Combine or Negate

Group or separate items in your dataset.

You can also combine searches. Unlike our legacy search, in annotation search, Filter and Rank is a list of Annotation objects. Filtered annotations will be ANDed. When you combine both Filter and Rank, filter will be applied before ranking annotations. This is important because limiting the result set on large applications can speedup the overall query drastically when doing a ranking.

Java

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 postAnnotationsSearchesResponse = stub.postAnnotationsSearches(
    PostAnnotationsSearchesRequest.newBuilder().addSearches(
        Search.newBuilder().setQuery(
            Query.newBuilder()
                .addFilters(
                    Filter.newBuilder().setAnnotation(
                        Annotation.newBuilder().setData(
                                Data.newBuilder().addConcepts(  // You can search by multiple concepts.
                                Concept.newBuilder()
                                    .setId("cat")  // You could search by concept Name as well.
                                    .setValue(1f)  // Value of 0 will search for images that don't have the concept.
                            )
                        )
                    )
                )
                .addRanks(
                Rank.newBuilder().setAnnotation(
                    Annotation.newBuilder().setData(
                            Data.newBuilder().addConcepts(  // You can search by multiple concepts.
                            Concept.newBuilder()
                                .setId("dog")  // You could search by concept Name as well.
                                .setValue(1f)  // Value of 0 will search for images that don't have the concept.
                        )
                    )
                )
            )
        )    
    )
    .build()
);

if (postAnnotationsSearchesResponse.getStatus().getCode() != StatusCode.SUCCESS) {
  throw new RuntimeException("Post annotations searches failed, status: " + postAnnotationsSearchesResponse.getStatus());
}

System.out.println("Found inputs " + postAnnotationsSearchesResponse.getHitsCount() + ":");
for (Hit hit : postAnnotationsSearchesResponse.getHitsList()) {
    System.out.printf("\tScore %.2f for annotation % of input %s\n", hit.getScore(), hit.getAnnotation().getId(), hit.getInput().getId())
}

NodeJS

// 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.PostAnnotationsSearches(
    {
        searches: [
            {
                query: {
                    filters: [
                        {
                            annotation: {
                                data: {
                                    concepts: [  // You can search by multiple concepts.
                                        {
                                            id: "cat",  // You could search by concept Name as well.
                                            value: 1  // Value of 0 will search for images that don't have the concept
                                        }
                                    ]
                                }
                            }
                        }
                    ],
                    ranks: [
                        {
                            annotation: {
                                data: {
                                    concepts: [  // You can search by multiple concepts.
                                        {
                                            id: "dog",  // You could search by concept Name as well.
                                            value: 0  // Value of 0 will search for images that don't have the concept
                                        }
                                    ]
                                }
                            }
                        }
                    ]             
                }
            }
        ]
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post annotations searches failed, status: " + response.status.description);
        }

        console.log("Search result:");
        for (const hit of response.hits) {
            console.log("\tScore " + hit.score + " for annotation: " + hit.annotation.id + " of input: ", hit.input.id);
        }
    }
);

Python

from clarifai_grpc.grpc.api import service_pb2, resources_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2

# 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_annotations_searches_response = stub.PostAnnotationsSearches(
    service_pb2.PostAnnotationsSearchesRequest(
        searches = [
            resources_pb2.Search(
                query=resources_pb2.Query(
                    filters=[
                        resources_pb2.Filter(
                            annotation=resources_pb2.Annotation(
                                data=resources_pb2.Data(
                                    concepts=[  # You can search by multiple concepts.
                                        resources_pb2.Concept(
                                            id="cat",  # You could search by concept Name as well.
                                            value=1  # Value of 0 will search for images that don't have the concept.
                                        )
                                    ]
                                )
                            )
                        )
                    ],
                    ranks=[
                        resources_pb2.Rank(
                            annotation=resources_pb2.Annotation(
                                data=resources_pb2.Data(
                                    concepts=[  # You can search by multiple concepts.
                                        resources_pb2.Concept(
                                            id="dog",  # You could search by concept Name as well.
                                            value=0  # Value of 0 will search for images that don't have the concept.
                                        )
                                    ]
                                )
                            )
                        )
                    ]
                )
            )
        ]
    ),
    metadata=metadata
)

if post_annotations_searches_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(post_annotations_searches_response.outputs[0].status.code))
    print("\tDescription: {}".format(post_annotations_searches_response.outputs[0].status.description))
    print("\tDetails: {}".format(post_annotations_searches_response.outputs[0].status.details))
    raise Exception("Post searches failed, status: " + post_annotations_searches_response.status.description)

print("Search result:")
for hit in post_annotations_searches_response.hits:
    print("\tScore %.2f for annotation: %s off input: %s" % (hit.score, hit.annotation.id, hit.input.id))

cURL

# 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 '
{
    "searches": [
      {
        "query": {
          "filters": [
            {
              "annotation": {
                "data": {
                  "concepts": [
                    {
                      "id":"people",
                      "value": 1
                    }
                  ]
                }
              }
            }
          ],
          "ranks": [
            {
              "annotation": {
                "data": {
                  "concepts": [
                    {
                      "id":"people",
                      "value": 1
                    }
                  ]
                }
              }
            }
          ]
        }
      }
    ]
}'\
https://api.clarifai.com/v2/searches

Javascript (REST)

const raw = JSON.stringify({
  "user_app_id": {
		"user_id": "{YOUR_USER_ID}",
		"app_id": "{YOUR_APP_ID}"
	},
  "searches": [
    {
      "query": {
        "filters": [
          {
            "annotation": {
              "data": {
                "concepts": [
                  {
                    "id":"people",
                    "value": 1
                  }
                ]
              }
            }
          }
        ],
        "ranks": [
          {
            "annotation": {
              "data": {
                "concepts": [
                  {
                    "id":"people",
                    "value": 1
                  }
                ]
              }
            }
          }
        ]
      }
    }
  ]
});

const requestOptions = {
  method: 'POST',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  },
	body: raw
};

fetch(`https://api.clarifai.com/v2/searches`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));