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[ai-form-recognizer] Rework samples for 3.1.0-beta.3 (#14689)
* [ai-form-recognizer] Migrate to samples v2 * Move test-assets to assets * Remove dirname based path computation * Backport fix for stronglyTypedRecognizedForm to samples/v3 * Fix aka.ms links for FR data types * Remove old typescript samples * Use newer invoice sample document * Rework samples and tag with metadata * Added package.json metadata * Fixed a tense inconsistency * Disable generation of docs.ms metadata until GA. * Fixed links in sample READMEs * [min/max] Changed asset path from test-assets to assets * addressed feedback
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103
sdk/formrecognizer/ai-form-recognizer/samples-dev/authenticationMethods.ts
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|---|---|---|
| @@ -0,0 +1,103 @@ | ||
| // Copyright (c) Microsoft Corporation. | ||
| // Licensed under the MIT License. | ||
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| /** | ||
| * This sample demonstrates how to use either an Azure Active Directory (RBAC) | ||
| * or an API Key to authenticate a FormRecognizerClient. | ||
| * | ||
| * @summary authenticate a service client using both Azure Active Directory | ||
| * and an API key | ||
| * @azsdk-weight 50 | ||
| */ | ||
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| // To use an API Key, import `AzureKeyCredential` from the Form Recognizer package | ||
| import { FormRecognizerClient, AzureKeyCredential } from "@azure/ai-form-recognizer"; | ||
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| // To use Azure AD, import `DefaultAzureCredential` from the `@azure/identity` package | ||
| import { DefaultAzureCredential } from "@azure/identity"; | ||
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| import * as fs from "fs"; | ||
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| // Load the .env file if it exists | ||
| import * as dotenv from "dotenv"; | ||
| dotenv.config(); | ||
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| // You will need to set this environment variables or edit the following values | ||
| const endpoint = process.env["FORM_RECOGNIZER_ENDPOINT"] ?? "<cognitive services endpoint>"; | ||
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| /** | ||
| * Create a client using Azure Active Directory to authenticate | ||
| */ | ||
| async function useAad() { | ||
| console.log("-- Azure Active Directory --"); | ||
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| // DefaultAzureCredential expects the following three environment variables: | ||
| // - AZURE_TENANT_ID: The tenant ID in Azure Active Directory | ||
| // - AZURE_CLIENT_ID: The application (client) ID registered in the AAD tenant | ||
| // - AZURE_CLIENT_SECRET: The client secret for the registered application | ||
| const credential = new DefaultAzureCredential(); | ||
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| const client = new FormRecognizerClient(endpoint, credential); | ||
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| await recognizeContentWithClient(client); | ||
| } | ||
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| /** | ||
| * Create a client using an API Key (Cognitive Services Subscription Key) to authenticate. | ||
| */ | ||
| async function useApiKey() { | ||
| console.log("-- API Key --"); | ||
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| // If using an API Key, you will need to set this environment variable | ||
| const apiKey = process.env["FORM_RECOGNIZER_API_KEY"] ?? "<api key>"; | ||
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| const credential = new AzureKeyCredential(apiKey); | ||
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| const client = new FormRecognizerClient(endpoint, credential); | ||
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| await recognizeContentWithClient(client); | ||
| } | ||
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| /** | ||
| * This function will use the client generated by `useApiKey` and `useAad` | ||
| * for content recognition and print its output. | ||
| */ | ||
| async function recognizeContentWithClient(client: FormRecognizerClient) { | ||
| const fileName = "./assets/forms/Form_1.jpg"; | ||
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| if (!fs.existsSync(fileName)) { | ||
| throw new Error(`Expecting file ${fileName} exists`); | ||
| } | ||
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| const readStream = fs.createReadStream(fileName); | ||
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| const poller = await client.beginRecognizeContent(readStream); | ||
| const pages = await poller.pollUntilDone(); | ||
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| if (!pages || pages.length === 0) { | ||
| throw new Error("Expected to receive a non-empty list of pages!"); | ||
| } | ||
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| for (const page of pages!) { | ||
| console.log( | ||
| `Page ${page.pageNumber}: width="${page.width}", and height="${page.height}" (unit: ${page.unit})` | ||
| ); | ||
| for (const table of page.tables!) { | ||
| for (const cell of table.cells) { | ||
| console.log(`cell [${cell.rowIndex},${cell.columnIndex}] has text "${cell.text}"`); | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| export async function main() { | ||
| console.log("== Client Authentication Methods Sample =="); | ||
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| await useAad(); | ||
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| await useApiKey(); | ||
| } | ||
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| main().catch((err) => { | ||
| console.error("The sample encountered an error:", err); | ||
| }); |
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sdk/formrecognizer/ai-form-recognizer/samples-dev/copyModel.ts
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| // Copyright (c) Microsoft Corporation. | ||
| // Licensed under the MIT License. | ||
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| /** | ||
| * This sample demonstrates how to copy a model from a source Form Recognizer resource | ||
| * to a target Form Recognizer resource. | ||
| * | ||
| * @summary copy a custom model from one Form Recognizer resource to another | ||
| * @azsdk-weight 60 | ||
| */ | ||
|
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| import { FormTrainingClient, AzureKeyCredential } from "@azure/ai-form-recognizer"; | ||
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| // Load the .env file if it exists | ||
| import * as dotenv from "dotenv"; | ||
| dotenv.config(); | ||
|
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| export async function main() { | ||
| // You will need to set these environment variables or edit the following values | ||
| // information about the source Form Recognizer resource | ||
| const endpoint = | ||
| process.env["FORM_RECOGNIZER_SOURCE_ENDPOINT"] ?? "<source cognitive services endpoint>"; | ||
| const apiKey = process.env["FORM_RECOGNIZER_SOURCE_API_KEY"] ?? "<source resource api key>"; | ||
| const sourceModelId = | ||
| process.env["FORM_RECOGNIZER_SOURCE_MODEL_ID"] ?? "<source custom model id>"; | ||
| // information about the target Form Recognizer resource | ||
| const targetEndpoint = | ||
| process.env["FORM_RECOGNIZER_TARGET_ENDPOINT"] ?? "<target cognitive services endpoint>"; | ||
| const targetApiKey = process.env["FORM_RECOGNIZER_TARGET_API_KEY"] ?? "<target resource api key>"; | ||
| const targetResourceRegion = | ||
| process.env["FORM_RECOGNIZER_TARGET_REGION"] ?? "<target resource region>"; | ||
| const targetResourceId = | ||
| process.env["FORM_RECOGNIZER_TARGET_RESOURCE_ID"] ?? "<target resource resource id>"; | ||
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| const targetClient = new FormTrainingClient(targetEndpoint, new AzureKeyCredential(targetApiKey)); | ||
| const authorization = await targetClient.getCopyAuthorization( | ||
| targetResourceId, | ||
| targetResourceRegion | ||
| ); | ||
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| const sourceClient = new FormTrainingClient(endpoint, new AzureKeyCredential(apiKey)); | ||
| const poller = await sourceClient.beginCopyModel(sourceModelId, authorization, { | ||
| onProgress: (state) => { | ||
| console.log(`Copy model status: ${state.status}`); | ||
| } | ||
| }); | ||
| const result = await poller.pollUntilDone(); | ||
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| // now verify that the copy in the target Form Recognizer resource | ||
| console.log(`Model ID: ${result.modelId}`); | ||
| console.log(`Status : ${result.status}`); | ||
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| const model = await targetClient.getCustomModel(result.modelId); | ||
| console.log(model); | ||
| } | ||
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| main().catch((err) => { | ||
| console.error("The sample encountered an error:", err); | ||
| }); |
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sdk/formrecognizer/ai-form-recognizer/samples-dev/createComposedModel.ts
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| // Copyright (c) Microsoft Corporation. | ||
| // Licensed under the MIT License. | ||
|
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| /** | ||
| * This sample demonstrates how to create a composed model from several | ||
| * individual labeled models. | ||
| * | ||
| * We train all of the models used in the compose and then finally create | ||
| * the composed model. | ||
| * | ||
| * NOTE: Only models trained using labels can be composed. Attempting to | ||
| * compose an unlabeled model will result in an error. | ||
| * | ||
| * @summary create a composed model from several individual labeled models | ||
| * @azsdk-weight 60 | ||
| */ | ||
|
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| import { FormTrainingClient, AzureKeyCredential } from "@azure/ai-form-recognizer"; | ||
|
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| // Load the .env file if it exists | ||
| import * as dotenv from "dotenv"; | ||
| dotenv.config(); | ||
|
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| export async function main() { | ||
| // You will need to set these environment variables or edit the following values | ||
| const endpoint = process.env["FORM_RECOGNIZER_ENDPOINT"] ?? "<cognitive services endpoint>"; | ||
| const apiKey = process.env["FORM_RECOGNIZER_API_KEY"] ?? "<api key>"; | ||
|
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| // This object will hold the SAS-encoded URLs to containers that hold | ||
| // different types of purchase order documents and their labels. | ||
| const purchaseOrderSasUrls = { | ||
| supplies: | ||
| process.env["PURCHASE_ORDER_OFFICE_SUPPLIES_SAS_URL"] ?? | ||
| "<sas url to container with purchase orders for supplies>", | ||
| equipment: | ||
| process.env["PURCHASE_ORDER_OFFICE_SUPPLIES_SAS_URL"] ?? | ||
| "<sas url to container with purchase orders for equipment>", | ||
| furniture: | ||
| process.env["PURCHASE_ORDER_OFFICE_SUPPLIES_SAS_URL"] ?? | ||
| "<sas url to container with purchase orders for furniture>", | ||
| cleaningSupplies: | ||
| process.env["PURCHASE_ORDER_OFFICE_SUPPLIES_SAS_URL"] ?? | ||
| "<sas url to container with purchase orders for cleaning supplies>" | ||
| }; | ||
|
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| // Train all of the individual models and extract their model IDs | ||
| const trainingClient = new FormTrainingClient(endpoint, new AzureKeyCredential(apiKey)); | ||
|
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| const modelIds = await Promise.all( | ||
| Object.entries(purchaseOrderSasUrls) | ||
| .map(async ([kind, sasUrl]) => { | ||
| const poller = await trainingClient.beginTraining(sasUrl, true, { | ||
| onProgress(state) { | ||
| console.log(`training model "${kind}": ${state.status}`); | ||
| }, | ||
| modelName: kind | ||
| }); | ||
|
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| return poller.pollUntilDone(); | ||
| }) | ||
| .map(async (model) => (await model).modelId) | ||
| ); | ||
|
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| // Finally, create the composed model. | ||
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| const poller = await trainingClient.beginCreateComposedModel(modelIds, { | ||
| onProgress(state) { | ||
| console.log(`composing model "purchase_order": ${state.status}`); | ||
| }, | ||
| modelName: "purchase_order" | ||
| }); | ||
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| const composedModel = await poller.pollUntilDone(); | ||
|
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| // Print the model info to console | ||
| console.log(`Composed model: ${composedModel.modelName} (${composedModel.modelId}`); | ||
|
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| console.log("Properties:", composedModel.properties); | ||
|
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| // Errors | ||
| if (composedModel.errors && composedModel.errors.length > 0) { | ||
| console.log("Model Errors:"); | ||
| for (const error of composedModel.errors) { | ||
| console.log(`- [${error.code}] ${error.message}`); | ||
| } | ||
| } | ||
|
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| // Submodels | ||
| console.log("Submodels:"); | ||
| for (const model of composedModel.submodels ?? []) { | ||
| console.log(`- ${model.formType} (${model.modelId}) - ${model.accuracy} accuracy`); | ||
| console.log(" Fields:"); | ||
| for (const [name, field] of Object.entries(model.fields)) { | ||
| console.log(` - ${name} (${field.accuracy} accuracy)`); | ||
| } | ||
| } | ||
|
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| // Training Documents | ||
| console.log("Training Documents:"); | ||
| for (const info of composedModel.trainingDocuments ?? []) { | ||
| console.log(`- ${info.name} (in ${info.modelId})`); | ||
| } | ||
| } | ||
|
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| main().catch((err) => { | ||
| console.error("The sample encountered an error:", err); | ||
| }); |
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