
    },
    "cleanupIntervalSeconds": 300,
    "distance": "cosine",
    "dynamicEfFactor": 8,
    "dynamicEfMax": 500,
    "dynamicEfMin": 100,
    "ef": -1,
    "efConstruction": 64,
    "filterStrategy": "sweeping",
    "flatSearchCutoff": 40000,
    "maxConnections": 32,
    "multivector": {
      "aggregation": "maxSim",
      "enabled": false,
      "muvera": {
        "dprojections": 16,
        "enabled": false,
        "ksim": 4,
        "repetitions": 10
      }
    },
    "pq": {
      "bitCompression": false,
      "centroids": 256,
      "enabled": false,
      "encoder": {
        "distribution": "log-normal",
        "type": "kmeans"
      },
      "segments": 0,
      "trainingLimit": 100000
    },
    "rq": {
      "bits": 8,
      "enabled": true,
      "rescoreLimit": 20
    },
    "skip": false,
    "skipDefaultQuantization": false,
    "sq": {
      "enabled": false,
      "rescoreLimit": 20,
      "trainingLimit": 100000
    },
    "trackDefaultQuantization": true,
    "vectorCacheMaxObjects": 20000
  },
  "vectorIndexType": "hnsw",
  "vectorizer": "none"
}
  • Weaviate Version: 1.33.9
  • Deployment Method: k8s
  • Multi Node? Number of Running Nodes: Single
  • Client Language and Version: Python 3.11.2
  • Multitenancy?: No
2

hi @Charlie_Chen !

Can you share the filters you are using? A common mistake that can lead to this behavior is filtering on a word tokenized property expecting it to behave like a filter tokenized property.

Check here more about tokenization in Weaviate: Configure tokenization for keyword search | Weaviate Documentation

Thanks!

3

Actually, I’m not using any filter at all. I’ve simplified my code for clarity.

here it is:


# the vector was copied from the weaviate using curl 'http://weaviate.weaviate.svc.cluster.local/v1/objects/PtAi_embedding_39fa06cf_4178_e8bc_8172_9869630559c1_productsSpPicNew/48217fbb-b790-4268-891d-7d0540ec38f7?include=vector&consistency_level=&node_name=&tenant='
vector = [0.02058773,0.00096861296,0.00069703767,0.017659662,0.017947042,0.043837707,0.024906829,0.0057961713,0.0026296955,0.021094147,0.010730339,0.0132259065,0.057855774,0.003471092,0.00758764,0.016447624,0.0029034046,0.025131308,0.02222485,0.020169798,0.103902,0.14338872,0.026818318,0.13391331,0.11602441,0.010837378,0.012711845,0.01599177,0.016491197,0.0042941933,0.029967746,0.030533072,0.039931234,0.028310677,0.026202297,0.004233939,0.015713168,0.0005755227,0.006695112,0.01427786,0.10372469,0.050451268,0.047361143,0.009079552,0.002998895,0.009479757,0.056249842,0.029431373,0.031509735,0.046413444,0.0023861807,0.0015386876,0.015659802,0.0045539513,0.032843146,0.009109186,0.0051608365,0.010172452,0.037128765,0.00462176,0.033522863,0.0030555667,0.0027285193,0.011515605,0.005764216,0.036039244,0.04676599,0.00025033974,0,0.0043272814,0.009604287,0.018739121,0.013747025,0.03479138,0.006745519,0.024179604,0.082598455,0.010930175,0.06568404,0.025104944,0.0039509744,0.06909568,0.006568586,0.034756314,0.0019356147,0.016390651,0.021226525,0.019171938,0.038691808,0.01019403,0.010525446,0.039221972,0.010014031,0.033159755,0.008581228,0.019668791,0.024079753,0.015678143,0.018727098,0.01800988,0.05583978,0.004282286,0.038520698,0.04507832,0.08328284,0.03596932,0.0033958221,0.06773822,0.091201425,0.0049095363,0.16458303,0.115231335,0.0077697965,0.06523707,0.041082617,0.001710193,0.05038138,0.059321724,0.018921098,0.00876948,0.03737273,0.009147611,0,0.02142493,0.014461956,0.08313503,0.05743388,0.0071350727,0.01702651,0.0030192314,0.0031295198,0.11593865,0.02522434,0.028298913,0.026351279,0.008461106,0.0120715555,0.0003701881,0.13349445,0.07251561,0.002369519,0.027548868,0.02061619,0.03847075,0.0052352436,0.03188234,0.034953985,0.013638123,0.039899155,0.009553041,0.048398122,0.0035080467,0.0392726,0.049613073,0.0798299,0.0045051873,0.0031299859,0.017349545,0.02268955,0.022654142,0.017635968,0.0015800287,0.03432567,0.009481527,0.016601935,0.023362322,0.004465202,0.030435178,0.07375209,0.05277933,0.0016512051,0.040116396,0.0036923324,0.025721002,0.0225879,0.002398364,0.111542456,0.02949407,0.02124639,0.026717348,0.0020867244,0.0014990416,0.0093988255,0.001536869,0.015219315,0.05583002,0.023024777,0.0033639434,0.0009822911,0.0024561465,0.0016340633,0.057263453,0.026385657,0.04214523,0.084245235,0.007427427,0.045563783,0.027275287,0.057240255,0.039437804,0.003686577,0.029334024,0.026070269,0.012572837,0.061546903,0.0002052931,0.059056196,0.065873034,0.018163126,0.053173248,0.03984779,0.03368375,0.011833427,0.056735005,0.0051745134,0.061360516,0.0014302823,0.033203263,0.033403262,0.023045,0.032962915,0.06637534,0.0014381331,0.017395472,0.0074583865,0.012279982,0.021530015,0.019553842,0.02427525,0.022855777,0.014247166,0.060121465,0.0038577158,0.024245517,0.023641022,0.023838418,0.012994555,0.020584963,0.03699796,0.08667564,0.03209398,0.0134564685,0.08556647,0.019794853,0.030652737,0.013650822,0.0020945533,0.05491152,0.029724251,0.001350065,0.020105958,0.025364924,0.0055205957,0.039270114,0.014302903,0.0019014124,0.014584405,0.0077861906,0.003931034,0.0046032304,0.042680413,0.016913973,0.0038980038,0.008756306,0.04346068,0.02212312,0.01018352,0.00044517146,0.031968113,0.055192433,0.057106387,0.02010934,0.1286237,0.01244683,0.040695768,0.0032894625,0.053801395,0.024322746,0.020334616,0.0051173065,0.020787153,0.040613346,0.047419917,0.010835292,0.038950473,0.019583788,0.038273487,0.025340907,0.012060771,0.05220212,0.067412145,0.029580232,0.02328204,0.012306016,0.0135568995,0.0043167127,0.0042774724,0.022766028,0.0039494,0.003813017,0.060913336,0.0076949587,0.0019143438,0.01001591,0.05003908,0.040920336,0.00878025,0.050027605,0.022928968,0.028406108,0.07804916,0.04437423,0.041422784,0.046604536,0.016216937,0.0077145053,0.002613562,0.0687624,0.008701484,0.19065186,0.039447527,0.02555107,0.012735183,0.04803874,0.00469242,0.03154528,0.02951449,0.05535047,0.008105284,0.008177091,0.002968748,0.05837226,0.08886379,0.11838357,0.026868701,0.027113002,0.030060701,0.009322138,0.02018495,0.0039649704,0.027697233,0.008161825,0.009693676,0.010220863,0.0031626383,0.082749575,0.054939575,0.038110916,0.034912683,0.010131087,0.011370346,0.057938237,0.08892187,0.012848857,0.019860443,0.0053230734,0.011946854,0.033401396,0.049990166,0.0019955013,0.04315355,0.11279945,0.004404709,0.0033230162,0.0602137,0.026459796,0.017668175,0.09215303,0.16937432,0.044538293,0.097561866,0.0060491753,0.03177967,0.00078421243,0.0012723329,0.008794589,0.040264983,0.1205784,0.06253952,0.04197396,0.004132231,0.027313706,0.045360904,0.05483255,0.017880313,0.00060604385,0.015652195,0.013318156,0.090027474,0.014091681,0.031692877,0.035685148,0.014049576,0.029989706,0.023588626,0.0070320247,0.0439034,0.010573158,0.04063669,0.010533819,0.04093738,0.011462805,0.006737995,0.10125126,0.017334165,0.009791187,0.007790154,0.045690943,0.1085405,0.008286049,0.0064926655,0.042357583,0.057971455,0.140543,0.015223681,0.031107388,0.008365173,0.0004262152,0.009696263,0.011125846,0.26248094,0.046542265,0.045199163,0.014580864,0.001396276,0.036396045,0.0049281265,0.06189366,0.011291265,0.030412056,0.07895009,0.017477997,0.014442461,0.025470596,0.009277277,0.104354426,0.09510565,0.03158634,0.013938844,0.01545274,0.018710116,0.00372387,0.06180252,0.029388307,0.011850688,0.0106145935,0.0024857104,0.027768554,0.025145452,0.012501429,0.052493382,0.013151021,0.1001395,0.02558369,0.026335265,0.051633615,0.0067370227,0.00780829,0.0013285201,0.0054016267,0.012850375,0.0032250704,0.029540272,0.16308032,0.058022536,0.028560763,0.037246063,0.0075988304,0.04398654,0.0061650244,0.018529,0.107092656,0.024220696,0.04563295,0.06846713,0.021772433,0.008103699,0.0040668235,0.059709232,0.00969549,0.00074186357,0.000035694244,0.016792433,0.02637813,0.010836435,0.0063188765,0.008314738,0.017607218,0.00854898,0.022056466,0.053785037,0.0063147214,0.040422883,0.02721995,0.001447414,0.038768385,0.0598553,0.00012572174,0.023847641,0.027893068,0.00069607317,0.07854251,0.09753371,0.0301962,0.06452993,0.006031201,0.015167055,0.014845649,0.057808578,0.038010854,0.008326757,0.056766648]


rslt = await collection.query.near_vector(
	filters=None,
	near_vector=vector,
	certainty=0,
	limit=100,
	offset=0,
	return_metadata=MetadataQuery(distance=True, certainty=True, score=True)
)
for itm in rslt.objects:
	logger.info(f"========={itm.metadata.certainty}:  {itm.uuid}========")


# the output
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9599666595458984:  94f2ca78-9045-4d04-a56a-8457f51211a2========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9523476362228394:  4af274b9-a9f5-4536-949f-bc28c040354e========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.947085976600647:  2e2b1f5b-0eff-4679-8245-1c345376ca76========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9468532800674438:  eef47c71-4947-419f-aebd-296a18a28a3f========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.946763277053833:  016fa748-2cec-4d32-9471-ed6b86da2d1a========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9465303421020508:  9c18100d-1f89-4661-abd7-a1fbbcec2574========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9435456991195679:  046e3a7e-53f7-496b-9ea4-d42aa4235453========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9418513178825378:  73f0875d-26ec-4237-9c7b-9f656ee6be0e========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9411472082138062:  c5b2b52a-b406-47c5-930e-1b19c54cac67========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9353201389312744:  8ef898ca-425f-4f3e-b0d1-edbe050a1a1e========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9350724220275879:  6f5c5738-a5a0-42ad-bc81-99e3f5980c22========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9350379705429077:  e319f720-fdd1-485e-a613-c92731e01547========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9344598650932312:  8ed1e942-390b-4251-9b6d-488539abbe4c========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}
{"funcName": "anear_vector", "lineno": 94, "message": "=========0.9317565560340881:  bc74b086-bc12-4742-9616-d2cc6df175f0========", "CorrelationId": "58c0e65e-ee8b-45db-a70f-576f0c3887b9", "timestamp": "2026-02-05 03:14:38", "level": "INFO", "category": "app.infrastructure.factories.weaviate_store_wrapper"}

As you can see, the first id of the output list is “94f2ca78-9045-4d04-a56a-8457f51211a2” which is not the same as the expected id in the curl (48217fbb-b790-4268-891d-7d0540ec38f7). And it even not existed in the above list.

The result of the curl
curl 'http://weaviate.weaviate.svc.cluster.local/v1/objects/PtAi_embedding_39fa06cf_4178_e8bc_8172_9869630559c1_productsSpPicNew/48217fbb-b790-4268-891d-7d0540ec38f7?include=vector&consistency_level=&node_name=&tenant='

The result:

{"class":"PtAi_embedding_39fa06cf_4178_e8bc_8172_9869630559c1_productsSpPicNew","creationTimeUnix":1768365506285,"id":"48217fbb-b790-4268-891d-7d0540ec38f7","lastUpdateTimeUnix":1768365506285,"properties":{"inputs":{"image":"ProductPic/2024/08/12/66b98123327ffa00029dda03.jpg","image_type":"filekey","text":""},"inputs_image":"ProductPic/2024/08/12/66b98123327ffa00029dda03.jpg","inputs_image_type":"filekey","inputs_text":"","metadata":{"_id":"b1196447-67f2-4eb5-aae6-96d5f47ad562","model":"pytorch.resnet34","unsupported_types":[]},"metadata__id":"b1196447-67f2-4eb5-aae6-96d5f47ad562","metadata_model":"pytorch.resnet34","metadata_unsupported_types":[]},"vector":[0.02058773,0.00096861296,0.00069703767,0.017659662,0.017947042,0.043837707,0.024906829,0.0057961713,0.0026296955,0.021094147,0.010730339,0.0132259065,0.057855774,0.003471092,0.00758764,0.016447624,0.0029034046,0.025131308,0.02222485,0.020169798,0.103902,0.14338872,0.026818318,0.13391331,0.11602441,0.010837378,0.012711845,0.01599177,0.016491197,0.0042941933,0.029967746,0.030533072,0.039931234,0.028310677,0.026202297,0.004233939,0.015713168,0.0005755227,0.006695112,0.01427786,0.10372469,0.050451268,0.047361143,0.009079552,0.002998895,0.009479757,0.056249842,0.029431373,0.031509735,0.046413444,0.0023861807,0.0015386876,0.015659802,0.0045539513,0.032843146,0.009109186,0.0051608365,0.010172452,0.037128765,0.00462176,0.033522863,0.0030555667,0.0027285193,0.011515605,0.005764216,0.036039244,0.04676599,0.00025033974,0,0.0043272814,0.009604287,0.018739121,0.013747025,0.03479138,0.006745519,0.024179604,0.082598455,0.010930175,0.06568404,0.025104944,0.0039509744,0.06909568,0.006568586,0.034756314,0.0019356147,0.016390651,0.021226525,0.019171938,0.038691808,0.01019403,0.010525446,0.039221972,0.010014031,0.033159755,0.008581228,0.019668791,0.024079753,0.015678143,0.018727098,0.01800988,0.05583978,0.004282286,0.038520698,0.04507832,0.08328284,0.03596932,0.0033958221,0.06773822,0.091201425,0.0049095363,0.16458303,0.115231335,0.0077697965,0.06523707,0.041082617,0.001710193,0.05038138,0.059321724,0.018921098,0.00876948,0.03737273,0.009147611,0,0.02142493,0.014461956,0.08313503,0.05743388,0.0071350727,0.01702651,0.0030192314,0.0031295198,0.11593865,0.02522434,0.028298913,0.026351279,0.008461106,0.0120715555,0.0003701881,0.13349445,0.07251561,0.002369519,0.027548868,0.02061619,0.03847075,0.0052352436,0.03188234,0.034953985,0.013638123,0.039899155,0.009553041,0.048398122,0.0035080467,0.0392726,0.049613073,0.0798299,0.0045051873,0.0031299859,0.017349545,0.02268955,0.022654142,0.017635968,0.0015800287,0.03432567,0.009481527,0.016601935,0.023362322,0.004465202,0.030435178,0.07375209,0.05277933,0.0016512051,0.040116396,0.0036923324,0.025721002,0.0225879,0.002398364,0.111542456,0.02949407,0.02124639,0.026717348,0.0020867244,0.0014990416,0.0093988255,0.001536869,0.015219315,0.05583002,0.023024777,0.0033639434,0.0009822911,0.0024561465,0.0016340633,0.057263453,0.026385657,0.04214523,0.084245235,0.007427427,0.045563783,0.027275287,0.057240255,0.039437804,0.003686577,0.029334024,0.026070269,0.012572837,0.061546903,0.0002052931,0.059056196,0.065873034,0.018163126,0.053173248,0.03984779,0.03368375,0.011833427,0.056735005,0.0051745134,0.061360516,0.0014302823,0.033203263,0.033403262,0.023045,0.032962915,0.06637534,0.0014381331,0.017395472,0.0074583865,0.012279982,0.021530015,0.019553842,0.02427525,0.022855777,0.014247166,0.060121465,0.0038577158,0.024245517,0.023641022,0.023838418,0.012994555,0.020584963,0.03699796,0.08667564,0.03209398,0.0134564685,0.08556647,0.019794853,0.030652737,0.013650822,0.0020945533,0.05491152,0.029724251,0.001350065,0.020105958,0.025364924,0.0055205957,0.039270114,0.014302903,0.0019014124,0.014584405,0.0077861906,0.003931034,0.0046032304,0.042680413,0.016913973,0.0038980038,0.008756306,0.04346068,0.02212312,0.01018352,0.00044517146,0.031968113,0.055192433,0.057106387,0.02010934,0.1286237,0.01244683,0.040695768,0.0032894625,0.053801395,0.024322746,0.020334616,0.0051173065,0.020787153,0.040613346,0.047419917,0.010835292,0.038950473,0.019583788,0.038273487,0.025340907,0.012060771,0.05220212,0.067412145,0.029580232,0.02328204,0.012306016,0.0135568995,0.0043167127,0.0042774724,0.022766028,0.0039494,0.003813017,0.060913336,0.0076949587,0.0019143438,0.01001591,0.05003908,0.040920336,0.00878025,0.050027605,0.022928968,0.028406108,0.07804916,0.04437423,0.041422784,0.046604536,0.016216937,0.0077145053,0.002613562,0.0687624,0.008701484,0.19065186,0.039447527,0.02555107,0.012735183,0.04803874,0.00469242,0.03154528,0.02951449,0.05535047,0.008105284,0.008177091,0.002968748,0.05837226,0.08886379,0.11838357,0.026868701,0.027113002,0.030060701,0.009322138,0.02018495,0.0039649704,0.027697233,0.008161825,0.009693676,0.010220863,0.0031626383,0.082749575,0.054939575,0.038110916,0.034912683,0.010131087,0.011370346,0.057938237,0.08892187,0.012848857,0.019860443,0.0053230734,0.011946854,0.033401396,0.049990166,0.0019955013,0.04315355,0.11279945,0.004404709,0.0033230162,0.0602137,0.026459796,0.017668175,0.09215303,0.16937432,0.044538293,0.097561866,0.0060491753,0.03177967,0.00078421243,0.0012723329,0.008794589,0.040264983,0.1205784,0.06253952,0.04197396,0.004132231,0.027313706,0.045360904,0.05483255,0.017880313,0.00060604385,0.015652195,0.013318156,0.090027474,0.014091681,0.031692877,0.035685148,0.014049576,0.029989706,0.023588626,0.0070320247,0.0439034,0.010573158,0.04063669,0.010533819,0.04093738,0.011462805,0.006737995,0.10125126,0.017334165,0.009791187,0.007790154,0.045690943,0.1085405,0.008286049,0.0064926655,0.042357583,0.057971455,0.140543,0.015223681,0.031107388,0.008365173,0.0004262152,0.009696263,0.011125846,0.26248094,0.046542265,0.045199163,0.014580864,0.001396276,0.036396045,0.0049281265,0.06189366,0.011291265,0.030412056,0.07895009,0.017477997,0.014442461,0.025470596,0.009277277,0.104354426,0.09510565,0.03158634,0.013938844,0.01545274,0.018710116,0.00372387,0.06180252,0.029388307,0.011850688,0.0106145935,0.0024857104,0.027768554,0.025145452,0.012501429,0.052493382,0.013151021,0.1001395,0.02558369,0.026335265,0.051633615,0.0067370227,0.00780829,0.0013285201,0.0054016267,0.012850375,0.0032250704,0.029540272,0.16308032,0.058022536,0.028560763,0.037246063,0.0075988304,0.04398654,0.0061650244,0.018529,0.107092656,0.024220696,0.04563295,0.06846713,0.021772433,0.008103699,0.0040668235,0.059709232,0.00969549,0.00074186357,0.000035694244,0.016792433,0.02637813,0.010836435,0.0063188765,0.008314738,0.017607218,0.00854898,0.022056466,0.053785037,0.0063147214,0.040422883,0.02721995,0.001447414,0.038768385,0.0598553,0.00012572174,0.023847641,0.027893068,0.00069607317,0.07854251,0.09753371,0.0301962,0.06452993,0.006031201,0.015167055,0.014845649,0.057808578,0.038010854,0.008326757,0.056766648],"vectorWeights":null}
4

I also tried setting limit to 50,000 (even though the collection only contains 29,155 objects), but near_vector returned only 26,576 results—missing about 3,000 records—and the expected item was still not among them.

5

Hi @Charlie_Chen !!

Can you re ingest this dataset in a new collection or cluster?

If this fixes it, it may be an indexing issue :thinking:

The way it is, I cannot reproduce it as I do not have the dataset. So this could test eliminate indexing issues.

Or, can you reproduce this issue with a dataset that can be shared on a brand new weaviate on latest version?

And finally, the latest patch for 1.33 is 1.33.16, can you try upgrading to this last version an check if issue persists?

Thanks!

6

Thanks, @DudaNogueira

I’ll try the latest patch 1.33.16 firstly. And if it dose not work, is there a convenient way to ingest this dataset in a new collection for 29,155 objects ?

7

You can use this as a guide, and migrate a collection to the same cluster, triggering the reindex: Migrate data | Weaviate Documentation

8

I have upgraded to 1.33.16. Sadly, it still doesn’t work.

I’ll try re ingest later. But if re ingest fixes the issue, how can I avoid this problem in the future?
And if it’s still not working, are there any other ways I can try to resolve it?

9

If re ingesting fixes, then we will need to dig deeper to understand what went wrong. From experience, it can happen if it runs out of disk, or other event that can corrupt some index in some edge cases.

If it doesn’t, then we can try reproducing this with some synthetic data so we can raise an issue :slight_smile:

Thanks!