This call doesn't return a response body. Instead, it returns an HTTP status code 204.

This is the cURL request.

curl -X DELETE -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" https://api.einstein.ai/v2/oauth2/tokens/<REFRESH_TOKEN>

Returns an OAuth access token. You must pass a valid access token in the header of each API call.

For information about how to create an access token, see Generate an OAuth Token Using Your Key. For information about how to create a refresh token, see Generate an OAuth Token Using a Refresh Token.

This is the cURL request for generating an access or refresh token.

// Generate an access token
curl -H "Content-type: application/x-www-form-urlencoded" -X POST https://api.einstein.ai/v2/oauth2/token -d "grant_type=urn:ietf:params:oauth:grant-type:jwt-bearer&assertion=<ASSERTION_STRING>"

// Generate a refresh token
curl -H "Content-type: application/x-www-form-urlencoded" -X POST https://api.einstein.ai/v2/oauth2/token -d "grant_type=urn:ietf:params:oauth:grant-type:jwt-bearer&assertion=<ASSERTION_STRING>&scope=offline"

Each apiusage object in the response contains plan information for a single calendar month for a single license. If you have a six-month paid plan and you make this call on the first month, the response contains six apiusage objects; one for each calendar month in the plan. For more information about plans, see Rate Limits.

This the cURL request for this call.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" https://api.einstein.ai/v2/apiusage

• If you're using the free tier, the response contains plan information only for the current month. You see plan information only after you make your first prediction. If you call the /apiusage resource before you make your first prediction call, the API returns an empty array.
• If you're using the paid tier, the response contains plan information for each month in your plan starting with the current month.

The planData array contains an object for each plan type associated with the calendar month and the license. This code snippet shows the planData if the user has two Heroku GOLD plans.

"planData": [
       {
         "plan": "GOLD",
         "amount": 2,
         "source": "HEROKU"
       }
     ]

Returns a single dataset.

Deletes the specified text dataset and its labels and examples.

This is an example of the cURL request.

curl -X DELETE -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" https://api.einstein.ai/v2/language/datasets/10008

Keep the following points in mind when deleting a dataset.

• If a dataset is being trained and has an associated model with a status of QUEUED or RUNNING, you must wait until the training is complete before you can delete the dataset.

• If you want to delete a dataset and the models associated with it, delete the models first. Deleting a dataset doesn't delete the models created from that dataset. After you delete a dataset, you can still make predictions against the associated models, but you lose the ability to list the models for the deleted dataset.

Important: As of August 14, 2023 Get Deletion Status has been deprecated and will no longer be supported by Salesforce. Deletion is now immediate. To determine if the a dataset has been deleted use the Get Dataset command. A deleted dataset will return a 404 (Not Found) http status.

• After you delete a dataset, use the id to get the status of the deletion. See Get Deletion Status.

Returns a list of datasets and their labels that were created by the current user. The response is sorted by dataset ID.

This is an example of the cURL request.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" https://api.einstein.ai/v2/language/datasets

Page Through Datasets

By default, this call returns 25 datasets. If you want to page through your datasets, use the offset and count query parameters.

Here's an example of these query parameters. If you omit the count parameter, the API returns 25 datasets. If you omit the offset parameter, paging starts at 0.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache"  "https://api.einstein.ai/v2/language/models/TUWBAIRLFB3KKEDZMEGIUR6COM/lc?offset=100&count=20"

For example, let's say you want to page through all of your datasets and show 20 at a time. The first call would have offset=0 and count=20, the second call would have offset=20 and count=20, and so on.

Get Global Datasets

Global datasets are public datasets that Salesforce provides. You can use these datasets to include additional data during training when you create a model. To get a list of the global datasets, use the global query parameter.

Here's an example of the global query parameter. The response JSON is the same as for your own custom datasets.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache"  "https://api.einstein.ai/v2/language/datasets?global=true"

Creates a dataset, labels, and examples from the specified .csv, .tsv, or .json file. The call returns immediately and continues to upload data in the background.

Request Parameters

Here's an example of the cURL request with parameters.

// Create a dataset from a local file
curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "data=@C:\Data\weather.csv" -F "type=text-intent"   https://api.einstein.ai/v2/language/datasets/upload

The API call is asynchronous, so you receive a dataset ID back immediately but the available value is false and the statusMsg value is UPLOADING. Use the dataset ID and make a call to Get a Dataset to query when the upload is complete. When available is true and statusMsg is SUCCEEDED, the data upload is complete, and you can train the dataset to create a model.

Keep the following points in mind when creating datasets.

FILE SIZE

• The maximum file size you can upload from a local drive or web location is 25 MB.

• If your file is more than 20 MB, for better performance, we recommend that you upload it to a cloud location that doesn't require authentication and pass the URL in the path parameter.

• If you have a large amount of data, consider breaking up your data into multiple files. You can load the first file using this call and then load subsequent files using PUT. See Create Examples From a File.

DATASETS

• The maximum total dataset size is 2 GB.

LABELS

• The maximum label name length is 180 characters. If a file contains a class label name greater than 180 characters, the label is created in the dataset, but the API truncates the label name to 180 characters.

• You must have at least two labels in the dataset. If you don't have at least two labels, you can create the dataset but training the dataset fails.

• A dataset can have up to 500 labels. However, we recommend that a dataset has a maximum of 100 labels for better model accuracy.

EXAMPLES

• If a file contains duplicate intent or sentiment strings, only the first one is loaded.

• If the dataset type is text-intent, each label must have at least 20 examples. We recommend that each label has at least 100 examples for better model accuracy. If you have only 20 examples, be sure to use the multilingual-intent or multilingual-intent-ood algorithm when you train the dataset.

• If the dataset type is text-sentiment, each label must have at least 100 examples.

• We recommend a maximum of 10,000 examples across all labels.

• A dataset can have a maximum of 3 million words across all examples. If you try to train a dataset that has more than 3 million words, you receive an error.

• For best results, we recommend that each example is less than 150 words.

• If the file contains duplicate text strings (examples), only the first string is uploaded. If there's more than one text string with the same text, only the first string is uploaded and the others are skipped. This is true whether the duplicate text strings have the same label or different labels.

OTHER

• The Einstein Language APIs support only UTF-8 text characters. If your examples or labels contain any non-UTF-8 text, you receive an error that the file format is invalid when you try to create the dataset.

Each dataset type supports different file formats. This table lists the file formats supported by each dataset type.

CSV File Format Considerations

• You can use a .csv file to create both intent and sentiment datasets.

• When you create a dataset from a .csv file, the dataset name is inherited from the file name.

• Each .csv file contains sentiment or intent data in this format: "sentiment or intent string", label-name followed by a CRLF. In the following example, the intent string is in double quotes followed by the label current-weather.

"what's the weather look like",current-weather
"is it raining",current-weather
"what's the temperature",current-weather

TSV File Format Considerations

• You can use a .tsv file to create both intent and sentiment datasets.

• When you create a dataset from a .tsv file, the dataset name is inherited from the file name.

• Each .tsv file contains sentiment or intent data in this format: "sentiment or intent string"<tab_char>label-name followed by a CRLF. In the following example, the intent string is in double quotes followed by the label current-weather.

"what's the weather look like"  current-weather
"is it raining" current-weather
"what's the temperature"    current-weather

JSON File Format Considerations

• You can use a .json file to create only an intent dataset.

• The top-level object must be called "intents" as shown in the example file.

• When you create a dataset from a .json file, the dataset name is inherited from the file name.

• The JSON structure is a top-level object that contains multiple arrays. Each array contains the intent strings in double quotes. This image shows the structure of the JSON file. The labels are current-weather, five-day-forecast, and hourly-forecast. The content in double quotes becomes a single example that has the specified label.

Creates a dataset, labels, and examples from the specified .csv, .tsv, or .json file. The call returns after the dataset is created and all of the data is uploaded. Use this API call for files that are smaller than 10 MB.

Request Parameters

Here's an example of the cURL request with parameters.

// Create a dataset from a local file
curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "data=@C:\Data\weather.csv" -F "type=text-intent"   https://api.einstein.ai/v2/language/datasets/upload/sync

The API call is synchronous, so results are returned after the data has been uploaded to the dataset. If this call succeeds, it returns the labels array, available is true, and statusMsg is SUCCEEDED.

Keep the following points in mind when creating datasets.

FILE SIZE

• The maximum file size you can upload from a local drive or web location is 25 MB.

• If your file is larger than 10 MB, we recommend that you use the asynchronous call to create a dataset. If you use this call with a large dataset .zip file, the call could time out. See Create a Dataset From a File Asynchronously.

• If you have a large amount of data, consider breaking up your data into multiple files. You can load the first file using this call and then load subsequent files using PUT. See Create Examples From a File.

DATSETS

• The maximum total dataset size is 2 GB.

LABELS

• The maximum label name length is 180 characters. If a file contains a class label name greater than 180 characters, the label is created in the dataset, but the API truncates the label name to 180 characters.

• You must have at least two labels in the dataset. If you don't have at least two labels, you can create the dataset but training the dataset fails.

• A dataset can have up to 500 labels. However, we recommend that a dataset has a maximum of 100 labels for better model accuracy.

EXAMPLES

• If a file contains duplicate intent or sentiment strings, only the first one is loaded.

• If the dataset type is text-intent, each label must have at least 20 examples. We recommend that each label has at least 100 examples for better model accuracy. If you have only 20 examples, be sure to use the multilingual-intent or multilingual-intent-ood algorithm when you train the dataset.

• If the dataset type is text-sentiment, each label must have at least 100 examples.

• We recommend a maximum of 10,000 examples across all labels.

• A dataset can have a maximum of 3 million words across all examples. If you try to train a dataset that has more than 3 million words, you receive an error.

• For best results, we recommend that each example is less than 150 words.

-If the file contains duplicate text strings, only the first string is uploaded. If there's more than one text string with the same text, only the first string is uploaded and the others are skipped. This is true whether the duplicate text strings have the same label or different labels.

OTHER

• The Einstein Language APIs support only UTF-8 text characters. If your examples or labels contain any non-UTF-8 text, you receive an error that the file format is invalid when you try to create the dataset.

Each dataset type supports different file formats. This table lists the file formats supported by each dataset type.

CSV File Format Considerations

• You can use a .csv file to create both intent and sentiment datasets.

• When you create a dataset from a .csv file, the dataset name is inherited from the file name.

• Each .csv file contains sentiment or intent data in this format: "sentiment or intent string", label-name followed by a CRLF. In the following example, the intent string is in double quotes followed by the label current-weather.

"what's the weather look like",current-weather
"is it raining",current-weather
"what's the temperature",current-weather

TSV File Format Considerations

• You can use a .tsv file to create both intent and sentiment datasets.

• When you create a dataset from a .tsv file, the dataset name is inherited from the file name.

• Each .tsv file contains sentiment or intent data in this format: "sentiment or intent string"<tab_char>label-name followed by a CRLF. In the following example, the intent string is in double quotes followed by the label current-weather.

"what's the weather look like"  current-weather
"is it raining" current-weather
"what's the temperature"    current-weather

JSON File Format Considerations

• You can use a .json file to create only an intent dataset.

• The top-level object must be called "intents" as shown in the example file.

• When you create a dataset from a .json file, the dataset name is inherited from the file name.

• The JSON structure is a top-level object that contains multiple arrays. Each array contains the intent strings in double quotes. This image shows the structure of the JSON file. The labels are current-weather, five-day-forecast, and hourly-forecast. The content in double quotes becomes a single example that has the specified label.

Returns all the examples for the specified dataset.

This is an example of the cURL request.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" https://api.einstein.ai/v2/language/datasets/57/examples

Page Through Examples

By default, this call returns 100 examples. If you want to page through the examples in a dataset, use the offset and count query parameters.

Here's an example of these query parameters. If you omit the count parameter or the count parameter is greater than 100, the API returns 100 examples. If you omit the offset parameter, paging starts at 0.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache"  "https://api.einstein.ai/v2/language/datasets/57/examples?offset=100&count=50"

How Paging Works

To page through all the examples in a dataset:

1. Make the Get a Dataset call to return the totalExamples value for the dataset.
2. Make the Get All Examples call and pass in the offset and count values until you reach the end of the examples.

For example, let's say you have a dataset and you want to display information about the examples in a UI and show 15 at a time. The first call would have offset=0 and count=15, the second call would have offset=15 and count=15, and so on.

Return Specific Example Types

Use the source query parameter to return examples that were created in the dataset.

• all—Return upload examples.
• upload—Return examples that were created from uploading a file.

Limits

Each Einstein Vision and Language account is limited to 30 calls per calendar month to Einstein Language endpoints that return examples.

This limit applies across all APIs that return examples. If you exceed this limit, you receive an error message. See Release Notes.

Adds examples from a .csv, .tsv, or .json file to a dataset.

Request Parameters

Here's an example of the cURL request with parameters.

// Add examples to a dataset from a local file
curl -X PUT -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "data=@C:\Data\weather_update.csv"  https://api.einstein.ai/v2/language/datasets/1001412/upload

This call adds examples to the specified dataset from a .csv, .tsv, or .json file. This is an asynchronous call, so the results that are initially returned contain information for the original dataset and available is false.

Use the dataset ID and use Get a Dataset to query when the upload is complete. When available is true and statusMsg is SUCCEEDED, the data upload is complete.

Each dataset type supports different file formats. This table lists the file formats supported by each dataset type.

Keep the following tipd in mind when you create examples:

FILE SIZE

• The maximum file size you can upload from a local drive is 25 MB.

DATASETS

• The maximum total dataset size is 2 GB.

LABELS

• If the file contains a label that's already in the dataset, the API adds the sentiment or intent strings (examples) with the specified label in the dataset.

• If the file contains a label that isn't in the dataset, the API adds a new label (label name limit is 180 characters).

EXAMPLES

• A dataset can have a maximum of 3 million words across all examples. If you try to train a dataset that has more than 3 million words, you receive an error.

• For best results, we recommend that each example is around 100 words.

OTHER

• The Einstein Language APIs support only UTF-8 text characters. If your examples or labels contain any non-UTF-8 text, you receive an error that the file format is invalid when you try to create the examples.

• If you try to create examples in a dataset while a previous call to create examples is still processing (the dataset's available value is false), the call fails and you receive an error. You must wait until the dataset's available value is true before starting another upload.

See Create a Dataset From a File Asynchronously or Create a Dataset From a File Synchronously for information about the file structure.

Returns the status of a training job. Use the progress field to determine how far the training has progressed. When training completes successfully, the status is SUCCEEDED and the progress is 1.

This is an example of the cURL request.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" https://api.einstein.ai/v2/language/train/TUWBAIRLFB3KKEDZMEGIUR6COM

Retrains a dataset and updates a model. Use this API call when you want to update a model and keep the model ID instead of creating a new model.

Request Parameters

This is an example of the cURL request with parameters.

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "modelId=TUWBAIRLFB3KKEDZMEGIUR6COM"  https://api.einstein.ai/v2/language/retrain

This call retrains the dataset associated with model you specify in the request parameters. Use this call to retrain a dataset and update the model after new examples are added to a dataset. To find out the values specified in the trainParams parameter when the model was trained, such as withGlobalDatasetId, see Get Training Status.

Keep the following points in mind when training a dataset:

• A dataset can have only one training in progress at a time. Let's say you retrain a dataset and there's a model with a status of RUNNING or QUEUED. If you attempt to retrain the same dataset again, you receive an error.
• You receive an error when you train a dataset that has more than 3 million words across all examples. Be sure that when you create a dataset or add examples to a dataset, that it contains less than 3 million words. For best results, we recommend that each example is around 100 words.

This cURL command sends in the trainParams request parameter. This command has double quotes and escaped double quotes around trainSplitRatio to run on Windows. You might need to reformat it to run on another OS.

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "modelId=TUWBAIRLFB3KKEDZMEGIUR6COM" -F "trainParams={\"trainSplitRatio\":0.7}" https://api.einstein.ai/v2/language/retrain

Trains a dataset and creates a model.

Request Parameters

This is an example of the cURL request with parameters.

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "name=Weather Intent Model" -F "datasetId=1001411" https://api.einstein.ai/v2/language/train

Keep the following points in mind when training a dataset:

• If you’re unsure which values to set for the epochs and learningRate parameters, we recommend that you omit them and use the defaults.
• A dataset can have only one training in progress at a time. Let's say you train a dataset and there's a model with a status of RUNNING or QUEUED. If you attempt to train the same dataset again, you receive an error.
• You receive an error when you train a dataset that has more than 3 million words across all examples. Be sure that when you create a dataset or add examples to a dataset, that it contains less than 3 million words. For best results, we recommend that each example is around 100 words.

This cURL command sends in the trainParams request parameter. This command has double quotes and escaped double quotes around trainSplitRatio to run on Windows. You might need to reformat it to run on another OS.

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "name=Weather Intent Model" -F "datasetId=1001411" -F "trainParams={\"trainSplitRatio\":0.7}" https://api.einstein.ai/v2/language/train

If you want to train a dataset and update an existing model, see Retrain a Dataset.

Returns all models for the specified dataset.

This is an example of the cURL request.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" https://api.einstein.ai/v2/language/datasets/1001511/models

Page Through Models

By default, this call returns 100 models. If you want to page through your models, use the offset and count query parameters.

Here's an example of these query parameters. If you omit the offset parameter, paging starts at 0.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" "https://api.einstein.ai/v2/language/datasets/<DATASET_ID>/models?offset=5&count=5"

Returns the metrics for a model, such as the f1 score, accuracy, and confusion matrix. The combination of these metrics gives you a picture of model accuracy and how well the model will perform. This call returns the metrics for the last epoch in the training used to create the model.

This is an example of the cURL request.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" https://api.einstein.ai/v2/language/models/TUWBAIRLFB3KKEDZMEGIUR6COM

Keep the following points in mind when analyzing the precision-recall metrics.

• The precision-recall curve arrays are calculated using the examples in the test set (as specified by the trainSplitRatio). The margin of error when these values are computed depends on the number of examples in the test set.
• The granularity of the threshold variations depends on the number of examples in the test set and the number of labels in the dataset from which the model was created.
• The maximum number of values returned in any one of these arrays is 2,000.

Deletes the specified model.

After you delete a model, you can use the id to get the status of the deletion. See Get Deletion Status.

This is an example of the cURL request.

curl -X DELETE -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" https://api.einstein.ai/v2/language/models/KD33Y2UZHI2TDB7VXQSUHZBNGA

Returns the metrics for each epoch in a model. These metrics show you the f1 score, accuracy, confusion matrix, test accuracy, and so on for each training iteration performed to create the model.

This is an example of the cURL request.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" "https://api.einstein.ai/v2/language/models/TUWBAIRLFB3KKEDZMEGIUR6COM/lc?offset=0&count=3"

Use the labels array and the confusionMatrix array to build the confusion matrix for an epoch. The labels in the array become the matrix rows and columns. Here's what the confusion matrix for the first epoch in the results.

Page Through Results

By default, this call returns learning curve data for 25 epochs. If you want to page through the results, use the offset and count query parameters.

Here's an example of these query parameters. If you omit the count parameter, the API returns 25 epochs. If you omit the offset parameter, paging starts at 0.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache"  "https://api.einstein.ai/v2/language/datasets?offset=100&count=20"

For example, let's say you want to page through all of the learning curve results and show 20 at a time. The first call would have offset=0 and count=20, the second call would have offset=20 and count=20, and so on.

If you want to find out the total number of epochs that were performed to create a model, make the call the get the training status and use the lastEpochDone value. This value will give you an idea of how much paging you need to do. See Get Training Status.

The Einstein NER (beta) named-entity recognition (NER) model returns entities for a specified string.

Note: As a beta feature, Einstein NER is a preview and isn’t part of the “Services” under your Main Services Agreement with Salesforce. Use this feature at your sole discretion, and make your purchase decisions only on the basis of generally available products and features. Salesforce doesn’t guarantee general availability of this feature within any particular time frame or at all, and we can discontinue it at any time. This feature is for evaluation purposes only, not for production use. It’s offered as is and isn’t supported, and Salesforce has no liability for any harm or damage arising out of or in connection with it. All restrictions, Salesforce reservation of rights, obligations concerning the Services, and terms for related Non-Salesforce Applications and Content apply equally to your use of this feature.

Request Parameters

Note: As a beta feature, Danish, Korean, and Swedish language support is a preview and isn’t part of the “Services” under your master subscription agreement with Salesforce. Use this feature at your sole discretion, and make your purchase decisions only on the basis of generally available products and features. Salesforce doesn’t guarantee general availability of this feature within any particular time frame or at all, and we can discontinue it at any time. This feature is for evaluation purposes only, not for production use. It’s offered as is and isn’t supported, and Salesforce has no liability for any harm or damage arising out of or in connection with it. All restrictions, Salesforce reservation of rights, obligations concerning the Services, and terms for related Non-Salesforce Applications and Content apply equally to your use of this feature.

This is an example of the cURL request with parameters.

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "document=Marc Benioff, the CEO of Salesforce, gave the keynote speech at the conference in Paris last week." https://api.einstein.ai/v2/language/entities

Keep the following points in mind:

• Response Sort Order—The entities returned in the response are sorted by the span start value. This is the start position in the document parameter string of the words the the API recognizes as an entity.

Pass Request Parameters as JSON

When you call the NER endpoint, you can specify a Content-Type of application/json and pass the request parameters as JSON.

{
    "modelId": "ENTITIES",
    "language": "en_US",
    "document": "Marc Benioff, the CEO of Salesforce, gave the keynote speech at the conference in Paris last week."
}

The cURL call looks like this.

Windows

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Content-Type: application/json" -d "{\"modelId\":\"ENTITIES\", \"language\": \"en_US\", \"document\":\"Marc Benioff, the CEO of Salesforce, gave the keynote speech at the conference in Paris last week.\"}" https://api.einstein.ai/v2/language/entities

Linux

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Content-Type: application/json" -d '{"modelId":"ENTITIES", "language": "en_US", "document":"Marc Benioff, the CEO of Salesforce, gave the keynote speech at the conference in Paris last week."}' https://api.einstein.ai/v2/language/entities

Returns an intent prediction for the given string.

Request Parameters

This is an example of the cURL request with parameters.

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "modelId=WEQ6PHPBGFYVX5C7QDP6XU3NXY" -F "document=what is the weather in los angeles" https://api.einstein.ai/v2/language/intent

Keep the following points in mind when sending text in for prediction:

• If you omit the numResults parameter, the response returns probabilities for all the labels in the model.

Passing Parameters as JSON

As an alternative to passing the modelId and document parameters as multipart/form-data fields, you can pass those parameters as JSON. You can pass the JSON directly as a string or reference a file that contains the JSON.

This cURL call sends a JSON string into a case routing model for classification. The Content-Type has changed to application/json.

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: application/json" -d "{\"modelId\":\"2ELVAO5BNVGZLBHMYTV7CGD5AY\",\"document\":\"my password stopped working\"}" https://api.einstein.ai/v2/language/intent

Here's what the response might look like.

{
    "probabilities": [
        {
            "label": "Password Help",
            "probability": 0.99040705
        },
        {
            "label": "Order Change",
            "probability": 0.003532466
        },
        {
            "label": "Shipping Info",
            "probability": 0.003473858
        },
        {
            "label": "Billing",
            "probability": 0.0024010758
        },
        {
            "label": "Sales Opportunity",
            "probability": 0.00018560764
        }
    ],
    "object": "predictresponse"
}

You can store the JSON in a file and reference that file. For example, the file intent.json might look like the following.

{
    "modelId": "2ELVAO5BNVGZLBHMYTV7CGD5AY",
    "document": "my password stopped working"
}

This cURL call references that file.

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: application/json" -d @c:\data\intent.json https://api.einstein.ai/v2/language/intent

Rate Limit Headers

Any time you make an API call to the /intent resource, your rate limit information is returned in the header. The rate limit headers specify your prediction usage for the current calendar month only.

X-RateLimit-Limit 2000
X-RateLimit-Remaining 1997
X-RateLimit-Reset 2017-04-01 19:31:42.0

Returns a sentiment prediction for the given string.

Request Parameters

This is an example of the cURL request with parameters.

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: multipart/form-data" -F "modelId=WJH4YCA7YX4PCWVNCYNWYHBMY4" -F "document=I can't tell you how much fun it was" https://api.einstein.ai/v2/language/sentiment

Keep the following points in mind when sending text in for prediction:

• If you omit the numResults parameter, the response returns probabilities for all the labels in the model.

Passing Parameters as JSON

As an alternative to passing the modelId and document parameters as multipart/form-data fields, you can pass those parameters as JSON. You can pass the JSON directly as a string or reference a file that contains the JSON.

This cURL call sends a JSON string into the prebuilt sentiment model for classification. The Content-Type has changed to application/json.

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: application/json" -d "{\"modelId\":\"CommunitySentiment\",\"document\":\"The presentation was great and I learned a lot\"}" https://api.einstein.ai/v2/language/sentiment

Here's what the response might look like.

{
    "probabilities": [
        {
            "label": "positive",
            "probability": 0.8673582
        },
        {
            "label": "negative",
            "probability": 0.1316828
        },
        {
            "label": "neutral",
            "probability": 0.0009590242
        }
    ],
    "object": "predictresponse"
}

You can store the JSON in a file and reference that file. For example, the file sentiment.json might look like the following.

{
    "modelId": "CommunitySentiment",
    "document": "The presentation was great and I learned a lot"
}

This cURL call references that file.

curl -X POST -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" -H "Content-Type: application/json" -d @c:\data\sentiment.json https://api.einstein.ai/v2/language/sentiment

Rate Limit Headers

Any time you make an API call to the /sentiment resource, your rate limit information is returned in the header. The rate limit headers specify your prediction usage for the current calendar month only.

X-RateLimit-Limit 2000
X-RateLimit-Remaining 1997
X-RateLimit-Reset 2017-04-01 19:31:42.0

Returns all the examples for the specified label. Returns uploaded examples.

NOTE: This is a new endpoint. Use this updated endpoint after December 3, 2020 to get all examples for a label.

This is an example of the cURL request.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache" https://api.einstein.ai/v2/language/examples/1000108/label/16599

Page Through Examples

By default, this call returns 100 examples. If you want to page through all the examples returned by this call, use the offset and count query parameters. The numExamples value indicates how many examples have the specified label, so you can use this value to control the paging.

The following call shows how to page through examples using these query parameters. If you omit the count parameter or the count parameter is greater than 100, the API returns 100 examples. If you omit the offset parameter, paging starts at 0. When using multiple query parameters in a cURL call, be sure to enclose the endpoint in quotes.

curl -X GET -H "Authorization: Bearer <TOKEN>" -H "Cache-Control: no-cache"  "https://api.einstein.ai/v2/language/examples/7890/label/1234?offset=50&count=50"

Note: This call returns uploaded examples that have the specified label. This call doesn't support the source parameter.

Limits

Each Einstein Vision and Language account is limited to 30 calls per calendar month to Einstein Language endpoints that return examples.

This limit applies across all APIs that return examples. If you exceed this limit, you receive an error message. See Release Notes.

Returns a prediction from an OCR model for the specified image URL or local image file.

Returns a single dataset.

Deletes the specified dataset and associated labels and examples.

Returns all the examples for the specified dataset. By default, returns examples created by uploading them from a .zip file.