Similarity / Vector search
Overview
This page shows you how to perform similarity-based searches with the nearXXX
operators.
These operators work by searching for objects with the most similar vector representation to the query. Note that due to differences in configuration and data, we have a separate page for image searches.
Similarity-based operators
These operators are available:
near<Media>
- Finds objects closest to an input medium:- E.g.: Use it to find text objects most similar to
cute animals
, or images most similar to a particular image.
- E.g.: Use it to find text objects most similar to
nearObject
- Finds objects closest to another Weaviate object:- E.g.: Use it to find Weaviate objects most similar to object
56b9449e-65db-5df4-887b-0a4773f52aa7
.
- E.g.: Use it to find Weaviate objects most similar to object
nearVector
- Find objects closest to an input vector.- E.g.: Use it to find Weaviate objects most similar to vector
[-0.368, 0.1397, ... , 0.0971]
.
- E.g.: Use it to find Weaviate objects most similar to vector
An input medium
You can use these operators to find objects most similar to a raw (un-vectorized) input, such as text or image. For text objects, you can provide an input text to nearText
, and for image objects, you can provide an input image to nearImage
. (Or either if you are using CLIP.)
The example below searches the JeopardyQuestion
class for the top 2 objects best matching "animals in movies"
, using nearText
:
- Python
- JavaScript/TypeScript
- GraphQL
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))
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));
{
Get {
JeopardyQuestion(
limit: 2
nearText: {
concepts: ["animals in movies"]
}
) {
question
answer
_additional {
distance
}
}
}
}
Example response
It should produce a response like the one below:
{
"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 }
}
]
}
}
}
An object
You can use the nearObject
operator to find objects most similar to an existing Weaviate object. To do so, specify the object ID (e.g. 56b9449e-65db-5df4-887b-0a4773f52aa7
) as shown below.
See this section
The example below searches the JeopardyQuestion
class for the top 2 objects best matching the object with ID 56b9449e-65db-5df4-887b-0a4773f52aa7
, using nearObject
:
- Python
- JavaScript/TypeScript
- GraphQL
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))
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withNearObject({ id: '56b9449e-65db-5df4-887b-0a4773f52aa7' })
.withLimit(2)
.withFields('question answer _additional { distance }')
.do();
console.log(JSON.stringify(result, null, 2));
{
Get {
JeopardyQuestion (
limit: 2
nearObject: {
id: "56b9449e-65db-5df4-887b-0a4773f52aa7"
}
) {
question
answer
_additional {
distance
}
}
}
}
A vector
You can use the nearVector
operator to find objects most similar to an input vector (e.g. [-0.368, 0.1397, ... , 0.0971]
).
The example below searches the JeopardyQuestion
class for the top 2 objects best matching the object with the provided vector, using nearVector
:
- Python
- JavaScript/TypeScript
- GraphQL
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, 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-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, 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})
.with_limit(2)
.with_additional(["distance"])
.do()
)
print(json.dumps(response, indent=2))
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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.withLimit(2)
.withFields('question answer _additional { distance }')
.do();
console.log(JSON.stringify(result, null, 2));
{
Get {
JeopardyQuestion (
limit: 2
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0.02244087, -0.023458473, -0.0053859423, -0.01925416]
}
) {
question
answer
_additional {
distance
}
}
}
}
Limit the results
You can set a limit on:
- the number of results returned (with
limit
), or - how similar the results are to the query (with
distance
), or - the number of "jumps" in
distance
from the query (with theautocut
filter).
autocut
can be combined with limit
, to set the maximum number of results returned by autocut
.
Number of results
You can set the maximum number of results returned with limit
in the same way as shown in the search basics how-to guide.
Similarly, you can retrieve a maximum n
objects after the first m
results by using limit
with offset
as shown in the search basics how-to guide.
To limit the number of results returned by a near...
query, add the limit
operator. To start at a given offset, add the offset
operator. For example if we want to obtain the animals in movies #2 and #3 from the nearText
example above, we'll need to use offset: 1, limit: 2
. The example below searches the JeopardyQuestion
class for objects best matching "animals in movies"
, skips 1 object (offset
) and returns the next 2 objects:
- Python
- JavaScript/TypeScript
- GraphQL
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))
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));
{
Get {
JeopardyQuestion(
nearText: {
concepts: ["animals in movies"]
}
limit: 2
offset: 1
) {
question
answer
_additional {
distance
}
}
}
}
Distance threshold
You can set a threshold for similarity search by setting a maximum distance
. The distance indicates how dissimilar two objects are.
Multiple distance metrics are available in Weaviate. You can set it in the schema as shown here.
The example below searches the JeopardyQuestion
class for objects best matching "animals in movies"
, returning those with a distance
less than 0.18
:
- Python
- JavaScript/TypeScript
- GraphQL
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 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));
{
Get {
JeopardyQuestion(
nearText: {
concepts: ["animals in movies"]
distance: 0.18
}
) {
question
answer
_additional {
distance
}
}
}
}
0.18
?The numerical value for distance
will depend on many factors, including the vectorization model and the distance metric used. As such, there are no hard and fast rules. In this case, we selected this value as our trial and error evaluation of this dataset indicated this value to produce relatively intuitive outputs.
certainty
possible for cosine
distance metric onlyIf the distance metric is set as cosine
the certainty
variable can be used, which normalizes the complement of distance to a value between 0 and 1.
Autocut
Another way to limit the results returned by a similarity search is to use the autocut
filter. Autocut takes a positive integer parameter N
, looks at the distance between each result and the query, and stops returning results after the N
th "jump" in distance. For example, if the distances for six objects returned by nearText
were [0.1899, 0.1901, 0.191, 0.21, 0.215, 0.23]
then autocut: 1
would return the first three objects, autocut: 2
would return all but the last object, and autocut: 3
would return all objects.
Autocut can be used as follows:
- Python
- JavaScript/TypeScript
- GraphQL
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))
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));
{
Get {
JeopardyQuestion(
nearText: {
concepts: ["animals in movies"]
}
autocut: 1
) {
question
answer
_additional {
distance
}
}
}
}
Example response
It should produce a response like the one below:
{
"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 by a property or cross-reference
v1.19
You can group search results by any arbitrary property or cross-reference.
The example below searches the JeopardyQuestion
class for objects best matching "animals in movies"
, fetching the 10 closest results. Then those results are grouped by round
, returning a maximum of two groups, each group with a maximum of two results (hits
):
To group results by a cross-reference, try replacing the path
value from round
to hasCategory
in the example below.
- Python
- JavaScript/TypeScript
- GraphQL
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))
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withNearText({
concepts: ['animals in movies'],
})
.withLimit(10)
.withGroupBy({
path: ['round'],
groups: 2,
objectsPerGroup: 2,
})
.withFields(`
_additional {
group {
id
groupedBy {
path
value
}
count
minDistance
maxDistance
hits {
question
answer
}
}
}
`)
.do();
// For each group, display the properties under _additional.group
for (const group of result.data.Get.JeopardyQuestion)
console.log(group['_additional']['group']);
{
Get {
JeopardyQuestion(
nearText: {
concepts: ["animals in movies"],
}
limit: 10
groupBy: {
path: ["round"],
groups: 2,
objectsPerGroup: 2
}
) {
_additional {
group {
id
groupedBy {
path
value
}
count
minDistance
maxDistance
hits {
question
answer
}
}
}
}
}
}
Example response
It should produce a response like the one below:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"_additional": {
"group": {
"count": 2,
"groupedBy": {
"path": [
"round"
],
"value": "Jeopardy!"
},
"hits": [
{
"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>]"
},
{
"answer": "dogs",
"question": "Scooby-Doo, Goofy & Pluto are cartoon versions"
}
],
"id": 0,
"maxDistance": 0.17842054,
"minDistance": 0.17602539
}
}
},
{
"_additional": {
"group": {
"count": 1,
"groupedBy": {
"path": [
"round"
],
"value": "Double Jeopardy!"
},
"hits": [
{
"answer": "fox",
"question": "In titles, animal associated with both Volpone and Reynard"
}
],
"id": 1,
"maxDistance": 0.18770188,
"minDistance": 0.18770188
}
}
}
]
}
}
}
Add a conditional (where
) filter
You can add a conditional filter to your search results using the where
argument.
The example below searches the JeopardyQuestion
class for the top 2 objects best matching "animals in movies"
, as long as their round
property is exactly "Double Jeopardy!"
:
- Python
- JavaScript/TypeScript
- GraphQL
response = (
client.query
.get("JeopardyQuestion", ["question", "answer", "round"])
.with_near_text({
"concepts": ["animals in movies"]
})
.with_limit(2)
.with_additional(["distance"])
.with_where({
"path": ["round"],
"operator": "Equal",
"valueText": "Double Jeopardy!"
})
.do()
)
print(json.dumps(response, indent=2))
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withNearText({ concepts: ['animals in movies'] })
.withLimit(2)
.withWhere({
path: ['round'],
operator: 'Equal',
valueText: 'Double Jeopardy!',
})
.withFields('question answer round _additional { distance }')
.do();
console.log(JSON.stringify(result, null, 2));
{
Get {
JeopardyQuestion(
limit: 2
nearText: {
concepts: ["animals in movies"]
}
where: {
path: ["round"]
operator: Equal
valueText: "Double Jeopardy!"
}
) {
question
answer
_additional {
distance
}
}
}
}
Example response
It should produce a response like the one below:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"_additional": {
"distance": 0.18759078
},
"answer": "fox",
"question": "In titles, animal associated with both Volpone and Reynard",
"round": "Double Jeopardy!"
},
{
"_additional": {
"distance": 0.19532347
},
"answer": "Swan",
"question": "In a Tchaikovsky ballet, Prince Siegfried goes hunting for these animals & falls in love with 1 of them",
"round": "Double Jeopardy!"
}
]
}
}
}
Find least similar results
Sometimes you may want to find objects that are the least similar to a given input. This might be possible for some distance metrics:
- For cosine distances, perform a similarity search for a negative of a vector to find least.
- For Euclidean or dot distances, the definition of "least similar" vector is not as clear-cut.
Accordingly, we generally recommend using cosine distance for this use case, and searching for a negative of your input vector with nearVector
.
Further discussions
Here, the concept of least similar
relates to finding vectors that are opposite to each other in the embedding space.
This may not necessarily mean that these least similar
results have the opposite meaning in a semantic sense, such as antonyms in words.
Take the words rain and drought for example. While these are opposite concepts, both of them are unrelated to astrophysics. As such, in many models the distance between embeddings for 'rain' and 'astrophysics' will be likely greater than the distance between embeddings for 'rain' and 'drought'. Accordingly, you should consider the context of your use case when interpreting the results.
More Resources
For additional information, try these sources.