Combine or Negate
You can also combine searches using AND.
import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
// Here we search for images which we labeled with "cat" and for which the General prediction model does not find
// a "dog" concept.
MultiSearchResponse postSearchesResponse = stub.postSearches(
PostSearchesRequest.newBuilder().setQuery(
Query.newBuilder()
.addAnds(
And.newBuilder().setInput( // Setting Input indicates we search for images that have the concept(s)
// which we added to the input manually.
Input.newBuilder().setData(
Data.newBuilder().addConcepts(
Concept.newBuilder()
.setName("cat")
.setValue(1f)
)
)
)
)
.addAnds(
And.newBuilder().setOutput( // Setting Output indicates we search for images that have the concept(s)
// which were predicted by the General model.
Output.newBuilder().setData(
Data.newBuilder().addConcepts(
Concept.newBuilder()
.setName("dog")
.setValue(0f)
)
)
)
)
)
.build()
);
if (postSearchesResponse.getStatus().getCode() != StatusCode.SUCCESS) {
throw new RuntimeException("Post searches failed, status: " + postSearchesResponse.getStatus());
}
System.out.println("Found inputs " + postSearchesResponse.getHitsCount() + ":");
for (Hit hit : postSearchesResponse.getHitsList()) {
System.out.printf("\tScore %.2f for %s\n", hit.getScore(), hit.getInput().getId());
}// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
// Here we search for images which we labeled with "cat" and for which the General prediction model does not find
// a "dog" concept.
stub.PostSearches(
{
query: {
ands: [
{
input: { // Setting Input indicates we search for images that have the concept(s)
// which we added to the input manually.
data: {
concepts: [
{
name: "cat",
value: 1
}
]
}
}
},
{
output: { // Setting Output indicates we search for images that have the concept(s)
// which were predicted by the General model.
data: {
concepts: [
{
name: "dog",
value: 0
}
]
}
}
}
]
}
},
metadata,
(err, response) => {
if (err) {
throw new Error(err);
}
if (response.status.code !== 10000) {
throw new Error("Post searches failed, status: " + response.status.description);
}
console.log("Found inputs:");
for (const hit of response.hits) {
console.log("\tScore " + hit.score + " for " + hit.input.id);
}
}
);# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
# Here we search for images which we labeled with "cat" and for which the General prediction model does not find
# a "dog" concept.
post_searches_response = stub.PostSearches(
service_pb2.PostSearchesRequest(
query=resources_pb2.Query(
ands=[
resources_pb2.And(
input=resources_pb2.Input( # Setting Input indicates we search for images that have the concept(s)
# which we added to the input manually.
data=resources_pb2.Data(
concepts=[
resources_pb2.Concept(
name="cat",
value=1
)
]
)
)
),
resources_pb2.And(
output=resources_pb2.Output( # Setting Output indicates we search for images that have the concept(s)
# which were predicted by the General model.
data=resources_pb2.Data(
concepts=[
resources_pb2.Concept(
name="dog",
value=0
)
]
)
)
)
]
)
),
metadata=metadata
)
if post_searches_response.status.code != status_code_pb2.SUCCESS:
raise Exception("Post searches failed, status: " + post_searches_response.status.description)
print("Found inputs:")
for hit in post_searches_response.hits:
print("\tScore %.2f for %s" % (hit.score, hit.input.id))app.inputs.search([
{ input: { url: 'https://samples.clarifai.com/puppy.jpeg' } },
{ concept: { name: 'cat', type: 'input' } },
{ concept: { name: 'dog' } }
]).then(
function(response) {
// do something with response
},
function(err) {
// there was an error
}
);from clarifai.rest import ClarifaiApp, InputSearchTerm, OutputSearchTerm, SearchQueryBuilder
app = ClarifaiApp(api_key='YOUR_API_KEY')
term1 = InputSearchTerm(concept='cat')
term2 = OutputSearchTerm(concept='dog', value=False)
term3 = OutputSearchTerm(url="https://samples.clarifai.com/metro-north.jpg")
query = SearchQueryBuilder()
query.add_term(term1)
query.add_term(term2)
query.add_term(term3)
app.inputs.search(query)client.searchInputs()
.ands(
SearchClause.matchUserTaggedConcept(Concept.forName("cat")),
SearchClause.matchConcept(Concept.forName("dog").withValue(false)),
SearchClause.matchImageVisually(ClarifaiImage.of("https://samples.clarifai.com/metro-north.jpg"))
)
.getPage(1)
.executeSync();using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Searches;
namespace YourNamespace
{
public class YourClassName
{
public static async Task Main()
{
var client = new ClarifaiClient("YOUR_API_KEY");
await client.SearchInputs(
SearchBy.UserTaggedConceptName("cat"),
SearchBy.ConceptName("dog"),
SearchBy.ImageURL("https://samples.clarifai.com/metro-north.jpg"))
.Page(1)
.ExecuteAsync();
}
}
}//Search for inputs that are predicted as "fast" and visually similar to the given image.
ClarifaiConcept *conceptFromGeneralModel = [[ClarifaiConcept alloc] initWithConceptName:@"fast"];
ClarifaiSearchTerm *term1 = [ClarifaiSearchTerm searchByPredictedConcept:conceptFromGeneralModel];
ClarifaiSearchTerm *term2 = [ClarifaiSearchTerm searchVisuallyWithImageURL:@"https://samples.clarifai.com/metro-north.jpg"];
[_app search:@[term1, term2] page:@1 perPage:@20 completion:^(NSArray<ClarifaiSearchResult *> *results, NSError *error) {
// Print output of first search result.
NSLog(@"inputID: %@", results[0].inputID);
NSLog(@"URL: %@", results[0].mediaURL);
NSLog(@"probability of input matching search query: %@", results[0].score);
}];use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Searches\SearchBy;
use Clarifai\DTOs\Searches\SearchInputsResult;
$client = new ClarifaiClient('YOUR_API_KEY');
$response = $client->searchInputs([
SearchBy::userTaggedConceptName('cat'),
SearchBy::conceptName('dog'),
SearchBy::imageURL('https://samples.clarifai.com/metro-north.jpg')
])
->executeSync();
if ($response->isSuccessful()) {
echo "Response is successful.\n";
/** @var SearchInputsResult $result */
$result = $response->get();
foreach ($result->searchHits() as $searchHit) {
echo $searchHit->input()->id() . ' ' . $searchHit->score() . "\n";
}
} else {
echo "Response is not successful. Reason: \n";
echo $response->status()->description() . "\n";
echo $response->status()->errorDetails() . "\n";
echo "Status code: " . $response->status()->statusCode();
}# Here we search for images which we labeled with "cat" and for which the General prediction model does not find
# a "dog" concept.
curl -X POST \
-H "Authorization: Key {api-key}" \
-H "Content-Type: application/json" \
-d '
{
"query": {
"ands": [
{
"input":{
"data": {
"concepts": [
{
"name": "cat",
"value": 1
}
]
}
}
},
{
"output": {
"data": {
"concepts": [
{
"name": "dog",
"value": 0
}
]
}
}
}
]
}
}'\
https://api.clarifai.com/v2/searchesLast updated