Build a new model with a given ID from a set of input documents and labeled fields.
The Model ID can consist of any text, so long as it does not begin with "prebuilt-" (as these models refer to prebuilt Form Recognizer models that are common to all resources), and so long as it does not already exist within the resource.
The Form Recognizer service reads the training data set from an Azure Storage container, given as a URL to the container with a SAS token that allows the service backend to communicate with the container. At a minimum, the "read" and "list" permissions are required. In addition, the data in the given container must be organized according to a particular convention, which is documented in the service's documentation for building custom models.
const modelId = "aNewModel";
const containerUrl = "<training data container SAS URL>";
const poller = await client.beginBuildDocumentModel(modelId, containerUrl, {
// Optionally, a text description may be attached to the model
description: "This is an example model!"
});
// Model building, like all other model creation operations, returns a poller that eventually produces a ModelDetails
// object
const modelDetails = await poller.pollUntilDone();
const {
modelId, // identical to the modelId given when creating the model
description, // identical to the description given when creating the model
createdOn, // the Date (timestamp) that the model was created
docTypes // information about the document types in the model and their field schemas
} = modelDetails;
the unique ID of the model to create
SAS-encoded URL to an Azure Storage container holding the training data set
the mode to use when building the model (see DocumentModelBuildMode
)
optional settings for the model build operation
a long-running operation (poller) that will eventually produce the created model information or an error
Creates a single composed model from several pre-existing submodels.
The resulting composed model combines the document types of its component models, and inserts a classification step into the extraction pipeline to determine which of its component submodels is most appropriate for the given input.
const modelId = "aNewComposedModel";
const subModelIds = [
"documentType1Model",
"documentType2Model",
"documentType3Model"
];
// The resulting composed model can classify and extract data from documents
// conforming to any of the above document types
const poller = await client.beginComposeDocumentModel(modelId, subModelIds, {
description: "This is a composed model that can handle several document types."
});
// Model composition, like all other model creation operations, returns a poller that eventually produces a
// ModelDetails object
const modelDetails = await poller.pollUntilDone();
const {
modelId, // identical to the modelId given when creating the model
description, // identical to the description given when creating the model
createdOn, // the Date (timestamp) that the model was created
docTypes // information about the document types of the composed submodels
} = modelDetails;
the unique ID of the model to create
an Iterable of strings representing the unique model IDs of the models to compose
optional settings for model creation
a long-running operation (poller) that will eventually produce the created model information or an error
Copies a model with the given ID into the resource and model ID encoded by a given copy authorization.
See CopyAuthorization and getCopyAuthorization.
// We need a client for the source model's resource
const sourceEndpoint = "https://<source resource name>.cognitiveservices.azure.com";
const sourceCredential = new AzureKeyCredential("<source api key>");
const sourceClient = new DocumentModelAdministrationClient(sourceEndpoint, sourceCredential);
// We create the copy authorization using a client authenticated with the destination resource. Note that these two
// resources can be the same (you can copy a model to a new ID in the same resource).
const copyAuthorization = await client.getCopyAuthorization("<destination model ID>");
// Finally, use the _source_ client to copy the model and await the copy operation
const poller = await sourceClient.beginCopyModelTo("<source model ID>");
// Model copying, like all other model creation operations, returns a poller that eventually produces a ModelDetails
// object
const modelDetails = await poller.pollUntilDone();
const {
modelId, // identical to the modelId given when creating the copy authorization
description, // identical to the description given when creating the copy authorization
createdOn, // the Date (timestamp) that the model was created
docTypes // information about the document types of the model (identical to the original, source model)
} = modelDetails;
the unique ID of the source model that will be copied
an authorization to copy the model, created using the getCopyAuthorization
optional settings for
a long-running operation (poller) that will eventually produce the copied model information or an error
Deletes a model with the given ID from the client's resource, if it exists. This operation CANNOT be reverted.
await client.deleteModel("<model ID to delete>"));
the unique ID of the model to delete from the resource
optional settings for the request
Creates an authorization to copy a model into the resource, used with the beginCopyModelTo
method.
The CopyAuthorization
grants another cognitive service resource the right to create a model in this client's
resource with the model ID and optional description that are encoded into the authorization.
// The copyAuthorization data structure stored below grants any cognitive services resource the right to copy a
// model into the client's resource with the given destination model ID.
const copyAuthorization = await client.getCopyAuthorization("<destination model ID>");
the unique ID of the destination model (the ID to copy the model into)
optional settings for creating the copy authorization
a copy authorization that encodes the given modelId and optional description
Retrieves information about a model ({@link ModelDetails}) by ID.
This method can retrieve information about custom as well as prebuilt models.
In previous versions of the Form Recognizer REST API and SDK, the getModel
method could return any model, even
one that failed to create due to errors. In the new service versions, getDocumentModel
and listDocumentModels
only produce successfully created models (i.e. models that are "ready" for use). Failed models are now retrieved
through the "operations" APIs, see getOperation and listOperations.
// The ID of the prebuilt business card model
const modelId = "prebuilt-businessCard";
const {
modelId, // identical to the modelId given when calling `getDocumentModel`
description, // a textual description of the model, if provided during model creation
createdOn, // the Date (timestamp) that the model was created
// information about the document types in the model and their field schemas
docTypes: {
// the document type of the prebuilt business card model
"prebuilt:businesscard": {
// an optional, textual description of this document type
description,
// the schema of the fields in this document type, see the FieldSchema type
fieldSchema,
// the service's confidences in the fields (an object with field names as properties and numeric confidence
// values)
fieldConfidence
}
}
} = await client.getDocumentModel(modelId);
the unique ID of the model to query
optional settings for the request
information about the model with the given ID
Retrieves information about an operation (OperationDetails
) by its ID.
Operations represent non-analysis tasks, such as building, composing, or copying a model.
the ID of the operation to query
optional settings for the request
information about the operation with the given ID
// The ID of the operation, which should be a GUID
const operationId = "<operation GUID>";
const {
operationId, // identical to the operationId given when calling `getOperation`
kind, // the operation kind, one of "documentModelBuild", "documentModelCompose", or "documentModelCopyTo"
status, // the status of the operation, one of "notStarted", "running", "failed", "succeeded", or "canceled"
percentCompleted, // a number between 0 and 100 representing the progress of the operation
createdOn, // a Date object that reflects the time when the operation was started
lastUpdatedOn, // a Date object that reflects the time when the operation state was last modified
} = await client.getOperation(operationId);
Retrieve basic information about this client's resource.
const {
// Information about the custom models in the current resource
customDocumentModelDetails: {
// The number of custom models in the current resource
count,
// The maximum number of models that the current resource can support
limit
}
} = await client.getResourceDetails();
optional settings for the request
basic information about this client's resource
List summaries of models in the resource. Custom as well as prebuilt models will be included. This operation supports paging.
The model summary ({@link ModelSummary}) includes only the basic information about the model, and does not include information about the document types in the model (such as the field schemas and confidence values).
To access the full information about the model, use getDocumentModel.
In previous versions of the Form Recognizer REST API and SDK, the listModels
method would return all models, even
those that failed to create due to errors. In the new service versions, listDocumentModels
and getDocumentModel
only produce successfully created models (i.e. models that are "ready" for use). Failed models are now retrieved
through the "operations" APIs, see getOperation and listOperations.
for await (const summary of client.listDocumentModels()) {
const {
modelId, // The model's unique ID
description, // a textual description of the model, if provided during model creation
} = summary;
// You can get the full model info using `getDocumentModel`
const model = await client.getDocumentModel(modelId);
}
// The listDocumentModels method is paged, and you can iterate by page using the `byPage` method.
const pages = client.listDocumentModels().byPage();
for await (const page of pages) {
// Each page is an array of models and can be iterated synchronously
for (const model of page) {
const {
modelId, // The model's unique ID
description, // a textual description of the model, if provided during model creation
} = summary;
// You can get the full model info using `getDocumentModel`
const model = await client.getDocumentModel(modelId);
}
}
optional settings for the model requests
an async iterable of model summaries that supports paging
List model creation operations in the resource. This will produce all operations, including operations that failed to create models successfully. This operation supports paging.
for await (const operation of client.listOperations()) {
const {
operationId, // the operation's GUID
status, // the operation status, one of "notStarted", "running", "succeeded", "failed", or "canceled"
percentCompleted // the progress of the operation, from 0 to 100
} = operation;
}
// The listOperations method is paged, and you can iterate by page using the `byPage` method.
const pages = client.listOperations().byPage();
for await (const page of pages) {
// Each page is an array of operation info objects and can be iterated synchronously
for (const operation of page) {
const {
operationId, // the operation's GUID
status, // the operation status, one of "notStarted", "running", "succeeded", "failed", or "canceled"
percentCompleted // the progress of the operation, from 0 to 100
} = operation;
}
}
optional settings for the operation requests
an async iterable of operation information objects that supports paging
Generated using TypeDoc
A client for interacting with the Form Recognizer service's model management features, such as creating, reading, listing, deleting, and copying models.
Examples:
Azure Active Directory
import { DocumentModelAdministrationClient } from "@azure/ai-form-recognizer"; import { DefaultAzureCredential } from "@azure/identity"; const endpoint = "https://<resource name>.cognitiveservices.azure.com"; const credential = new DefaultAzureCredential(); const client = new DocumentModelAdministrationClient(endpoint, credential);
API Key (Subscription Key)
import { DocumentModelAdministrationClient, AzureKeyCredential } from "@azure/ai-form-recognizer"; const endpoint = "https://<resource name>.cognitiveservices.azure.com"; const credential = new AzureKeyCredential("<api key>"); const client = new DocumentModelAdministrationClient(endpoint, credential);