Hybrid search
Hybrid
search combines the results of a vector search and a keyword (BM25F) search by fusing the two result sets.
The fusion method and the relative weights are configurable.
Basic hybrid search
Combine the results of a vector search and a keyword search. The search uses a single query string.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- GraphQL
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(query="food", limit=3)
for o in response.objects:
print(o.properties)
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_hybrid(
query="food"
)
.with_limit(3)
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.hybrid('food', {
limit: 2,
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
}
let result;
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withHybrid({
query: 'food',
})
.withLimit(3)
.withFields('question answer')
.do();
console.log(JSON.stringify(result, null, 2));
ctx := context.Background()
className := "JeopardyQuestion"
query := "food"
limit := 3
q := client.GraphQL().Get().
WithClassName(className).
WithFields(graphql.Field{Name: "question"}, graphql.Field{Name: "answer"}).
WithHybrid(client.GraphQL().HybridArgumentBuilder().WithQuery(query)).
WithLimit(limit)
result, err := q.Do(ctx)
{
Get {
JeopardyQuestion(
limit: 3
hybrid: {
query: "food"
}
) {
question
answer
}
}
}
Example response
The output is like this:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"answer": "a closer grocer",
"question": "A nearer food merchant"
},
{
"answer": "Famine",
"question": "From the Latin for \"hunger\", it's a period when food is extremely scarce"
},
{
"answer": "Tofu",
"question": "A popular health food, this soybean curd is used to make a variety of dishes & an ice cream substitute"
}
]
}
}
}
Named vectors
v1.24
A hybrid search on a collection that has named vectors must specify a target
vector. Weaviate uses the query vector to search the target vector space.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- GraphQL
reviews = client.collections.get("WineReviewNV")
response = reviews.query.hybrid(
query="A French Riesling",
target_vector="title_country",
limit=3
)
for o in response.objects:
print(o.properties)
# Named vectors are not supported 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.hybrid('a sweet German white wine', {
targetVector: 'title_country',
limit: 2,
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
}
result = await client.graphql
.get()
.withClassName('WineReviewNV')
.withHybrid({
query: 'a sweet German white wine',
targetVectors: ['title_country'],
})
.withLimit(2)
.withFields('title review_body country')
.do();
console.log(JSON.stringify(result, null, 2));
{
Get {
WineReviewNV(
limit: 2
hybrid: {
targetVectors: ["title_country"]
query: "A French Riesling"
}
) {
title
review_body
country
}
}
}
Example response
The output is like this:
Explain the search results
To see the object rankings, set the explain score
field in your query. The search rankings are part of the object metadata. Weaviate uses the score to order the search results.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- GraphQL
from weaviate.classes.query import MetadataQuery
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
query="food",
alpha=0.5,
return_metadata=MetadataQuery(score=True, explain_score=True),
limit=3,
)
for o in response.objects:
print(o.properties)
print(o.metadata.score, o.metadata.explain_score)
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_hybrid(
query="food",
)
.with_additional(["score", "explainScore"])
.with_limit(3)
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.hybrid('food', {
limit: 2,
returnProperties: ['question', 'answer'],
returnMetadata: ['score','explainScore']
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
console.log(object.metadata?.score);
console.log(object.metadata?.explainScore);
}
let result;
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withHybrid({
query: 'food',
})
.withFields('question answer _additional { score explainScore }')
.withLimit(3)
.do();
console.log(JSON.stringify(result, null, 2));
ctx := context.Background()
className := "JeopardyQuestion"
query := "food"
limit := 3
result, err := client.GraphQL().Get().
WithClassName(className).
WithFields(
graphql.Field{Name: "question"},
graphql.Field{Name: "answer"},
graphql.Field{Name: "_additional", Fields: []graphql.Field{{Name: "score"}, {Name: "explainScore"}}},
).
WithHybrid(client.GraphQL().HybridArgumentBuilder().WithQuery(query)).
WithLimit(limit).
Do(ctx)
{
Get {
JeopardyQuestion(
limit: 3
hybrid: {
query: "food"
}
) {
question
answer
_additional {
score
explainScore
}
}
}
}
Example response
The output is like this:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"_additional": {
"explainScore": "(bm25)\n(hybrid) Document df958a90-c3ad-5fde-9122-cd777c22da6c contributed 0.003968253968253968 to the score\n(hybrid) Document df958a90-c3ad-5fde-9122-cd777c22da6c contributed 0.012295081967213115 to the score",
"score": "0.016263336"
},
"answer": "a closer grocer",
"question": "A nearer food merchant"
},
{
"_additional": {
"explainScore": "(vector) [0.0223698 -0.02752683 -0.0061537363 0.0023812135 -0.00036100898 -0.0078375945 -0.018505432 -0.037500713 -0.0042215516 -0.012620432]... \n(hybrid) Document ec776112-e651-519d-afd1-b48e6237bbcb contributed 0.012096774193548387 to the score",
"score": "0.012096774"
},
"answer": "Famine",
"question": "From the Latin for \"hunger\", it's a period when food is extremely scarce"
},
{
"_additional": {
"explainScore": "(vector) [0.0223698 -0.02752683 -0.0061537363 0.0023812135 -0.00036100898 -0.0078375945 -0.018505432 -0.037500713 -0.0042215516 -0.012620432]... \n(hybrid) Document 98807640-cd16-507d-86a1-801902d784de contributed 0.011904761904761904 to the score",
"score": "0.011904762"
},
"answer": "Tofu",
"question": "A popular health food, this soybean curd is used to make a variety of dishes & an ice cream substitute"
}
]
}
}
}
Balance keyword and vector search
Hybrid search results can favor the keyword component or the vector component. To change the relative weights of the keyword and vector components, set the alpha
value in your query.
- An
alpha
of1
is a pure vector search. - An
alpha
of0
is a pure keyword search.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- GraphQL
client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
query="food",
alpha=0.25,
limit=3,
)
for o in response.objects:
print(o.properties)
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_hybrid(
query="food",
alpha=0.25
)
.with_limit(3)
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.hybrid('food', {
alpha: 0.25,
limit: 3
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
}
let result;
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withHybrid({
query: 'food',
alpha: 0.25,
})
.withLimit(3)
.withFields('question answer')
.do();
console.log(JSON.stringify(result, null, 2));
ctx := context.Background()
className := "JeopardyQuestion"
query := "food"
limit := 3
alpha := float32(0.25)
result, err := client.GraphQL().Get().
WithClassName(className).
WithFields(graphql.Field{Name: "question"}, graphql.Field{Name: "answer"}).
WithHybrid(client.GraphQL().HybridArgumentBuilder().
WithQuery(query).
WithAlpha(alpha),
).
WithLimit(limit).
Do(ctx)
{
Get {
JeopardyQuestion(
limit: 3
hybrid: {
query: "food"
alpha: 0.25
}
) {
question
answer
}
}
}
Example response
The output is like this:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"answer": "a closer grocer",
"question": "A nearer food merchant"
},
{
"answer": "food stores (supermarkets)",
"question": "This type of retail store sells more shampoo & makeup than any other"
},
{
"answer": "cake",
"question": "Devil's food & angel food are types of this dessert"
}
]
}
}
}
Change the fusion method
Relative Score Fusion
is the default fusion method starting in v1.24
.
- To use the keyword and vector search relative scores instead of the search rankings, use
Relative Score Fusion
. - To use
autocut
with thehybrid
operator, useRelative Score Fusion
.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- GraphQL
from weaviate.classes.query import HybridFusion
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
query="food",
fusion_type=HybridFusion.RELATIVE_SCORE,
limit=3,
)
for o in response.objects:
print(o.properties)
from weaviate.gql.get import HybridFusion
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_hybrid(
query="food",
fusion_type=HybridFusion.RELATIVE_SCORE
)
.with_limit(3)
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.hybrid('food', {
limit: 2,
fusionType: 'RelativeScore', // can also be 'Ranked'
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
}
import { FusionType } from 'weaviate-ts-client';
let result;
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withHybrid({
query: 'food',
alpha: 0.25,
fusionType: FusionType.rankedFusion
})
.withLimit(3)
.withFields('question answer')
.do();
console.log(JSON.stringify(result, null, 2));
ctx := context.Background()
className := "JeopardyQuestion"
query := "food"
limit := 3
fusionType := "relativeScoreFusion"
result, err := client.GraphQL().Get().
WithClassName(className).
WithFields(graphql.Field{Name: "question"}, graphql.Field{Name: "answer"}).
WithHybrid(client.GraphQL().HybridArgumentBuilder().
WithQuery(query).
WithFusionType(graphql.FusionType(fusionType)),
).
WithLimit(limit).
Do(ctx)
{
Get {
JeopardyQuestion(
limit: 3
hybrid: {
query: "food"
fusionType: relativeScoreFusion
}
) {
question
answer
}
}
}
Example response
The output is like this:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"answer": "a closer grocer",
"question": "A nearer food merchant"
},
{
"answer": "food stores (supermarkets)",
"question": "This type of retail store sells more shampoo & makeup than any other"
},
{
"answer": "cake",
"question": "Devil's food & angel food are types of this dessert"
}
]
}
}
}
Additional information
For a discussion of fusion methods, see this blog post and this reference page
Specify keyword search properties
v1.19.0
The keyword search portion of hybrid search can be directed to only search a subset of object properties. This does not affect the vector search portion.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- GraphQL
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
query="food",
query_properties=["question"],
alpha=0.25,
limit=3,
)
for o in response.objects:
print(o.properties)
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_hybrid(
query="food",
properties=["question"],
alpha=0.25
)
.with_limit(3)
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.hybrid('food', {
queryProperties: ['question'],
alpha: 0.25,
limit: 3
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
}
let result;
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withHybrid({
query: 'food',
properties: ['question'],
alpha: 0.25,
})
.withLimit(3)
.withFields('question answer')
.do();
console.log(JSON.stringify(result, null, 2));
ctx := context.Background()
className := "JeopardyQuestion"
query := "food"
limit := 3
alpha := float32(0.25)
properties := []string{"question"}
result, err := client.GraphQL().Get().
WithClassName(className).
WithFields(graphql.Field{Name: "question"}, graphql.Field{Name: "answer"}).
WithHybrid(client.GraphQL().HybridArgumentBuilder().
WithQuery(query).
WithAlpha(alpha).
WithProperties(properties),
).
WithLimit(limit).
Do(ctx)
{
Get {
JeopardyQuestion(
limit: 3
hybrid: {
query: "food"
properties: ["question"]
alpha: 0.25
}
) {
question
answer
}
}
}
Example response
The output is like this:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"answer": "a closer grocer",
"question": "A nearer food merchant"
},
{
"answer": "cake",
"question": "Devil's food & angel food are types of this dessert"
},
{
"answer": "honey",
"question": "The primary source of this food is the Apis mellifera"
}
]
}
}
}
Set weights on property values
Specify the relative value of an object's properties
in the keyword search. Higher values increase the property's contribution to the search score.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- GraphQL
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
query="food",
query_properties=["question^2", "answer"],
alpha=0.25,
limit=3,
)
for o in response.objects:
print(o.properties)
response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_hybrid(
query="food",
properties=["question^2", "answer"],
alpha=0.25
)
.with_limit(3)
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.hybrid('food', {
queryProperties: ['question^2', 'answer'],
alpha: 0.25,
limit: 3
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
}
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withHybrid({
query: 'food',
properties: ['question^2', 'answer'],
alpha: 0.25,
})
.withLimit(3)
.withFields('question answer')
.do();
console.log(JSON.stringify(result, null, 2));
ctx := context.Background()
className := "JeopardyQuestion"
query := "food"
limit := 3
alpha := float32(0.25)
properties := []string{"question^2", "answer"}
result, err := client.GraphQL().Get().
WithClassName(className).
WithFields(graphql.Field{Name: "question"}, graphql.Field{Name: "answer"}).
WithHybrid(client.GraphQL().HybridArgumentBuilder().
WithQuery(query).
WithAlpha(alpha).
WithProperties(properties),
).
WithLimit(limit).
Do(ctx)
{
Get {
JeopardyQuestion(
limit: 3
hybrid: {
query: "food"
properties: ["question^2", "answer"]
alpha: 0.25
}
) {
question
answer
}
}
}
Example response
The output is like this:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"answer": "a closer grocer",
"question": "A nearer food merchant"
},
{
"answer": "cake",
"question": "Devil's food & angel food are types of this dessert"
},
{
"answer": "food stores (supermarkets)",
"question": "This type of retail store sells more shampoo & makeup than any other"
}
]
}
}
}
Specify a search vector
The vector component of hybrid search can use a query string or a query vector. To specify a query vector instead of a query string, provide a query vector (for the vector search) and a query string (for the keyword search) in your query.
- Python Client v4
- Python Client v3
- JS/TS Client v3
- JS/TS Client v2
- Go
- GraphQL
query_vector = [-0.02] * 1536 # Some vector that is compatible with object vectors
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
query="food",
vector=query_vector,
alpha=0.25,
limit=3,
)
for o in response.objects:
print(o.properties)
vector = [0.013085687533020973, -0.00777443777769804, 0.005439540836960077, -0.021052561700344086, -0.02164270170032978, -0.006985447835177183, -0.018974246457219124, -0.025260508060455322, -0.0013630924513563514, -0.03597281128168106, 0.027993107214570045, 0.01635710895061493, -0.02948128432035446, 0.009159981273114681, -0.0026780758053064346, 0.001033544773235917, 0.04051431640982628, 0.001919554895721376, 0.024952609091997147, -0.01960287243127823, 0.0001997531799133867, 0.031405650079250336, 0.021142365410923958, -0.007954045198857784, -0.0008338918560184538, -0.0040572043508291245, 7.381747127510607e-05, -0.019051222130656242, 0.004942412953823805, -0.01888444274663925, 0.028121398761868477, 0.004631306976079941, -0.031559597700834274, -0.003143130801618099, 0.01867917738854885, -0.024208521470427513, 0.0056351847015321255, -0.019333461299538612, -0.0012756941141560674, 0.01737060956656933, 0.020128866657614708, -0.007755194325000048, -0.0071329823695123196, -0.007761608809232712, -0.0074986121617257595, 0.00579554820433259, 0.002782312221825123, 0.01349621918052435, 0.003954571671783924, 0.003170392708852887, 0.017678506672382355, 0.008980373851954937, -0.027454284951090813, -0.004377932287752628, 0.013547535054385662, 0.028506271541118622, -0.011225467547774315, -0.003855146234855056, -8.654634439153597e-05, -0.021770991384983063, -0.004724318161606789, 0.037281379103660583, -0.0543954074382782, 0.01912819594144821, 0.009916898794472218, -0.007806510664522648, 0.0035921495873481035, 0.011757874861359596, -0.004980900324881077, -0.014381427317857742, 0.005952704697847366, 0.009839924052357674, -0.02256639674305916, 0.014561034739017487, 0.01888444274663925, 0.0006859562126919627, 0.0024984683841466904, 0.0033355674240738153, -0.007633317727595568, -0.015330781228840351, 0.02297692745923996, -0.02996879070997238, 0.017473241314291954, 0.01232235599309206, 0.019949259236454964, 0.009769363328814507, -0.038307707756757736, 0.0278134997934103, 0.012264625169336796, 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response = (
client.query
.get("JeopardyQuestion", ["question", "answer"])
.with_hybrid(
query="food",
vector=vector
)
.with_limit(3)
.do()
)
print(json.dumps(response, indent=2))
const jeopardy = client.collections.get('JeopardyQuestion');
let queryVector = Array(1536).fill(0.12345) // Query vector [0.12345, 0.12345, 0.12345...]
const result = await jeopardy.query.hybrid('food', {
vector: queryVector,
alpha: 0.25,
limit: 3
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
}
let result;
result = await client.graphql
.get()
.withClassName('JeopardyQuestion')
.withHybrid({
query: 'food',
alpha: 0.25,
vector: [0.013085687533020973, -0.00777443777769804, 0.005439540836960077, -0.021052561700344086, -0.02164270170032978, -0.006985447835177183, -0.018974246457219124, -0.025260508060455322, -0.0013630924513563514, -0.03597281128168106, 0.027993107214570045, 0.01635710895061493, -0.02948128432035446, 0.009159981273114681, -0.0026780758053064346, 0.001033544773235917, 0.04051431640982628, 0.001919554895721376, 0.024952609091997147, -0.01960287243127823, 0.0001997531799133867, 0.031405650079250336, 0.021142365410923958, -0.007954045198857784, -0.0008338918560184538, -0.0040572043508291245, 7.381747127510607e-05, -0.019051222130656242, 0.004942412953823805, -0.01888444274663925, 0.028121398761868477, 0.004631306976079941, -0.031559597700834274, -0.003143130801618099, 0.01867917738854885, -0.024208521470427513, 0.0056351847015321255, -0.019333461299538612, -0.0012756941141560674, 0.01737060956656933, 0.020128866657614708, -0.007755194325000048, -0.0071329823695123196, -0.007761608809232712, 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})
.withLimit(3)
.withFields('question answer')
.do();
console.log(JSON.stringify(result, null, 2));
ctx := context.Background()
className := "JeopardyQuestion"
query := "food"
limit := 3
//Create a vector 384 dimensions long
// Define the length of the slice
length := 384
// Initialize the slice with the specified length
values := make([]float32, length)
// Fill the slice with values
for i := 0; i < length; i++ {
values[i] = 0.1 * float32(i+1)
}
vector := values
result, err := client.GraphQL().Get().
WithClassName(className).
WithFields(graphql.Field{Name: "question"}, graphql.Field{Name: "answer"}).
WithHybrid(client.GraphQL().HybridArgumentBuilder().
WithQuery(query).
WithVector(vector),
).
WithLimit(limit).
Do(ctx)
{
Get {
JeopardyQuestion(
limit: 3
hybrid: {
query: "food"
vector: [0.013085687533020973, -0.00777443777769804, 0.005439540836960077, -0.021052561700344086, -0.02164270170032978, -0.006985447835177183, -0.018974246457219124, -0.025260508060455322, -0.0013630924513563514, -0.03597281128168106, 0.027993107214570045, 0.01635710895061493, -0.02948128432035446, 0.009159981273114681, -0.0026780758053064346, 0.001033544773235917, 0.04051431640982628, 0.001919554895721376, 0.024952609091997147, -0.01960287243127823, 0.0001997531799133867, 0.031405650079250336, 0.021142365410923958, -0.007954045198857784, -0.0008338918560184538, -0.0040572043508291245, 7.381747127510607e-05, -0.019051222130656242, 0.004942412953823805, -0.01888444274663925, 0.028121398761868477, 0.004631306976079941, -0.031559597700834274, -0.003143130801618099, 0.01867917738854885, -0.024208521470427513, 0.0056351847015321255, -0.019333461299538612, -0.0012756941141560674, 0.01737060956656933, 0.020128866657614708, -0.007755194325000048, -0.0071329823695123196, -0.007761608809232712, 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}
) {
question
answer
}
}
}
Example response
The output is like this:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"answer": "Risotto",
"question": "From the Italian word for rice, it's a rice dish cooked with broth & often grated cheese"
},
{
"answer": "arrabiata",
"question": "Italian for \"angry\", it describes a pasta sauce spiced up with plenty of chiles"
},
{
"answer": "Fettucine Alfredo",
"question": "Ribbon-shaped noodles, sweet butter, cream, parmesan cheese & black pepper make up this pasta dish"
}
]
}
}
}
Vector search parameters
v1.25
You can specify vector similarity search parameters similar to near text or near vector searches, such as group by
and move to
/ move away
. An equivalent distance
threshold for vector search can be specified with the max vector distance
parameter.
- Python Client v4
- GraphQL
- JS/TS Client v3
from weaviate.classes.query import HybridVector, Move, HybridFusion
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
query="California",
max_vector_distance=0.4, # Maximum threshold for the vector search component
vector=HybridVector.near_text(
query="large animal",
move_away=Move(force=0.5, concepts=["mammal", "terrestrial"]),
),
alpha=0.75,
limit=5,
)
{
Get {
JeopardyQuestion(
limit: 5
hybrid: {
searches: {
nearText: {
concepts: [ "large animal" ]
}
}
maxVectorDistance: 0.5
alpha: 0.75
query: "California"
}
)
{
question
answer
points
}
}
}
const jeopardy = client.collections.get('JeopardyQuestion');
const result = await jeopardy.query.hybrid('California', {
vector: {
query: 'large animal',
moveAway: { force: 0.5, concepts:['mamal', 'terrestrial'] }
},
// maxVectorDistance support coming soon
alpha: 0.75,
limit: 5,
})
for (let object of result.objects) {
console.log(JSON.stringify(object.properties, null, 2));
}
Example response
The output is like this:
{
"data": {
"Get": {
"JeopardyQuestion": [
{
"answer": "Rhinoceros",
"points": 400,
"question": "The \"black\" species of this large horned mammal can grasp twigs with its upper lip"
},
{
"answer": "the hippopotamus",
"points": 400,
"question": "Close relative of the pig, though its name means \"river horse\""
},
{
"answer": "buffalo",
"points": 400,
"question": "Animal that was the main staple of the Plains Indians economy"
},
{
"answer": "California",
"points": 200,
"question": "Its state animal is the grizzly bear, & the state tree is a type of redwood"
},
{
"answer": "California",
"points": 200,
"question": "This western state sent its first refrigerated trainload of oranges back east February 14, 1886"
}
]
}
}
}
Hybrid search thresholds
v1.25
The only available search threshold is max vector distance
, which will set the maximum allowable distance for the vector search component.
- Python Client v4
- JS/TS Client v3
from weaviate.classes.query import HybridVector, Move, HybridFusion
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
query="California",
max_vector_distance=0.4, # Maximum threshold for the vector search component
alpha=0.75,
limit=5,
)
// TS support coming soon
Group results
v1.25
Define criteria to group search results.
- Python Client v4
# Grouping parameters
group_by = GroupBy(
prop="round", # group by this property
objects_per_group=3, # maximum objects per group
number_of_groups=2, # maximum number of groups
)
# Query
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
alpha=0.75,
query="California",
group_by=group_by
)
for grp_name, grp_content in response.groups.items():
print(grp_name, grp_content.objects)
Example response
The response is like this:
'Jeopardy!'
'Double Jeopardy!'
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
- GraphQL
jeopardy = client.collections.get("JeopardyQuestion")
response = jeopardy.query.hybrid(
query="food",
limit=3,
offset=1