Vector similarity search
Vector search returns the objects with most similar vectors to that of the query.
Search with text
Use the Near Text
operator to find objects with the nearest vector to an input text.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- Java
- GraphQL
from weaviate.classes.query import MetadataQuery
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.near_text(
query="animals in movies",
limit=2,
return_metadata=MetadataQuery(distance=True)
)
for o in response.objects:
print(o.properties)
print(o.metadata.distance)
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_near_text({
"concepts": ["animals in movies"]
})
.with_limit(2)
.with_additional(["distance"])
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.nearText('animals in movies', {
limit: 2,
returnMetadata: ['distance']
})
result.objects.forEach(item => {
console.log(JSON.stringify(item.properties, null, 2))
console.log(item.metadata?.distance)
})
let result;
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withNearText({ concepts: ['animals in movies'] })
.withLimit(2)
.withFields('question answer _additional { distance }')
.do();
console.log(JSON.stringify(result, null, 2));
response, err := client.GraphQL().Get().
WithClassName("JeopardyQuestion").
WithFields(
graphql.Field{Name: "question"},
graphql.Field{Name: "answer"},
graphql.Field{
Name: "_additional",
Fields: []graphql.Field{
{Name: "distance"},
},
},
).
WithNearText(client.GraphQL().NearTextArgBuilder().
WithConcepts([]string{"animals in movies"})).
WithLimit(2).
Do(ctx)
NearTextArgument nearText = NearTextArgument.builder()
.concepts(new String[]{ "animals in movies" })
.build();
Fields fields = Fields.builder()
.fields(new Field[]{
Field.builder().name("question").build(),
Field.builder().name("answer").build(),
Field.builder().name("_additional").fields(new Field[]{
Field.builder().name("distance").build()
}).build()
})
.build();
String query = GetBuilder.builder()
.className(className)
.fields(fields)
.withNearTextFilter(nearText)
.limit(2)
.build()
.buildQuery();
Result<GraphQLResponse> result = client.graphQL().raw().withQuery(query).run();
{
Get {
JeopardyQuestion(
limit: 2
nearText: {
concepts: ["animals in movies"]
}
) {
question
answer
_additional {
distance
}
}
}
}
Example response
The output is like this:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"answer": "meerkats",
"question": "Group of mammals seen <a href=\"http://www.j-archive.com/media/1998-06-01_J_28.jpg\" target=\"_blank\">here</a>: [like Timon in <i>The Lion King</i>]",
"_additional": { "distance": 0.17602634 }
},
{
"answer": "dogs",
"question": "Scooby-Doo, Goofy & Pluto are cartoon versions",
"_additional": { "distance": 0.17842108 }
}
]
}
}
}
Search with image
Use the Near Image
operator to find objects with the nearest vector to an image.
This example uses a base64 representation of an image.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Java
base64_string="SOME_BASE_64_REPRESENTATION"
# Get the collection containing images
dogs = client.collections.get("Dog")
# Perform query
response = dogs.query.near_image(
near_image=base64_string,
return_properties=["breed"],
limit=1,
# targetVector: "vector_name" # required when using multiple named vectors
)
print(response.objects[0])
client.close()
base64_string="SOME_BASE_64_REPRESENTATION"
# Perform query
response = (
client.query
.get("Dog", "breed")
.with_near_image(
{"image": base64_string},
encode=False # False because the image is already base64-encoded
)
.with_limit(1)
.do()
)
print(json.dumps(response, indent=2))
import { toBase64FromMedia } from 'weaviate-client';
const myCollection = client.collections.get('Dog');
const filePath = './images/search-image.jpg'
const base64String = await toBase64FromMedia(file.path)
// Perform query
const result = await myCollection.query.nearImage(base64String, {
returnProperties: ['breed'],
limit: 1,
// targetVector: 'vector_name' // required when using multiple named vectors
})
console.log(JSON.stringify(result.objects, null, 2));
const base64String = 'SOME_BASE_64_REPRESENTATION';
// Perform query
let result = await client.graphql
.get()
.withClassName('Dog')
.withNearImage({
image: base64String,
})
.withLimit(1)
.withFields('breed')
.do();
console.log(JSON.stringify(result, null, 2));
String base64_string = "SOME_BASE_64_REPRESENTATION";
NearImageArgument nearImage = NearImageArgument.builder()
.image(base64_string)
.build();
Fields fields = Fields.builder()
.fields(new Field[]{
Field.builder().name("Breed").build()
})
.build();
String query = GetBuilder.builder()
.className(className)
.fields(fields)
.withNearImageFilter(nearImage)
.limit(1)
.build()
.buildQuery();
Result<GraphQLResponse> result = client.graphQL().raw().withQuery(query).run();
See Image search for more information.
Search with an existing object
If you have an object ID, use the Near Object
operator to find similar objects to that object.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- Java
- GraphQL
from weaviate.classes.query import MetadataQuery
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.near_object(
near_object=uuid, # A UUID of an object (e.g. "56b9449e-65db-5df4-887b-0a4773f52aa7")
limit=2,
return_metadata=MetadataQuery(distance=True)
)
for o in response.objects:
print(o.properties)
print(o.metadata.distance)
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_near_object({
"id": "56b9449e-65db-5df4-887b-0a4773f52aa7"
})
.with_limit(2)
.with_additional(["distance"])
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.nearObject('56b9449e-65db-5df4-887b-0a4773f52aa7', {
limit: 2,
returnMetadata: ['distance']
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
console.log(JSON.stringify(object.metadata?.distance, null, 2));
}
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.nearObject('56b9449e-65db-5df4-887b-0a4773f52aa7', {
limit: 2,
returnMetadata: ['distance']
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
console.log(JSON.stringify(object.metadata?.distance, null, 2));
}
response, err := client.GraphQL().Get().
WithClassName("JeopardyQuestion").
WithFields(
graphql.Field{Name: "question"},
graphql.Field{Name: "answer"},
graphql.Field{
Name: "_additional",
Fields: []graphql.Field{
{Name: "distance"},
},
},
).
WithNearObject(client.GraphQL().NearObjectArgBuilder().
WithID("56b9449e-65db-5df4-887b-0a4773f52aa7")).
WithLimit(2).
Do(ctx)
String id = "0b7235c5-86f1-48a9-baab-3b9571dca854";
NearObjectArgument nearObject = NearObjectArgument.builder()
.id(id)
.build();
String query = GetBuilder.builder()
.className(className)
.withNearObjectFilter(nearObject)
.limit(2)
.build()
.buildQuery();
Result<GraphQLResponse> result = client.graphQL().raw().withQuery(query).run();
Additional information
Search with a vector
If you have an input vector, use the Near Vector
operator to find objects with similar vectors
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- Java
- GraphQL
from weaviate.classes.query import MetadataQuery
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.near_vector(
near_vector=query_vector, # your query vector goes here
limit=2,
return_metadata=MetadataQuery(distance=True)
)
for o in response.objects:
print(o.properties)
print(o.metadata.distance)
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_near_vector({
"vector": [-0.0125526935, -0.021168863, -0.01076519, -0.02589537, -0.0070362035, 0.019870078, -0.010001986, -0.019120263, 0.00090044655, -0.017393013, 0.021302758, 0.010055545, 0.02937665, -0.003816019, 0.007692291, 0.012385325, 0.032750815, 0.020847514, 0.020311933, -0.022159688, -0.0009924996, 0.009399457, 0.0022226637, -0.029510546, 0.014393755, -0.007223657, 0.018276723, -0.03639277, -0.010001986, -0.022842556, 0.010363504, -0.020927852, -0.006929087, -0.022521207, -0.007652122, -0.011126708, 0.0279038, -0.01721895, 0.016482525, 0.002281243, -0.00169294, 0.009191919, -0.019655844, -0.022869334, -0.012412104, 0.0031967526, -0.0033457114, -0.01483561, -0.03173321, 0.004746592, 0.010095714, 0.007973471, -0.032134898, -0.023739655, -0.008040419, 0.018290112, -0.013637247, -0.008488968, 0.024623364, -0.039365247, -0.0032586793, 0.0009606995, -0.029510546, 0.0063265576, -0.019602288, 0.003081268, 0.013463182, -0.006601043, 0.019910246, -0.01542475, 0.0367409, -0.01193008, 0.012961075, -0.015625594, 0.0062462203, -0.0058646183, -0.0059248717, 0.01889264, 0.008127451, 0.0037155973, 0.037142586, -0.025373178, -0.005503101, 0.014982895, 0.035053816, -0.012432188, -0.017285896, 0.022936283, 0.0024620018, 0.016937768, -0.0062127467, 0.02154377, 0.0066378643, 0.029698, 0.0013071538, 0.0043850746, -0.008040419, 0.024797428, -0.012452273, -0.025132166, -0.0031900578, 0.0000019433794, -0.002378317, -0.008629559, 0.0126732, -0.0022494427, 0.0009623732, 0.0035582704, 0.017312676, -0.024569806, -0.008890655, 0.023056788, 0.014902558, -0.047104403, -0.009011161, -0.030447815, 0.017982153, -0.0042009684, -0.00654079, 0.00069249026, 0.011936775, 0.023378137, 0.025105387, -0.009245478, 0.030929837, 0.00394322, 0.02123581, -0.0042545265, 0.0022578111, -0.017259117, 0.047157962, -0.00022029977, 0.03497348, -0.00072094303, -0.023605758, 0.036499888, -0.015384582, 0.011099929, -0.0139519, -0.03408977, 0.013155223, 0.030501373, -0.026698742, 0.004311432, -0.010236303, 0.011361024, 0.023793213, -0.00014874942, 0.0020352101, 0.0026829292, 0.00989487, 0.0074780583, 0.02734144, 0.003826061, 0.011722542, 0.00712993, -0.013992069, 0.0009406152, 0.010785274, -0.012325072, 0.01692438, 0.010617905, 0.016750315, -0.0070295087, 0.017687583, 0.038320865, 0.020485997, 0.005054551, -0.018812304, 0.0007201062, 0.0015381235, 0.0349467, 0.014728494, 0.050773136, -0.017901815, 0.0027716348, 0.0064704954, 0.026671965, -0.015063233, -0.013536825, 0.016696757, 0.008127451, 0.026966535, 0.029912233, -0.0031431946, 0.015156959, 0.012412104, -0.047907773, 0.022012403, -0.027006702, -0.0069491714, 0.010718327, 0.011976943, -0.008127451, -0.65212417, 0.00024289463, 0.0051214993, -0.013007938, 0.022373922, 0.0337952, -0.0026829292, -0.0110463705, -0.013034717, -0.0012167745, 0.010062239, -0.0023013272, 0.024409132, -0.009118277, -0.020191427, -0.01597372, 0.010115798, -0.030929837, -0.010932559, 0.010912475, -0.0009841312, 0.010571042, -0.008348378, -0.009104887, 0.02711382, 0.0036553445, -0.018263333, -0.030876279, 0.014594599, 0.037704945, -0.030126465, 0.014366977, 0.0055533117, 0.003487975, 0.044988856, 0.009881481, -0.012699978, 0.041132666, 0.01744657, 0.05417408, -0.004686339, 0.016121006, 0.0070495927, 0.015478308, -0.020593112, 0.0012376956, 0.027127208, -0.0051248465, 0.0005979267, 0.0063366, -0.008616169, 0.027877023, -0.00042679158, 0.008442105, 0.00069751136, 0.023806602, 0.029296314, -0.0047332025, 0.027877023, 0.0033005215, 0.014996285, -0.0061424514, 0.00451897, 0.015531867, -0.015317634, 0.044185482, 0.010196134, 0.007504837, 0.012405409, -0.030126465, 0.03821375, 0.0256008, -0.016710145, 0.0032804373, -0.013884953, 0.022775607, 0.030608488, -0.023431696, -0.008502358, 0.008683117, -0.0045490963, -0.0030143203, -0.024074392, 0.00874337, 0.009466405, -0.0072370465, -0.021383096, 0.001360712, 0.020298542, 0.0040168623, 0.008201093, 0.011106623, -0.03202778, 0.0046461704, -0.00088370964, -0.008957602, 0.0057575023, 0.00037407028, 0.017259117, -0.0482559, -0.0049507823, -0.024235068, -0.0014418861, 0.004425243, 0.023244241, 0.0107919695, -0.017058274, 0.0183035, 0.033339955, -0.009091497, 0.000118936776, 0.0031900578, -0.000044483608, -0.017058274, 0.001529755, -0.027984139, 0.02740839, -0.015344413, 0.015264076, -0.01719217, 0.010463926, -0.0067048124, 0.014942727, -0.00026653553, 0.02677908, -0.00036570182, -0.043194655, -0.022855945, -0.011294077, 0.005764197, 0.004910614, -0.0029724778, 0.0056637754, -0.01425986, -0.000008708432, 0.01866502, 0.031626094, 0.0050378144, 0.015451529, 0.009406152, -0.030742384, -0.0024318753, -0.029751558, -0.008348378, 0.0028519721, -0.008388547, -0.010611211, 0.0139519, -0.0006895613, -0.001230164, -0.0062462203, -0.013510046, 0.010617905, -0.010229609, 0.022213247, -0.00610563, -0.00568386, -0.0056503857, 0.02416812, -0.0076253433, 0.015183738, -0.005188447, -0.016080838, 0.013516741, 0.0062897364, -0.0068520973, 0.021396484, 0.007799407, -0.01721895, -0.025266062, 0.013791226, -0.017205559, -0.002068684, 0.032938268, 0.014661547, 0.023552202, -0.005827797, -0.008442105, -0.0074914475, 0.009111582, 0.016817262, -0.0050244248, -0.005871313, -0.008368462, 0.040329296, 0.008683117, 0.031518977, 0.026109602, -0.025815032, 0.011006202, -0.0034310697, 0.019575508, -0.013831395, -0.008676422, -0.008770149, -0.019990584, 0.008750064, 0.02851972, 0.0337952, 0.012666505, 0.021383096, -0.027448557, 0.0035448808, -0.016214734, 0.015197128, -0.027582452, -0.0138046155, -0.03899034, 0.008261346, 0.015478308, 0.017888425, 0.0153979715, 0.010658074, -0.011581952, 0.02530623, 0.017982153, -0.0059449556, 0.0054294583, 0.0022879376, -0.018758746, -0.0076119537, -0.027689569, 0.013463182, 0.011186961, -0.0063165156, 0.028412605, 0.011347636, 0.008709895, -0.003374164, -0.007919913, -0.025828423, 0.0033875536, -0.013831395, -0.0035716598, 0.010450536, -0.025172336, 0.003990083, -0.00093224674, 0.024047613, 0.008027029, -0.0029440252, 0.023458473, 0.016643198, -0.0326437, 0.019147042, 0.01925416, -0.0020151257, 0.0038628823, -0.026738912, 0.0008753412, -0.025105387, 0.0069491714, -0.02623011, 0.027033482, -0.0040737675, -0.021034967, 0.019468391, 0.0026042655, 0.03467891, 0.016107617, -0.0057139862, -0.011735932, 0.017687583, 0.011628816, 0.015090012, -0.006678033, -0.011715848, -0.01833028, 0.008040419, -0.01921399, -0.03267048, -0.005914829, 0.0014435598, -0.0030662047, 0.005479669, 0.01597372, -0.01454104, 0.023257632, 0.019722793, 0.0344379, 0.006929087, -0.043248214, 0.015853215, 0.012766927, -0.007417805, -0.018316891, -0.01163551, -0.017352844, -0.01978974, 0.015304244, -0.00005920687, 0.033580966, -0.0022343795, 0.0047800657, -0.007357552, 0.00033536615, 0.00887057, -0.025654359, 0.016388796, -0.011361024, 0.00019090556, 0.0060119033, -0.010075629, -0.0131485285, 0.01604067, -0.015531867, 0.0035616176, -0.017259117, 0.0035415334, 0.009265562, -0.0043348637, -0.005867966, -0.03283115, -0.004773371, -0.018410617, -0.0095400475, -0.006520706, -0.00414741, 0.031197628, 0.013690805, -0.008984381, -0.022320364, -0.012492441, -0.005724028, 0.09806499, 0.017272506, -0.00007704216, 0.00858939, 0.0030126465, -0.002835235, -0.023753043, -0.025587412, 0.016067449, 0.0024536331, 0.004719813, -0.02908208, 0.027743127, 0.0023414958, 0.0152908545, 0.00552988, -0.031974223, 0.0019582203, 0.010812053, -0.01952195, -0.00006171741, -0.02241409, 0.025252672, 0.013737668, 0.002356559, -0.03719614, 0.021637497, 0.033580966, 0.0044453274, -0.0074378895, -0.014715104, -0.01741979, -0.013489962, -0.003221858, 0.0038561875, -0.013121749, -0.012974464, 0.012619642, 0.053424265, -0.020459218, 0.011581952, 0.041962817, -0.00087032013, -0.0036988605, -0.0010025419, -0.020392269, 0.014902558, 0.021409875, 0.01771436, -0.006483885, 0.036633782, -0.00028808432, 0.011983639, 0.014326808, 0.024931323, 0.002629371, -0.01223804, -0.010972728, -0.011253908, 0.013831395, -0.01748674, -0.013777837, -0.0043449057, -0.009292341, -0.0015849868, -0.019455003, -0.031170849, 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0.02244087, -0.023458473, -0.0053859423, -0.01925416]
})
.with_limit(2)
.with_additional(["distance"])
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.nearVector(queryVector, {
limit: 2,
returnMetadata: ['distance']
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
console.log(JSON.stringify(object.metadata?.distance, null, 2));
}
let result;
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withNearVector({ vector: [-0.0125526935, -0.021168863, -0.01076519, -0.02589537, -0.0070362035, 0.019870078, -0.010001986, -0.019120263, 0.00090044655, -0.017393013, 0.021302758, 0.010055545, 0.02937665, -0.003816019, 0.007692291, 0.012385325, 0.032750815, 0.020847514, 0.020311933, -0.022159688, -0.0009924996, 0.009399457, 0.0022226637, -0.029510546, 0.014393755, -0.007223657, 0.018276723, -0.03639277, -0.010001986, -0.022842556, 0.010363504, -0.020927852, -0.006929087, -0.022521207, -0.007652122, -0.011126708, 0.0279038, -0.01721895, 0.016482525, 0.002281243, -0.00169294, 0.009191919, -0.019655844, -0.022869334, -0.012412104, 0.0031967526, -0.0033457114, -0.01483561, -0.03173321, 0.004746592, 0.010095714, 0.007973471, -0.032134898, -0.023739655, -0.008040419, 0.018290112, -0.013637247, -0.008488968, 0.024623364, -0.039365247, -0.0032586793, 0.0009606995, -0.029510546, 0.0063265576, -0.019602288, 0.003081268, 0.013463182, -0.006601043, 0.019910246, -0.01542475, 0.0367409, -0.01193008, 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-0.017339455, 0.016241511, 0.0069424766, 0.015652372, 0.014380367, 0.006791844, -0.0023967277, 0.037945956, -0.0285465, 0.02128937, 0.0049942983, -0.029831896, -0.023819992, -0.016281681, -0.0031850368, 0.0029691304, -0.0038227136, 0.023645928, -0.036473107, -0.02153038, -0.025279451, -0.010242999, 0.018156216, -0.025413347, 0.0036218707, 0.005111457, -0.014487483, -0.0059784297, -0.013690805, -0.171279, -0.0037222921, 0.01626829, -0.010417062, -0.0007322405, -0.001834367, 0.008776844, -0.012867348, -0.005884703, -0.0027147292, 0.022306973, 0.0042244, -0.049300287, -0.0157461, 0.016054058, 0.002781677, 0.00197161, 0.007980166, -0.014366977, -0.0071834885, 0.021048358, -0.024971493, 0.017955374, -0.007692291, 0.0043683373, 0.018557902, 0.01570593, 0.0027063608, 0.0011791164, -0.03698191, -0.014875779, 0.008455494, 0.016536081, 0.009486489, -0.001415107, 0.002960762, -0.008368462, -0.021878509, -0.022454258, 0.004686339, 0.012392019, 0.04394447, 0.016121006, -0.0068085813, 0.014085797, -0.0022946324, 0.008509053, -0.0063868104, 0.022333752, -0.026591627, 0.006497274, -0.01454104, 0.0080337245, -0.0059014396, 0.01602728, 0.02651129, -0.010738411, 0.014567819, -0.010303251, -0.031010175, -0.03821375, -0.0056403438, -0.00006835988, -0.0011732584, -0.021945456, -0.011146792, -0.023498643, 0.021409875, -0.026712133, -0.004190926, 0.002542339, 0.0062462203, -0.004522317, -0.02967122, 0.008334989, 0.00029415145, -0.018544514, 0.022240026, -0.024261847, -0.021811562, -0.020566333, 0.0390439, -0.025466906, 0.014059017, 0.013476572, -0.007451279, -0.0101760505, -0.021918677, 0.004093852, -0.003772503, 0.034304, -0.029483767, -0.025574021, -0.015893385, -0.003407638, 0.030233582, 0.007799407, 0.00002280406, 0.021838339, -0.01633524, -0.006875529, -0.010229609, 0.0053256894, 0.02011109, -0.010885696, 0.04016862, 0.028760733, 0.015183738, 0.013061496, -0.0073307734, 0.0077324593, 0.007739154, 0.015344413, 0.03783884, -0.012124228, 0.0145276515, -0.00086027797, 0.0006744981, 0.035375167, -0.0044620642, 0.030903058, 0.01567915, -0.0053189946, -0.014045628, -0.018852472, 0.0035683124, -0.09554776, -0.013791226, -0.015116791, 0.0013891648, -0.026377395, 0.019147042, -0.008254652, 0.040623866, -0.01656286, 0.01948178, 0.01310836, -0.006025293, 0.005971735, -0.0051348885, 0.019843299, 0.02007092, -0.027421778, 0.00007709446, 0.0038896615, 0.005737418, 0.010095714, -0.0044988855, 0.011294077, -0.001899641, -0.01567915, 0.007216962, -0.02095463, 0.024797428, -0.0064805374, 0.010691548, 0.01208406, -0.012867348, -0.0057775867, -0.023110347, -0.019588897, 0.0060821986, -0.019374665, -0.0061391043, 0.031331524, -0.018490955, 0.004043641, 0.0032017739, -0.003973346, -0.014982895, 0.008696507, -0.025989097, 0.007156709, 0.013523435, -0.0041139363, -0.03055493, -0.02793058, -0.011106623, -0.02851972, 0.023753043, 0.04689017, 0.0035850494, 0.009834617, 0.0096003, 0.016147785, 0.019856688, 0.0031582578, 0.004666255, -0.00829482, 0.0395527, -0.01077858, -0.020512775, -0.020512775, 0.012057281, 0.027006702, -0.021999015, -0.009633774, 0.02878751, -0.026645185, -0.005057899, -0.016964547, 0.003315585, -0.02910886, -0.008107367, 0.0138046155, -0.023538811, -0.0028804247, -0.03491992, 0.0076789013, -0.03781206, 0.014032238, 0.019642456, 0.021798171, -0.0074780583, -0.01602728, -0.011909996, -0.015183738, 0.0031063734, 0.0016686714, -0.036553446, -0.0018594724, 0.015906774, -0.009225393, 0.006755023, 0.0065776114, 0.0139117325, -0.0045524435, 0.0051583205, -0.049166393, 0.018477565, -0.010182745, 0.0031398472, 0.022614934, 0.0048905294, 0.027234325, -0.005191794, 0.026966535, -0.0012477378, -0.029483767, 0.010303251, -0.0072370465, 0.015505088, -0.015183738, -0.009948429, 0.00054185797, -0.016844042, 0.0015339392, -0.008495663, 0.01105976, 0.008375158, 0.013992069, 0.00698934, 0.0035448808, 0.01427325, 0.0080538085, 0.005382595, -0.021677665, 0.004900572, 0.008977687, -0.034812804, 0.005998514, 0.024984881, 0.0032687215, -0.02795736, 0.009124972, 0.0022778956, 0.0038126716, 0.012646421, 0.0019180516, -0.0128004, 0.013034717, -0.046033237, -0.00021506949, -0.005104762, -0.010309946, 0.0054093744, 0.01632185, -0.005737418, 0.016937768, 0.010945949, -0.018129438, 0.0039532618, -0.0047432445, -0.04051675, 0.03703547, 0.007551701, 0.0031264576, 0.00073935365, 0.012887432, 0.00020000625, 0.003869577, -0.012961075, -0.010443841, 0.038481537, 0.0037089025, -0.013643941, 0.03639277, -0.040329296, -0.022293584, 0.004087157, 0.011709153, 0.014902558, -0.006122367, 0.007852965, 0.003081268, 0.018571293, -0.0077190697, 0.020927852, 0.021195643, -0.00010554723, -0.029055303, 0.006269652, 0.029885454, 0.0060554193, -0.0075583956, 0.0008188541, 0.013041412, -0.006453758, -0.03467891, 0.005814408, 0.015090012, 0.010383588, 0.013818005, 0.018504344, -0.025761476, -0.011856438, 0.0052219206, 0.021423263, 0.00829482, -0.009881481, -0.01326234, -0.0038093242, -0.016951159, -0.005590133, -0.0067115068, -0.03483958, -0.010838833, 0.01717878, 0.038053073, -0.015612204, -0.004231095, 0.008027029, -0.008040419, 0.025667747, -0.005677165, -0.016455745, -0.028010918, 0.024342183, 0.010095714, 0.014219692, 0.016910989, -0.00083266204, 0.010182745, 0.021516992, 0.011950164, -0.03055493, -0.012104144, 0.004468759, -0.006969256, -0.014393755, -0.021342928, 0.0085626105, -0.015665762, 0.0021841687, -0.004234442, 0.030715605, -0.017138612, 0.042712633, 0.0062462203, 0.020057531, 0.008073892, -0.0326437, 0.01250583, 0.024342183, 0.04747931, -0.027020091, 0.0019414834, 0.015170349, -0.016362019, 0.02825193, -0.009566827, -0.039954387, -0.00697595, -0.023927107, -0.0285465, -0.000100316945, -0.024850987, 0.022963062, 0.002122242, 0.027877023, 0.0012870695, -0.018182995, -0.0079266075, 0.016174564, -0.010068934, -0.015090012, -0.0054227635, 0.0051516257, -0.013235561, -0.0075583956, -0.0131485285, 0.039365247, 0.0065575275, -0.011474836, 0.0028268667, -0.004425243, -0.0020703576, -0.010631295, -0.011702458, -0.0038394507, 0.0059784297, 0.032268792, 0.02244087, -0.023458473, -0.0053859423, -0.01925416] })
.withLimit(2)
.withFields('question answer _additional { distance }')
.do();
console.log(JSON.stringify(result, null, 2));
// Edit the vector variable to add a vector of length 384
// vector := ADD_A_VECTOR_HERE
response, err := client.GraphQL().Get().
WithClassName("JeopardyQuestion").
WithFields(
graphql.Field{Name: "question"},
graphql.Field{Name: "answer"},
graphql.Field{
Name: "_additional",
Fields: []graphql.Field{
{Name: "distance"},
},
},
).
WithNearVector(client.GraphQL().NearVectorArgBuilder().
WithVector(vector)).
WithLimit(2).
Do(ctx)
To run the example, paste the sample test vector into the code sample.
Sample test vector
vector := []float32{0.326901312, 0.172652353, 0.574298978, -0.877372618, 0.208563102, 0.534870921, -0.905765693, -0.240794293, 0.2483627, 0.071935073, -0.470612466, 0.899590301, 0.821722525, 0.771190126, -0.729547086, -0.891606557, 0.304722712, -0.299226525, 0.400798778, -0.438221959, 0.84784485, 0.229913025, 0.072704543, 0.754321192, -0.019145501, -0.894141594, -0.994515521, -0.593096071, -0.42883483, 0.24194537, 0.620309746, 0.632115028, 0.588728611, 0.097637792, 0.778057433, 0.218009849, -0.967106101, 0.53489523, -0.41595204, 0.242416186, -0.947618483, -0.521548494, 0.22066765, 0.656955091, -0.937464798, 0.513341425, 0.578846678, 0.249978376, -0.085722009, -0.03557413, 0.943261393, 0.085512458, -0.125636201, 0.554060472, 0.485368427, -0.645984772, 0.756222985, -0.099291789, -0.590909311, 0.233526122, 0.085346719, -0.879696717, -0.5351979, -0.959582549, 0.160636781, -0.505745761, 0.597447967, 0.637738272, -0.7560195, -0.203242247, -0.14202656, 0.0531654, -0.256164061, -0.788468035, 0.687289393, -0.361320829, -0.454431255, -0.056878361, -0.24120844, -0.559818319, -0.260802008, -0.391211829, 0.941519464, 0.427640945, -0.747279873, 0.156631127, 0.283531662, -0.567472453, -0.056855298, 0.376830341, 0.24340912, 0.203539024, -0.472871161, 0.148073935, -0.205732037, -0.113967997, 0.744806131, -0.716108348, -0.121028453, -0.260367162, 0.799248419, 0.693572742, -0.791924921, -0.23802225, 0.61424365, -0.227275991, 0.288018577, 0.43869821, -0.054773369, 0.235872433, 0.150168526, -0.148419033, -0.42652761, 0.708727207, 0.084139137, -0.72887396, -0.218030612, 0.107339953, -0.518407575, 0.835435492, 0.035034357, -0.941809022, 0.787348994, 0.563871276, 0.766441516, -0.027821565, 0.245867777, 0.667148957, 0.738303557, -0.891110299, -0.275965165, -0.768567633, -0.475590831, 0.814911332, -0.297372689, 0.278844884, 0.95130689, 0.637530377, 0.618917313, 0.175740276, -0.249863627, -0.293828547, 0.320150997, -0.197713784, -0.633765065, -0.810942827, 0.591293734, 0.388968601, 0.523304585, -0.171063703, 0.602972529, -0.450091234, 0.345062519, -0.716491932, 0.435084962, -0.991825804, 0.689999161, -0.137097366, -0.537270475, -0.14424947, -0.62181862, 0.44289108, 0.072616733, 0.114381466, -0.972054206, 0.597329412, 0.562940173, 0.549476569, -0.706469709, 0.978081921, 0.180978079, 0.162027999, 0.788607827, -0.267257907, 0.985984986, -0.563312619, -0.640888755, 0.462486684, 0.369103705, 0.650806096, -0.167334677, 0.607351556, 0.822088516, 0.796317805, -0.503272355, -0.251183198, -0.171193987, 0.022293507, 0.428948271, 0.130966005, -0.736595944, 0.304682365, 0.663292867, -0.198997943, 0.035542683, 0.118594925, -0.509118134, 0.169740121, 0.375104805, -0.379886464, -0.498633816, -0.704396843, 0.030748626, 0.944446866, 0.888355185, -0.652586251, -0.906279254, 0.926259459, -0.214344492, 0.322871291, -0.027617198, 0.20895568, 0.035279297, -0.969237773, 0.403299676, 0.428694059, 0.829344779, 0.691959507, 0.383265745, -0.782718812, 0.775060865, -0.779937498, 0.584385461, -0.459012881, 0.662861143, 0.678415842, -0.127245162, -0.634464935, 0.646265039, -0.192781253, 0.950300755, 0.211855294, -0.503585688, 0.836612346, 0.787168113, 0.865806113, 0.38960291, 0.8664508, -0.572625523, 0.56761092, -0.735380506, -0.095070433, -0.783564692, -0.208375599, 0.739675191, 0.073271624, 0.359469611, 0.227572188, 0.03146414, 0.22938932, -0.447168816, 0.997660781, 0.215311392, -0.431177845, 0.016089255, 0.502448595, -0.705274029, -0.289382977, -0.577193696, 0.966175471, -0.510154942, -0.95823724, 0.24204605, 0.365546465, -0.297344885, 0.236294365, 0.446028631, 0.117976098, 0.094099994, 0.260277337, -0.461409164, -0.375480325, -0.614179681, -0.392757615, 0.100161621, -0.814176208, -0.347271514, 0.592469245, -0.988247355, -0.158397473, 0.921216369, -0.962889718, -0.932866744, 0.414358528, 0.12841629, -0.676515076, 0.940077931, -0.434330301, -0.2041959, 0.139998128, -0.937367769, -0.65941309, -0.716202446, -0.707964147, -0.389402878, 0.758786102, 0.543653384, -0.151055143, 0.406115293, -0.667719031, -0.811399948, 0.221955265, -0.493543772, 0.342954834, 0.327300923, -0.19955993, 0.752914123, -0.170643372, -0.14423466, 0.034084297, -0.855779749, 0.741368546, 0.240861775, -0.341099861, -0.6478463, 0.548267419, 0.409670736, 0.995208265, 0.807107939, -0.585172449, 0.163887551, 0.97695251, 0.575339181, -0.569841278, 0.675494554, -0.471893576, -0.030140821, -0.05243822, 0.050174597, -0.412903213, -0.683965383, 0.334143696, 0.421115564, 0.175047935, 0.530304957, 0.304087579, -0.792279648, 0.685567038, -0.803590175, -0.742988649, 0.559471864, -0.720445164, -0.299579897, 0.856260016, -0.181088629, -0.397816074, 0.767682872, 0.738067303, 0.359374803, -0.385285243, -0.038967135, -0.147880482, 0.83122139, -0.446691037, -0.789851962, -0.110046918, -0.468262552, -0.756854501, -0.445852765, 0.978448405, -0.726514778, 0.667864341, 0.74283952, 0.484586568, 0.51334425, 0.819917424, -0.838528257, 0.436940199, -0.448078512, -0.337453429, -0.172542255, 0.17131926, 0.511645199, 0.684561713, 0.486342731, 0.873551862, -0.731099225, -0.753154103, -0.236784718, -0.65032768, -0.239905204, -0.803154248, -0.640516296, 0.855964698, -0.416501359, 0.630052995}
Float[] vector = { -0.0125526935f, -0.021168863f, -0.01076519f, -0.02589537f, -0.0070362035f,
0.019870078f, -0.010001986f, -0.019120263f, 0.00090044655f, -0.017393013f };
NearVectorArgument nearVector = NearVectorArgument.builder()
.vector(vector)
.build();
String query = GetBuilder.builder()
.className(className)
.withNearVectorFilter(nearVector)
.limit(2)
.build()
.buildQuery();
Result<GraphQLResponse> result = client.graphQL().raw().withQuery(query).run();
{
Get {
JeopardyQuestion (
limit: 2
nearVector: {
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0.016241511, 0.0069424766, 0.015652372, 0.014380367, 0.006791844, -0.0023967277, 0.037945956, -0.0285465, 0.02128937, 0.0049942983, -0.029831896, -0.023819992, -0.016281681, -0.0031850368, 0.0029691304, -0.0038227136, 0.023645928, -0.036473107, -0.02153038, -0.025279451, -0.010242999, 0.018156216, -0.025413347, 0.0036218707, 0.005111457, -0.014487483, -0.0059784297, -0.013690805, -0.171279, -0.0037222921, 0.01626829, -0.010417062, -0.0007322405, -0.001834367, 0.008776844, -0.012867348, -0.005884703, -0.0027147292, 0.022306973, 0.0042244, -0.049300287, -0.0157461, 0.016054058, 0.002781677, 0.00197161, 0.007980166, -0.014366977, -0.0071834885, 0.021048358, -0.024971493, 0.017955374, -0.007692291, 0.0043683373, 0.018557902, 0.01570593, 0.0027063608, 0.0011791164, -0.03698191, -0.014875779, 0.008455494, 0.016536081, 0.009486489, -0.001415107, 0.002960762, -0.008368462, -0.021878509, -0.022454258, 0.004686339, 0.012392019, 0.04394447, 0.016121006, -0.0068085813, 0.014085797, -0.0022946324, 0.008509053, -0.0063868104, 0.022333752, -0.026591627, 0.006497274, -0.01454104, 0.0080337245, -0.0059014396, 0.01602728, 0.02651129, -0.010738411, 0.014567819, -0.010303251, -0.031010175, -0.03821375, -0.0056403438, -0.00006835988, -0.0011732584, -0.021945456, -0.011146792, -0.023498643, 0.021409875, -0.026712133, -0.004190926, 0.002542339, 0.0062462203, -0.004522317, -0.02967122, 0.008334989, 0.00029415145, -0.018544514, 0.022240026, -0.024261847, -0.021811562, -0.020566333, 0.0390439, -0.025466906, 0.014059017, 0.013476572, -0.007451279, -0.0101760505, -0.021918677, 0.004093852, -0.003772503, 0.034304, -0.029483767, -0.025574021, -0.015893385, -0.003407638, 0.030233582, 0.007799407, 0.00002280406, 0.021838339, -0.01633524, -0.006875529, -0.010229609, 0.0053256894, 0.02011109, -0.010885696, 0.04016862, 0.028760733, 0.015183738, 0.013061496, -0.0073307734, 0.0077324593, 0.007739154, 0.015344413, 0.03783884, -0.012124228, 0.0145276515, -0.00086027797, 0.0006744981, 0.035375167, -0.0044620642, 0.030903058, 0.01567915, -0.0053189946, -0.014045628, -0.018852472, 0.0035683124, -0.09554776, -0.013791226, -0.015116791, 0.0013891648, -0.026377395, 0.019147042, -0.008254652, 0.040623866, -0.01656286, 0.01948178, 0.01310836, -0.006025293, 0.005971735, -0.0051348885, 0.019843299, 0.02007092, -0.027421778, 0.00007709446, 0.0038896615, 0.005737418, 0.010095714, -0.0044988855, 0.011294077, -0.001899641, -0.01567915, 0.007216962, -0.02095463, 0.024797428, -0.0064805374, 0.010691548, 0.01208406, -0.012867348, -0.0057775867, -0.023110347, -0.019588897, 0.0060821986, -0.019374665, -0.0061391043, 0.031331524, -0.018490955, 0.004043641, 0.0032017739, -0.003973346, -0.014982895, 0.008696507, -0.025989097, 0.007156709, 0.013523435, -0.0041139363, -0.03055493, -0.02793058, -0.011106623, -0.02851972, 0.023753043, 0.04689017, 0.0035850494, 0.009834617, 0.0096003, 0.016147785, 0.019856688, 0.0031582578, 0.004666255, -0.00829482, 0.0395527, -0.01077858, -0.020512775, -0.020512775, 0.012057281, 0.027006702, -0.021999015, -0.009633774, 0.02878751, -0.026645185, -0.005057899, -0.016964547, 0.003315585, -0.02910886, -0.008107367, 0.0138046155, -0.023538811, -0.0028804247, -0.03491992, 0.0076789013, -0.03781206, 0.014032238, 0.019642456, 0.021798171, -0.0074780583, -0.01602728, -0.011909996, -0.015183738, 0.0031063734, 0.0016686714, -0.036553446, -0.0018594724, 0.015906774, -0.009225393, 0.006755023, 0.0065776114, 0.0139117325, -0.0045524435, 0.0051583205, -0.049166393, 0.018477565, -0.010182745, 0.0031398472, 0.022614934, 0.0048905294, 0.027234325, -0.005191794, 0.026966535, -0.0012477378, -0.029483767, 0.010303251, -0.0072370465, 0.015505088, -0.015183738, -0.009948429, 0.00054185797, -0.016844042, 0.0015339392, -0.008495663, 0.01105976, 0.008375158, 0.013992069, 0.00698934, 0.0035448808, 0.01427325, 0.0080538085, 0.005382595, -0.021677665, 0.004900572, 0.008977687, -0.034812804, 0.005998514, 0.024984881, 0.0032687215, -0.02795736, 0.009124972, 0.0022778956, 0.0038126716, 0.012646421, 0.0019180516, -0.0128004, 0.013034717, -0.046033237, -0.00021506949, -0.005104762, -0.010309946, 0.0054093744, 0.01632185, -0.005737418, 0.016937768, 0.010945949, -0.018129438, 0.0039532618, -0.0047432445, -0.04051675, 0.03703547, 0.007551701, 0.0031264576, 0.00073935365, 0.012887432, 0.00020000625, 0.003869577, -0.012961075, -0.010443841, 0.038481537, 0.0037089025, -0.013643941, 0.03639277, -0.040329296, -0.022293584, 0.004087157, 0.011709153, 0.014902558, -0.006122367, 0.007852965, 0.003081268, 0.018571293, -0.0077190697, 0.020927852, 0.021195643, -0.00010554723, -0.029055303, 0.006269652, 0.029885454, 0.0060554193, -0.0075583956, 0.0008188541, 0.013041412, -0.006453758, -0.03467891, 0.005814408, 0.015090012, 0.010383588, 0.013818005, 0.018504344, -0.025761476, -0.011856438, 0.0052219206, 0.021423263, 0.00829482, -0.009881481, -0.01326234, -0.0038093242, -0.016951159, -0.005590133, -0.0067115068, -0.03483958, -0.010838833, 0.01717878, 0.038053073, -0.015612204, -0.004231095, 0.008027029, -0.008040419, 0.025667747, -0.005677165, -0.016455745, -0.028010918, 0.024342183, 0.010095714, 0.014219692, 0.016910989, -0.00083266204, 0.010182745, 0.021516992, 0.011950164, -0.03055493, -0.012104144, 0.004468759, -0.006969256, -0.014393755, -0.021342928, 0.0085626105, -0.015665762, 0.0021841687, -0.004234442, 0.030715605, -0.017138612, 0.042712633, 0.0062462203, 0.020057531, 0.008073892, -0.0326437, 0.01250583, 0.024342183, 0.04747931, -0.027020091, 0.0019414834, 0.015170349, -0.016362019, 0.02825193, -0.009566827, -0.039954387, -0.00697595, -0.023927107, -0.0285465, -0.000100316945, -0.024850987, 0.022963062, 0.002122242, 0.027877023, 0.0012870695, -0.018182995, -0.0079266075, 0.016174564, -0.010068934, -0.015090012, -0.0054227635, 0.0051516257, -0.013235561, -0.0075583956, -0.0131485285, 0.039365247, 0.0065575275, -0.011474836, 0.0028268667, -0.004425243, -0.0020703576, -0.010631295, -0.011702458, -0.0038394507, 0.0059784297, 0.032268792, 0.02244087, -0.023458473, -0.0053859423, -0.01925416]
}
) {
question
answer
_additional {
distance
}
}
}
}
Named vectors
v1.24
To search a collection that has named vectors, use the target vector
field to specify which named vector to search.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- Java
- GraphQL
from weaviate.classes.query import MetadataQuery
reviews = client.collections.get("WineReviewNV")
response = reviews.query.near_text(
query="a sweet German white wine",
limit=2,
target_vector="title_country", # Specify the target vector for named vector collections
return_metadata=MetadataQuery(distance=True)
)
for o in response.objects:
print(o.properties)
print(o.metadata.distance)
# Unfortunately, named vectors are not suppored in the v3 API / Python client.
# Please upgrade to the v4 API / Python client to use named vectors.
const myNVCollection = client.collections.get('WineReviewNV');
const result = await myNVCollection.query.nearText('a sweet German white wine', {
targetVector: 'title_country',
returnMetadata: ['distance'],
limit: 2,
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
console.log(JSON.stringify(object.metadata?.distance, null, 2));
}
result = await client.graphql
.get()
.withClassName('WineReviewNV')
.withNearText({
concepts: ['a sweet German white wine'],
targetVectors: ['title_country'],
})
.withLimit(2)
.withFields('title review_body country')
.do();
console.log(JSON.stringify(result, null, 2));
className := "WineReviewNV"
targetVector := "title_country"
limit := 2
response, err := client.GraphQL().Get().
WithClassName(className).
WithFields(
graphql.Field{Name: "_additional",
Fields: []graphql.Field{{Name: "distance"}}},
).
WithNearText((&graphql.NearTextArgumentBuilder{}).
WithConcepts([]string{"a sweet German white wine"}).
WithTargetVectors(targetVector),
).
WithLimit(limit).
Do(ctx)
String[] target_vector = { "title_country" };
NearTextArgument nearText = NearTextArgument.builder()
.concepts(new String[]{ "a sweet German white wine" })
.targetVectors(target_vector)
.build();
String query = GetBuilder.builder()
.className(className)
.withNearTextFilter(nearText)
.limit(1)
.build()
.buildQuery();
Result<GraphQLResponse> result = client.graphQL().raw().withQuery(query).run();
{
Get {
WineReviewNV(
limit: 2
nearText: {
targetVectors: ["title_country"]
concepts: ["a sweet German white wine"]
}
) {
title
review_body
country
}
}
}
Example response
The output is like this:
{
"WineReviewNV": [
{
"country": "Austria",
"review_body": "With notions of cherry and cinnamon on the nose and just slight fizz, this is a refreshing, fruit-driven sparkling ros\u00e9 that's full of strawberry and cherry notes\u2014it might just be the very definition of easy summer wine. It ends dry, yet refreshing.",
"title": "Gebeshuber 2013 Frizzante Ros\u00e9 Pinot Noir (\u00d6sterreichischer Perlwein)"
},
{
"country": "Austria",
"review_body": "Beautifully perfumed, with acidity, white fruits and a mineral context. The wine is layered with citrus and lime, hints of fresh pineapple acidity. Screw cap.",
"title": "Stadt Krems 2009 Steinterrassen Riesling (Kremstal)"
}
]
}
Set a similarity threshold
To set a similarity threshold between the search and target vectors, define a maximum distance
(or certainty
).
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- Java
- GraphQL
from weaviate.classes.query import MetadataQuery
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.near_text(
query="animals in movies",
distance=0.25, # max accepted distance
return_metadata=MetadataQuery(distance=True)
)
for o in response.objects:
print(o.properties)
print(o.metadata.distance)
max_distance = 0.18
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_near_text({
"concepts": ["animals in movies"],
"distance": max_distance
})
.with_additional(["distance"])
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const maxDistance = 0.18;
const result = await jeopardy.query.nearText('animals in movies', {
distance: maxDistance,
returnMetadata: ['distance'],
})
result.objects.forEach(item => {
console.log(JSON.stringify(item.properties, null, 2))
console.log(item.metadata?.distance)
})
let result;
const maxDistance = 0.18;
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withNearText({
concepts: ['animals in movies'],
distance: maxDistance,
})
.withFields('question answer _additional { distance }')
.do();
console.log(JSON.stringify(result, null, 2));
maxDistance := float32(0.18)
response, err := client.GraphQL().Get().
WithClassName("JeopardyQuestion").
WithFields(
graphql.Field{Name: "question"},
graphql.Field{Name: "answer"},
graphql.Field{
Name: "_additional",
Fields: []graphql.Field{
{Name: "distance"},
},
},
).
WithNearText(client.GraphQL().NearTextArgBuilder().
WithConcepts([]string{"animals in movies"}).
WithDistance(maxDistance)).
Do(ctx)
NearTextArgument nearText = NearTextArgument.builder()
.concepts(new String[]{ "animals in movies" })
.distance(0.25f)
.build();
String query = GetBuilder.builder()
.className(className)
.withNearTextFilter(nearText)
.build()
.buildQuery();
Result<GraphQLResponse> result = client.graphQL().raw().withQuery(query).run();
{
Get {
JeopardyQuestion(
nearText: {
concepts: ["animals in movies"]
distance: 0.18
}
) {
question
answer
_additional {
distance
}
}
}
}
Additional information
- The distance value depends on many factors, including the vectorization model you use. Experiment with your data to find a value that works for you.
certainty
is only available withcosine
distance.- To find the least similar objects, use the negative cosine distance with
nearVector
search.
limit
& offset
Use limit
to set a fixed maximum number of objects to return.
Optionally, use offset
to paginate the results.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- Java
- GraphQL
from weaviate.classes.query import MetadataQuery
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.near_text(
query="animals in movies",
limit=2, # return 2 objects
offset=1, # With an offset of 1
return_metadata=MetadataQuery(distance=True)
)
for o in response.objects:
print(o.properties)
print(o.metadata.distance)
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_near_text({
"concepts": ["animals in movies"]
}).with_additional(["distance"])
.with_limit(2)
.with_offset(1)
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.nearText('animals in movies', {
limit: 2,
offset: 1,
returnMetadata: ['distance']
})
console.log(JSON.stringify(result.objects, null, 2));
let result;
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withNearText({ concepts: ['animals in movies'] })
.withLimit(2)
.withOffset(1)
.withFields('question answer _additional { distance }')
.do();
console.log(JSON.stringify(result, null, 2));
response, err := client.GraphQL().Get().
WithClassName("JeopardyQuestion").
WithFields(
graphql.Field{Name: "question"},
graphql.Field{Name: "answer"},
graphql.Field{
Name: "_additional",
Fields: []graphql.Field{
{Name: "distance"},
},
},
).
WithNearText(client.GraphQL().NearTextArgBuilder().
WithConcepts([]string{"animals in movies"})).
WithLimit(2).
WithOffset(1).
Do(ctx)
NearTextArgument nearText = NearTextArgument.builder()
.concepts(new String[]{ "animals in movies" })
.build();
Fields fields = Fields.builder()
.fields(new Field[]{
Field.builder().name("question").build(),
Field.builder().name("answer").build(),
Field.builder().name("_additional").fields(new Field[]{
Field.builder().name("distance").build()
}).build()
})
.build();
String query = GetBuilder.builder()
.className(className)
.withNearTextFilter(nearText)
.fields(fields)
.limit(3)
.offset(1)
.build()
.buildQuery();
Result<GraphQLResponse> result = client.graphQL().raw().withQuery(query).run();
{
Get {
JeopardyQuestion(
nearText: {
concepts: ["animals in movies"]
}
limit: 2
offset: 1
) {
question
answer
_additional {
distance
}
}
}
}
Limit result groups
To limit results to groups of similar distances to the query, use the autocut
filter to set the number of groups to return.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- Java
- GraphQL
from weaviate.classes.query import MetadataQuery
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.near_text(
query="animals in movies",
auto_limit=1, # number of close groups
return_metadata=MetadataQuery(distance=True)
)
for o in response.objects:
print(o.properties)
print(o.metadata.distance)
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_near_text({
"concepts": ["animals in movies"],
"distance": max_distance
})
.with_autocut(1)
.with_additional(["distance"])
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.nearText('animals in movies', {
autoLimit: 1,
returnMetadata: ['distance'],
})
result.objects.forEach(item => {
console.log(JSON.stringify(item.properties, null, 2))
console.log(item.metadata?.distance)
})
let result;
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withNearText({
concepts: ['animals in movies'],
})
.withAutocut(1)
.withFields('question answer _additional { distance }')
.do();
console.log(JSON.stringify(result, null, 2));
response, err := client.GraphQL().Get().
WithClassName("JeopardyQuestion").
WithFields(
graphql.Field{Name: "question"},
graphql.Field{Name: "answer"},
graphql.Field{
Name: "_additional",
Fields: []graphql.Field{
{Name: "distance"},
},
},
).
WithNearText(client.GraphQL().NearTextArgBuilder().
WithConcepts([]string{"animals in movies"})).
WithAutocut(1).
Do(ctx)
NearTextArgument nearText = NearTextArgument.builder()
.concepts(new String[]{ "animals in movies" })
.build();
Fields fields = Fields.builder()
.fields(new Field[]{
Field.builder().name("question").build(),
Field.builder().name("answer").build(),
Field.builder().name("_additional").fields(new Field[]{
Field.builder().name("distance").build()
}).build()
})
.build();
String query = GetBuilder.builder()
.className(className)
.fields(fields)
.withNearTextFilter(nearText)
.autocut(1)
.build()
.buildQuery();
Result<GraphQLResponse> result = client.graphQL().raw().withQuery(query).run();
{
Get {
JeopardyQuestion(
nearText: {
concepts: ["animals in movies"]
}
autocut: 1
) {
question
answer
_additional {
distance
}
}
}
}
Example response
The output is like this:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"answer": "meerkats",
"question": "Group of mammals seen <a href=\"http://www.j-archive.com/media/1998-06-01_J_28.jpg\" target=\"_blank\">here</a>: [like Timon in <i>The Lion King</i>]",
"_additional": { "distance": 0.17602634 }
},
{
"answer": "dogs",
"question": "Scooby-Doo, Goofy & Pluto are cartoon versions",
"_additional": { "distance": 0.17842108 }
}
]
}
}
}
Group results
Use a property or a cross-reference to group results. To group returned objects, the query must include a Near
search operator, such as Near Text
or Near Object
.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- Java
- GraphQL
from weaviate.classes.query import MetadataQuery, GroupBy
jeopardy = client.collections.get("JeopardyQuestion")
group_by = GroupBy(
prop="round", # group by this property
objects_per_group=2, # maximum objects per group
number_of_groups=2, # maximum number of groups
)
response = jeopardy.query.near_text(
query="animals in movies", # find object based on this query
limit=10, # maximum total objects
return_metadata=MetadataQuery(distance=True),
group_by=group_by
)
for o in response.objects:
print(o.uuid)
print(o.belongs_to_group)
print(o.metadata.distance)
for grp, grp_items in response.groups.items():
print("=" * 10 + grp_items.name + "=" * 10)
print(grp_items.number_of_objects)
for o in grp_items.objects:
print(o.properties)
print(o.metadata)
max_groups = 2
max_objects_per_group = 2
response = (
client.query
.get("JeopardyQuestion")
.with_near_text({
"concepts": ["animals in movies"]})
.with_limit(10)
.with_group_by(
["round"],
groups=max_groups,
objects_per_group=max_objects_per_group
)
.with_additional([
"""
group {
id
groupedBy {
path
value
}
count
minDistance
maxDistance
hits {
question
answer
}
}
"""
])
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.nearText('animals in movies', {
limit: 10,
returnMetadata: ['distance'],
groupBy