Embeddings

Translate complex data into numerical vector representations

---

Input: Text, images, or audio

Output: Embeddings

Embedding models, often referred to as embeddings or embedding vectors, allow you to convert complex data into vectors while preserving meaningful relationships between them.

Embeddings are a powerful tool in the world of machine learning, acting as a bridge between complex data and the mathematical world that machine learning models operate in.

Data lives in its own world. For example, text is a sequence of words, images are a grid of pixels, and audio is a wave of sound. These are all very different from the numbers that machine learning models use.

Embedding models take these complex data types and transform them into numerical vectors. These vectors are like condensed summaries of the data, capturing its important aspects in a way that the model can understand.

By using these vectors, machine learning models can now reason about the data, compare different pieces of information, and perform tasks like classification, similarity search, and prediction.

info

The initialization code used in the following examples is outlined in detail on the client installation page.

Text Embeddings

Below is an example of how you would create text embeddings using the Cohere Embed-v3 model.

The Cohere Embed-v3 model requires an input_type parameter to be specified, which can be set using one of the following values:

• search_document (default): For texts (documents) intended to be stored in a vector database.
• search_query: For search queries to find the most relevant documents in a vector database.
• classification: If the embeddings are used as input for a classification system.
• clustering: If the embeddings are used for text clustering.

Python

#################################################################################################################
# In this section, we set the user authentication, user and app ID, model details, and the text we want
# to provide as an input. Change these strings to run your own example.
#################################################################################################################

# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = "YOUR_PAT_HERE"
# Specify the correct user_id/app_id pairings
# Since you're making inferences outside your app's scope
USER_ID = "cohere"
APP_ID = "embed"
# Change these to whatever model and text URL you want to use
MODEL_ID = "cohere-embed-english-v3_0"
MODEL_VERSION_ID = "e2dd848faf454fbda85c26cf89c4926e"
RAW_TEXT = "Give me an exotic yet tasty recipe for some noodle dish"
# To use a hosted text file, assign the URL variable
# TEXT_FILE_URL = "https://samples.clarifai.com/negative_sentence_12.txt"
# Or, to use a local text file, assign the location variable
# TEXT_FILE_LOCATION = "YOUR_TEXT_FILE_LOCATION_HERE"

############################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
############################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2
from google.protobuf.struct_pb2 import Struct

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

params = Struct()
params.update(
    {
        "input_type": "search_query" 
    }
)

metadata = (("authorization", "Key " + PAT),)

userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)

# To use a local text file, uncomment the following lines
# with open(TEXT_FILE_LOCATION, "rb") as f:
#    file_bytes = f.read()

post_model_outputs_response = stub.PostModelOutputs(
    service_pb2.PostModelOutputsRequest(
        user_app_id=userDataObject,  # The userDataObject is created in the overview and is required when using a PAT
        model_id=MODEL_ID,
        version_id=MODEL_VERSION_ID,  # This is optional. Defaults to the latest model version
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    text=resources_pb2.Text(
                        raw=RAW_TEXT
                        # url=TEXT_FILE_URL
                        # raw=file_bytes
                    )
                )
            )
        ],
        model=resources_pb2.Model(
            model_version=resources_pb2.ModelVersion(
                output_info=resources_pb2.OutputInfo(params=params)
            )
        ),
    ),
    metadata=metadata,
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
    print(post_model_outputs_response.status)
    raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description )

# Uncomment this line to print the raw output
# print(post_model_outputs_response)

# Since we have one input, one output will exist here
output = post_model_outputs_response.outputs[0].data.embeddings
print(output)

JavaScript (REST)

<!--index.html file-->

<script>
    //////////////////////////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, user and app ID, model details, and the text we want
    // to provide as an input. Change these strings to run your own example.
    //////////////////////////////////////////////////////////////////////////////////////////////////////////////
  
    // Your PAT (Personal Access Token) can be found in the Account's Security section
    const PAT = "YOUR_PAT_HERE";    
    // Specify the correct user_id/app_id pairings
    // Since you're making inferences outside your app's scope
    const USER_ID = "cohere";
    const APP_ID = "embed";
    // Change these to whatever model and text you want to use
    const MODEL_ID = "cohere-embed-english-v3_0";
    const MODEL_VERSION_ID = "e2dd848faf454fbda85c26cf89c4926e";
    const RAW_TEXT = "Give me an exotic yet tasty recipe for some noodle dish";
    // To use a hosted text file, assign the URL variable
    // const TEXT_FILE_URL = 'https://samples.clarifai.com/negative_sentence_12.txt'
    
    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////
  
    const raw = JSON.stringify({  
        "inputs": [
        {
            "data": {
              "text": {
                   "raw": RAW_TEXT
                  // "url": TEXT_FILE_URL
               }
            }
        }
    ],  
      "model": {
          "model_version": {
              "output_info": {
                  "params": {
                      "input_type": "search_query"   
                  }
              }
          }
      }
    });
  
    const requestOptions = {
        method: "POST",
        headers: {
            "Accept": "application/json",
            "Authorization": "Key " + PAT
        },
        body: raw
    };
  
    fetch(`https://api.clarifai.com/v2/users/${USER_ID}/apps/${APP_ID}/models/${MODEL_ID}/versions/${MODEL_VERSION_ID}/outputs`, requestOptions)
        .then(response => response.text())
        .then(result => console.log(result))    
        .catch(error => console.log("error", error));
  
  </script>

NodeJS

//index.js file

////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, model details, and the text we want
// to provide as an input. Change these strings to run your own example.
///////////////////////////////////////////////////////////////////////////////////////////////////////////////// 

// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = "YOUR_PAT_HERE";
// Specify the correct user_id/app_id pairings
// Since you're making inferences outside your app's scope
const USER_ID = "cohere";
const APP_ID = "embed";
// Change these to whatever model and text you want to use
const MODEL_ID = "cohere-embed-english-v3_0";
const MODEL_VERSION_ID = "e2dd848faf454fbda85c26cf89c4926e";
const RAW_TEXT = "Give me an exotic yet tasty recipe for some noodle dish";
// To use a hosted text file, assign the URL variable
// const TEXT_FILE_URL = "https://samples.clarifai.com/negative_sentence_12.txt"
// Or, to use a local text file, assign the location variable
// TEXT_FILE_LOCATION = "YOUR_TEXT_FILE_LOCATION_HERE"

/////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
/////////////////////////////////////////////////////////////////////////////

const { ClarifaiStub, grpc } = require("clarifai-nodejs-grpc");

const stub = ClarifaiStub.grpc();

// This will be used by every Clarifai endpoint call
const metadata = new grpc.Metadata();
metadata.set("authorization", "Key " + PAT);

// To use a local text file, uncomment the following lines
// const fs = require("fs");
// const fileBytes = fs.readFileSync(TEXT_FILE_LOCATION);

stub.PostModelOutputs(
    {
        user_app_id: {
            "user_id": USER_ID,
            "app_id": APP_ID
        },
        model_id: MODEL_ID,
        version_id: MODEL_VERSION_ID,  // This is optional. Defaults to the latest model version
        inputs: [
            {
                "data": {
                    "text": {
                        "raw": RAW_TEXT
                        // url: TEXT_FILE_URL
                        // raw: fileBytes
                    }
                }
            }
        ],
        "model": {
            "model_version": {
                "output_info": {
                    "params": {
                        "input_type": "search_query"   
                    }
                }
            }
        }
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post models failed, status: " + response.status.description);
        }
        // Since we have one input, one output will exist here.
        const output = response.outputs[0].data.embeddings;

        console.log(output);

    }
);

Java

package com.clarifai.example;

import com.clarifai.grpc.api.*;
import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.status.StatusCode;
import com.google.protobuf.Struct;
import com.google.protobuf.Value;

import com.google.protobuf.ByteString;
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;

public class ClarifaiExample {

    ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, user and app ID, model details, and the text we want
    // to provide as an input. Change these strings to run your own example.
    //////////////////////////////////////////////////////////////////////////////////////////////////////////////////

    // Your PAT (Personal Access Token) can be found in the portal under Authentication
    static final String PAT = "YOUR_PAT_HERE";
    // Specify the correct user_id/app_id pairings
    // Since you're making inferences outside your app's scope
    static final String USER_ID = "cohere";
    static final String APP_ID = "embed";
    // Change these to whatever model you want to use
    static final String MODEL_ID = "cohere-embed-english-v3_0";
    static final String MODEL_VERSION_ID = "e2dd848faf454fbda85c26cf89c4926e";
    static final String RAW_TEXT = "Give me an exotic yet tasty recipe for some noodle dish";
    // To use a hosted text file, assign the URL variable
    // static final String TEXT_FILE_URL = "https://samples.clarifai.com/negative_sentence_12.txt";
    // Or, to use a local text file, assign the location variable
    // static final String TEXT_FILE_LOCATION = "YOUR_TEXT_FILE_LOCATION_HERE";

    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////	

    public static void main(String[] args) throws IOException {

        V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
                .withCallCredentials(new ClarifaiCallCredentials(PAT));

        Struct.Builder params = Struct.newBuilder()
                .putFields("input_type", Value.newBuilder().setStringValue("search_query").build());

        MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
                PostModelOutputsRequest.newBuilder()
                        .setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
                        .setModelId(MODEL_ID)
                        .setVersionId(MODEL_VERSION_ID) // This is optional. Defaults to the latest model version.
                        .addInputs(
                                Input.newBuilder().setData(
                                        Data.newBuilder().setText(
                                                Text.newBuilder().setRaw(RAW_TEXT)
                                        // Text.newBuilder().setUrl(TEXT_FILE_URL)
                                        // Text.newBuilder().setRawBytes(ByteString.copyFrom(Files.readAllBytes(
                                        // new File(TEXT_FILE_LOCATION).toPath()
                                        // )))
                                        )
                                )
                        )
                        .setModel(Model.newBuilder()
                                .setModelVersion(ModelVersion.newBuilder()
                                        .setOutputInfo(OutputInfo.newBuilder().setParams(params))
                                )
                        )
                        .build()
        );

        if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
            throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
        }

        // Since we have one input, one output will exist here
        Output output = postModelOutputsResponse.getOutputs(0);

        System.out.println(output.getData().getEmbeddingsList());

    }
}

PHP

<?php

require __DIR__ . "/vendor/autoload.php";

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, model details, and the text we want
// to provide as an input. Change these strings to run your own example.
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////

// Your PAT (Personal Access Token) can be found in the Account's Security section
$PAT = "YOUR_PAT_HERE";
// Specify the correct user_id/app_id pairings
// Since you're making inferences outside your app's scope
$USER_ID = "cohere";
$APP_ID = "embed";
// Change these to whatever model and image URL you want to use
$MODEL_ID = "cohere-embed-english-v3_0";
$MODEL_VERSION_ID = "e2dd848faf454fbda85c26cf89c4926e";
$RAW_TEXT = "Give me an exotic yet tasty recipe for some noodle dish!";
# To use a hosted text file, assign the URL variable
# $TEXT_FILE_URL = "https://samples.clarifai.com/negative_sentence_12.txt";
# Or, to use a local text file, assign the location variable
# $TEXT_FILE_LOCATION = "YOUR_TEXT_FILE_LOCATION_HERE";

///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////

use Clarifai\ClarifaiClient;
use Clarifai\Api\Data;
use Clarifai\Api\Text;
use Clarifai\Api\Input;
use Clarifai\Api\Model;
use Clarifai\Api\ModelVersion;
use Clarifai\Api\OutputInfo;
use Clarifai\Api\PostModelOutputsRequest;
use Clarifai\Api\Status\StatusCode;
use Clarifai\Api\UserAppIDSet;
use Google\Protobuf\Struct;

$client = ClarifaiClient::grpc();

$metadata = ["Authorization" => ["Key " . $PAT]];

$userDataObject = new UserAppIDSet([
    "user_id" => $USER_ID,
    "app_id" => $APP_ID,
]);

// create Struct instance
$params = new Struct();
$params->input_type = "search_query";

//$textData = file_get_contents($TEXT_FILE_LOCATION); // Get the text bytes data from the location

// Let's make a RPC call to the Clarifai platform. It uses the opened gRPC client channel to communicate a
// request and then wait for the response
[$response, $status] = $client->PostModelOutputs(
        // The request object carries the request along with the request status and other metadata related to the request itself
        new PostModelOutputsRequest([
            "user_app_id" => $userDataObject,
            "model_id" => $MODEL_ID,
            "version_id" => $MODEL_VERSION_ID, // This is optional. Defaults to the latest model version
            "inputs" => [
                new Input([
                    // The Input object wraps the Data object in order to meet the API specification
                    "data" => new Data([
                        // The Data object is constructed around the Text object. It offers a container that has additional text independent
                        // metadata. In this particular use case, no other metadata is needed to be specified
                        "text" => new Text([
                            // In the Clarifai platform, a text is defined by a special Text object
                            "raw" => $RAW_TEXT,
                            // "url" => $TEXT_FILE_URL
                            // "raw" => $textData
                        ]),
                    ]),
                ]),
            ],
            "model" => new Model([
                "model_version" => new ModelVersion([
                    "output_info" => new OutputInfo(["params" => $params]),
                ]),
            ]),
        ]),
        $metadata
    )
    ->wait();

// A response is returned and the first thing we do is check the status of it
// A successful response will have a status code of 0; otherwise, there is some error
if ($status->code !== 0) {
    throw new Exception("Error: {$status->details}");
}
// In addition to the RPC response status, there is a Clarifai API status that reports if the operation was a success or failure
// (not just that the communication was successful)
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
    throw new Exception("Failure response: " . $response->getStatus()->getDescription() . " " . $response->getStatus()->getDetails());
}

// Since we have one input, one output will exist here
$output = $response->getOutputs()[0]->getData()->getEmbeddings();

print_r($output);

?>

cURL

curl -X POST "https://api.clarifai.com/v2/users/cohere/apps/embed/models/cohere-embed-english-v3_0/versions/e2dd848faf454fbda85c26cf89c4926e/outputs" \
    -H "Authorization: Key YOUR_PAT_HERE" \
    -H "Content-Type: application/json" \
    -d '{
    "inputs": [
        {
            "data": {
                "text": {
                    "raw": "Give me an exotic yet tasty recipe for some noodle dish"
                }
            }
        }
    ],
    "model": {
        "model_version": {
            "output_info": {
                "params": {
                    "input_type":"search_query"
                }
            }
        }
    }
}'

Text Output Example

[vector: 0.025470778346061707
vector: 0.027972102165222168
vector: -0.01801256835460663
vector: -0.025394519791007042
vector: 0.007034968119114637
vector: -0.0002650028618518263
vector: -0.0037653285544365644
vector: -0.0023831194266676903
vector: -0.03773335739970207
vector: 0.05649327486753464
vector: 0.01828710362315178
vector: 0.00324104237370193
vector: -0.015084192156791687
vector: -0.0007111228187568486
vector: -0.009479095228016376
vector: -0.016197584569454193
vector: 0.01801256835460663
vector: 0.017738033086061478
vector: 0.027041731402277946
vector: 0.005357252433896065
vector: 0.0050331479869782925
vector: 0.028963478282094002
vector: -0.012285456992685795
vector: -0.035171028226614
vector: 0.041058287024497986
vector: -0.030442919582128525
vector: -0.0278653372079134
vector: -0.00623805308714509
vector: 0.011621995829045773
vector: 0.001777807017788291
vector: -0.005467828828841448
vector: 0.01667039655148983
vector: 0.05164314806461334
vector: 0.018698906525969505
vector: -0.07211128622293472
vector: -0.030061621218919754
vector: 0.0001396507868776098
vector: -0.01699068769812584
vector: -0.06198398023843765
vector: 0.05929963290691376
vector: 0.037702854722738266
vector: -0.020605402067303658
vector: -0.06363119184970856
vector: -0.004384939558804035
vector: -0.051002565771341324
vector: -0.021947575733065605
vector: 0.02457091398537159
vector: 0.006009273696690798
vector: 0.00549452006816864
vector: 0.008258937858045101
vector: 0.018912434577941895
vector: -0.005502145737409592
vector: 0.01715845987200737
vector: 0.009242690168321133
vector: -0.020818930119276047
vector: -0.009837516583502293
vector: 0.01894293911755085
vector: 0.005456389859318733
vector: 0.038556963205337524
vector: -0.041485339403152466
vector: -0.0633261501789093
vector: -0.012338838540017605
vector: 0.05271078646183014
vector: -0.00013786344788968563
vector: 0.010684000328183174
vector: 0.0098756467923522
vector: 0.001827375846914947
vector: 0.014962175861001015
vector: -0.006932017393410206
vector: 0.01074500847607851
vector: 0.0010018633911386132
vector: -0.01212531141936779
vector: 0.018043072894215584
vector: -0.09224387258291245
vector: -0.013825904577970505
vector: 0.057499904185533524
vector: 0.023579536005854607
vector: -0.02873469889163971
vector: -0.011759264394640923
vector: -0.03297474607825279
vector: -0.0017129861516878009
vector: 0.020147843286395073
vector: 0.020803678780794144
vector: -0.0855330154299736
vector: 0.013986051082611084
vector: -0.09023061394691467
vector: -0.004651848692446947
vector: 0.0305801872164011
vector: 0.007992029190063477
vector: 0.049172330647706985
vector: 0.0025413583498448133
vector: -0.022039087489247322
vector: -0.0013612377224490047
vector: 0.052253227680921555
vector: 0.03312726318836212
vector: -0.008724123239517212
vector: -0.01790580525994301
vector: -0.08461789786815643
vector: 0.04252247512340546
vector: 0.041271813213825226
vector: 0.07009802758693695
vector: -0.08187253773212433
vector: 0.11414569616317749
vector: -0.01561801042407751
vector: -0.0036909752525389194
vector: 0.03285272791981697
vector: 0.03279172256588936
vector: 0.015030810609459877
vector: -0.007652672939002514
vector: -0.015251963399350643
vector: -0.00938758347183466
vector: 0.015938302502036095
vector: 0.0018321421230211854
vector: -0.003012262750416994
vector: -0.028658440336585045
vector: 0.00906729232519865
vector: -0.017082199454307556
vector: -0.019308986142277718
vector: 0.003138091415166855
vector: -0.007309503387659788
vector: 0.02739252708852291
vector: 0.023366007953882217
vector: 0.005769055336713791
vector: 0.016090821474790573
vector: -0.042674995958805084
vector: -0.023823566734790802
vector: -0.030778462067246437
vector: -0.022908449172973633
vector: 0.0362691693007946
vector: -0.012399846687912941
vector: -0.06302111595869064
vector: 0.007019716314971447
vector: 0.08419083803892136
vector: 0.02656892128288746
vector: 0.02446414902806282
vector: -0.016853420063853264
vector: 0.03079371526837349
vector: -0.010554359294474125
vector: -0.0035651465877890587
vector: -0.0048844413831830025
vector: 0.029954856261610985
vector: -0.028871966525912285
vector: 0.005776681005954742
vector: 0.023625291883945465
vector: -0.02289319783449173
vector: 0.02571481093764305
vector: 0.014779152348637581
vector: -0.011530484072864056
vector: -0.01561801042407751
vector: -0.004785303492099047
vector: -0.03663521632552147
vector: -0.023228740319609642
vector: 0.018927687779068947
vector: -0.01635010540485382
vector: -0.031968116760253906
vector: 0.007694615516811609
vector: -0.03880099579691887
vector: 0.019827552139759064
vector: -0.02527250349521637
vector: 0.011812645941972733
vector: -0.06326514482498169
vector: -0.019308986142277718
vector: -0.00752303097397089
vector: -0.012155815027654171
vector: 0.0037081337068229914
vector: 0.005418260116130114
vector: 0.057011839002370834
vector: 0.03416439890861511
vector: 0.013025176711380482
vector: 0.006039777770638466
vector: 0.040295686572790146
vector: -0.02879570797085762
vector: -0.052802298218011856
vector: -0.02136800065636635
vector: 0.0009060619631782174
vector: -0.003061831695958972
vector: 0.026507912203669548
vector: 0.005151350516825914
vector: -0.020757922902703285
vector: 0.047494616359472275
vector: 0.012110059149563313
vector: -0.0232744961977005
vector: 0.0005414446932263672
vector: -0.002543264999985695
vector: -0.016380608081817627
vector: 0.009097795933485031
vector: -0.01482490822672844
vector: 0.04380363970994949
vector: 0.005200919695198536
vector: 0.0028940599877387285
vector: -0.0064058247953653336
vector: 0.0072713736444711685
vector: -0.05149063095450401
vector: 0.03614715486764908
vector: 0.03712327778339386
vector: -0.014977428130805492
vector: -0.01872941106557846
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Raw Output Example

status {
  code: SUCCESS
  description: "Ok"
  req_id: "d03748f58ba35a376266f4935132521b"
}
outputs {
  id: "a52203d9a2114428ac6ed23139e1afa9"
  status {
    code: SUCCESS
    description: "Ok"
  }
  created_at {
    seconds: 1702460385
    nanos: 732703311
  }
  model {
    id: "cohere-embed-english-v3_0"
    name: "embed-english-v3_0"
    created_at {
      seconds: 1699267051
      nanos: 239483000
    }
    app_id: "embed"
    model_version {
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      created_at {
        seconds: 1699269295
        nanos: 601056000
      }
      status {
        code: MODEL_TRAINED
        description: "Model is trained and ready"
      }
      completed_at {
        seconds: 1699269521
        nanos: 395913000
      }
      visibility {
        gettable: PUBLIC
      }
      app_id: "embed"
      user_id: "cohere"
      metadata {
      }
    }
    user_id: "cohere"
    model_type_id: "text-embedder"
    visibility {
      gettable: PUBLIC
    }
    modified_at {
      seconds: 1701248637
      nanos: 454591000
    }
    workflow_recommended {
    }
  }
  input {
    id: "3ce817f59bbe422d89d68b16dcd0b98b"
    data {
      text {
        raw: "Give me an exotic yet tasty recipe for some noodle dish"
        url: "https://samples.clarifai.com/placeholder.gif"
      }
    }
  }
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