• Capabilities
• Prerequisites
• Invoke Oracle Cloud Infrastructure Document Understanding from an Integration

You can optionally store and access the invoices, receipts, passports, and drivers licenses from which to exact data in Oracle Cloud Infrastructure Object Storage. See Object Storage.

Capabilities

Oracle Cloud Infrastructure Document Understanding is an AI service that enables you to extract text, tables, and other key data from PDF, PNG, and JPEG files through APIs and command line interface tools. Oracle Cloud Infrastructure Document Understanding automates repetitive business processing tasks with prebuilt AI models and customizes document extraction to satisfy your industry-specific needs. Oracle Integration supports using Oracle Cloud Infrastructure Document Understanding in an integration with the document understanding action.

See AI Document Understanding.

Prerequisites

See Prerequisites for information on the prerequisites you must satisfy in the Oracle Cloud Infrastructure Console.

Invoke Oracle Cloud Infrastructure Document Understanding from an Integration

1. Add an OCI Document Understanding action to an integration in either of the following ways:
   • On the side of the canvas, click Actions and drag the OCI Document Understanding action to the appropriate location.
   • Click at the location where you want to add the document understanding action, then select OCI Document Understanding.
2. Enter a name and optional description.

   For the Select Categories list, Pre-trained models is the only option available and cannot be deselected.

3. Select an action to perform, then click Continue.

   Action	Description	See...
   Analyze document	Extracts information from a document. This action accepts an inline document in base64 format or retrieves the document from object storage, performs the extraction, and returns an immediate response.	Step 4 to configure this selection in the wizard.
   Document classification	Classifies and returns the type of document. For example, if you send an invoice, the document type returned is an invoice. This action accepts an inline document in base64 format or retrieves the document from object storage and returns the document type in an immediate response.	Step 5 to configure this selection in the wizard.
   Create a processor job	Creates a processor job. This action reads a file from object storage and provides a response with a processor job ID.	Step 6 to configure this selection in the wizard.
   Get a processor job status	Gets the status of the processor job. This action takes the processor job ID as a parameter from the Create a processor job action and returns the status of the processor job.	Step 7. The Summary page is displayed and no further wizard configuration is required.
   Cancel a processor job	Cancels the processor job submitted through a Create a processor job action.	Step 7. The Summary page is displayed and no further wizard configuration is required.

4. If you selected the Analyze document action:
   1. Select the following information, then click Continue.

      Element	Description
      Compartment Name	Select the Oracle Cloud Infrastructure compartment in which Oracle Integration is installed.
      Document Type	Select the type of document from which to extract information. Key value extraction can identify values for predefined keys in a document. Invoice: Identifies values for predefined keys in an invoice. For example, if an invoice includes a vendor name, total, and invoice ID, Oracle Cloud Infrastructure Document Understanding can identify these values and return them as a key-value pair. Receipt: Identifies values for predefined keys in a receipt. For example, if a receipt includes a merchant name, merchant address, or merchant phone number, Oracle Cloud Infrastructure Document Understanding can identify these values and return them as a key-value pair. Driver License: Identifies values for predefined keys in a US or UK driver's documentation. For example, if a driver ID includes an issue date, region, and expiry date, Oracle Cloud Infrastructure Document Understanding can identify these values and return them as a key-value pair. Passport: Identifies values for predefined keys in an MRZ-supported passport. For example, if a passport includes nationality and date of issue, Oracle Cloud Infrastructure Document Understanding can identify these values and return them as a key-value pair. Your selection is passed into the mapper for you to map.
      Input source	Select the source location from which to get the input document. Inline: Accepts the document through base64 format in the request payload of the mapper. Object storage: Reads the document from the object storage bucket. Input storage bucket: Select the storage bucket from which to read the file. Namespace: Select the namespace for the object storage bucket. After completing configuration in the wizard, you must specify the file name to read from the object storage bucket in the mapper.

5. If you selected the Document classification action:
   1. Select the following information, then click Continue.

      Element	Description
      Compartment Name	Select the Oracle Cloud Infrastructure compartment in which Oracle Integration is installed.
      Input source	Select the source location from which to get the input document. Inline: Accepts the document through base64 format in the request payload of the mapper. Object storage: Reads the document from the object storage bucket and returns the response based on the selected document type. Input storage bucket: Select the storage bucket from which to read the file. Namespace: Select the namespace for the object storage bucket. After completing configuration in the wizard, you must specify the file name to read from the object storage bucket in the mapper.

6. If you selected the Create a processor job action:
   1. Select the following information, then click Continue.

      Element	Description
      Compartment Name	Select the Oracle Cloud Infrastructure compartment in which Oracle Integration is installed.
      Output storage bucket	Specify the output storage location for the file.
      Namespace	Select the namespace for the object storage bucket name.

7. On the Summary page, click Finish.
   Upon completing configuration in the wizard, you must perform specific tasks in the mapper based on the action and file source you configured.

   • Configure the Mapper to Read the Inline Document for the Analyze Document Action
   • Configure the Mapper to Read the Document from a Storage Bucket for the Analyze Document Action
   • Configure the Mapper to Read the Inline Document for the Document Classification Action
   • Configure the Mapper to Read the Document from a Storage Bucket for the Document Classification Action
   • Configure the Mapper for the Create a Processor Job Action
   • Configure the Mapper for the Get a Processor Job Status Action
   • Configure the Mapper for the Cancel a Processor Job Action

Configure the Mapper to Read the Inline Document for the Analyze Document Action

You send the data in base-64 format for any document type that you selected in the wizard.

1. Expand the target Document element.
2. Right-click Data, and select Create target node.
3. Click Functions .
4. Click Design View in the Expression Builder.
5. In the Functions section, expand Advanced, and drag encodeReferenceToBase64 into the Expression Builder. This step is required for all document types (invoice, receipt, drivers license, and passport).

   oraext:encodeReferenceToBase64 ( )

6. Drag Stream Reference from the Sources section into the Expression Builder.

   oraext:encodeReferenceToBase64 (/nssrcmpr:execute/ns19:streamReference )

   
   
   

7. Exit the mapper.

   The document understanding action is now configured.

Configure the Mapper to Read the Document from a Storage Bucket for the Analyze Document Action

You must select the file name to read from the object storage bucket in the mapper.

1. Drag the file name from the Sources section to the Object Name target element.

   If you also want to override other values you defined in the wizard, you can specify values for the Component id, Bucket Name, and Namespace Name target elements.

   
   
   

2. When complete, exit the mapper.

   The document understanding action is now configured.

Configure the Mapper to Read the Inline Document for the Document Classification Action

You send the data in base-64 format for any document type that you selected in the wizard for the Analyze document action.

1. Expand the target Document element.
2. Right-click Data, and select Create target node.
3. Click Functions .
4. Click Design View in the Expression Builder.
5. In the Functions section, expand Advanced, and drag encodeReferenceToBase64 into the Expression Builder.

   oraext:encodeReferenceToBase64 ( )

6. Drag Stream Reference from the Sources section into the Expression Builder.

   oraext:encodeReferenceToBase64 (/nssrcmpr:execute/nssrcmpr:attachments/ns19:attachment/ns20:attachmentReference )

   
   
   

7. When complete, exit the mapper.

   The document understanding action is now configured.

Configure the Mapper to Read the Document from a Storage Bucket for the Document Classification Action

You must select the file name to read from the object storage bucket in the mapper.

1. Drag the file name from the Sources section to the Target Object Name element.

   If you also want to override other values you defined in the wizard, you can specify values for the Component id, Bucket Name, and Namespace Name target elements.

   
   
   

2. When complete, exit the mapper.

   The document understanding action is now configured.

Configure the Mapper for the Create a Processor Job Action

This action returns a processor job ID. The input from the storage bucket is sent through the request mapper. The output is sent to the object storage bucket.

1. Expand the Target Request Wrapper to specify the target values. Most values are optional such as Display Name, Processor Config, and Document Type (for example, an invoice). However, the following elements are mandatory:
   1. Feature Type: Specify the document analysis type (for example, TEXT_EXTRACTION or others). For a complete list of available types, see DocumentFeature Reference.
   2. Bucket Name (under Input Location): Specify the input object storage bucket from which to get the file.
   3. Namespace (under Input Location): Specify the namespace for the input object storage bucket.
   4. Prefix (under Output Location): Specify the prefix. The folder in object storage is created with the prefix that you specify. If you also want to override values you defined in the wizard, you can specify values for the Bucket Name and Namespace Name target elements under Output Location.

   
   
   

   The response mapper returns a job ID (id element) and a job status (Percent Complete element).

   
   
   

2. Exit the response mapper.

   The document understanding action is now configured.

Configure the Mapper for the Get a Processor Job Status Action

Configure the Mapper for the Cancel a Processor Job Action

This action takes the job ID returned in the response mapper of the Create a Processor Job action. This action uses that value to cancel the processor job. You configure this use case as follows:

• Configure an initial OCI Document Understanding action with the Create a processor job action. This action returns the job ID.
• Configure a second OCI Document Understanding action with the Get a processor job status action. This action gets the status of the processor job.
• Configure a third OCI Document Understanding action with the Cancel a processor job action. This action cancels the processor job. The job status must be in progress.

1. Map the Sources Create A Processor Job id element to the Target Processor Job id element.

A design-time to runtime use case using the document understanding action is provided. See Extract Content from an Invoice PDF Document with a Document Understanding Action.

Watch a video to learn more:

• Capabilities
• Prerequisites
• Invoke Oracle Cloud Infrastructure Vision from an Integration

Capabilities

OCI Vision is an AI service that enables you to detect objects and text in images, classify images using labels, and detect faces in images. You can use OCI Vision to detect visual anomalies (for example, anomalies in manufacturing), identify objects in images to automate counting of common items (such as packages, products, shipments, vehicles, and so on), identify items that need attention (such as overgrowth of vegetation near an electric power line), classify items (for example, digital media assets) to organize them, identify textual/visual data in images to power analytic applications, and much more. Oracle Integration supports using OCI Vision in an integration with the vision action.

See AI Vision.

Prerequisites

See Prerequisites for information on the prerequisites you must satisfy in the Oracle Cloud Infrastructure Console.

Invoke Oracle Cloud Infrastructure Vision from an Integration

1. Add a Vision action to an integration in either of the following ways:
   • On the side of the canvas, click Actions and drag the OCI Vision action to the appropriate location.
   • Click at the location where you want to add the OCI Vision action, then select OCI Vision.
2. Enter a name and optional description, then click Continue.
3. Select the following information, then click Continue.

   Element	Description
   Compartment	Select the Oracle Cloud Infrastructure compartment in which your Oracle Integration is installed.
   Task	Select the task to perform. Label the image: Classifies objects in an image using labels. For example, if an image is a surveillance image of a street that has a vehicle, an electric power line, and a tree, OCI Vision can identify these objects, classify them using labels, and return this information. This might be helpful to identify items that need attention such as overgrowth of vegetation near an electric power line. Identify objects in the image: Identifies objects in an image. For example, if an image includes vehicles such as a car, a bus, or a truck, OCI Vision can identify these objects and return this information. Recognize text at the word and line level: Identifies words and lines of text in an image. For example, if an image is a visa application form and it contains text such as name, city, citizenship, country and other text content, OCI Vision can recognize the text and return this information. Identify faces in the image: Identifies faces and facial features of people in an image. For example, if an image is a photograph of a group of people, OCI Vision can identify the faces and facial features of the people in the image and return this information.
   Maximum no. of results to be returned	Select the maximum number of results to be returned after the images have been analyzed using the selected task.

4. On the Summary page, click Finish.
5. Open the mapper and define the mappings between the source and target elements as needed. You must send the data in base-64 format.

6. Drag Stream Reference from the Sources section into Data in the Target section.

7. Click Functions .
8. Click Design View in the Expression Builder.
9. In the Functions section, expand Advanced, and drag encodeReferenceToBase64 into the Expression Builder for the Data target element.

   oraext:encodeReferenceToBase64 (/nssrcmpr:execute/ns23:streamReference)

   Note:

   You can optionally specify Compartment Id in the mapper to override the value you selected initially for Compartment.
10. Exit the mapper.

    The vision action is now configured.

• Capabilities
• Prerequisites
• Invoke Oracle Cloud Infrastructure Generative AI from an Integration

Capabilities

OCI Generative AI is a fully managed AI service that offers customizable large language models (LLMs) for chat. You can prompt the OCI Generative AI chat model to generate text. Oracle Integration supports using OCI Generative AI in an integration with the generative AI action.

See Generative AI.

The OCI Generative AI’s chat model enables you to brainstorm ideas, get answers to questions, write descriptions or content, explain concepts, and much more.

Prerequisites

See Prerequisites for information on the prerequisites you must satisfy in the Oracle Cloud Infrastructure Console.

Invoke Oracle Cloud Infrastructure Generative AI from an Integration

1. Add a Generative AI action to an integration in either of the following ways:
   • On the side of the canvas, click Actions and drag the OCI Generative AI action to the appropriate location.
   • Click at the location where you want to add the OCI Generative AI action, then select OCI Generative AI.
2. Enter a name and optional description, then click Continue.
3. Select the following information, then click Continue.

   Element	Description
   Region	Select the region you want to use. Note: Ensure you select the region in which OCI is provisioned for you.
   Compartment	Select the Oracle Cloud Infrastructure compartment in which your Oracle Integration is installed.
   Model	Select a model for your chat conversation. If you select GENERIC (llama), all the Meta Llama models are listed for selection in the Model ID field. If you select COHERE, all the Cohere models are listed for selection in the Model ID field. In the Model ID field, select the model you want to use.

4. On the Summary page, click Finish.
5. Open the mapper and define the mappings between the source and target elements as needed.
6. Expand the topmost request node in the Target section.
7. Within the topmost request node in the Target section, expand Request Wrapper, expand Chat Request, expand Messages, and then expand Content.

8. Within Content, right-click Text, and select Create target node.

9. Click Design View in the Expression Builder.

10. You can prompt the OCI Generative AI chat model to generate text by specifying your request in the Expression Builder for the Text element. In the Expression Builder, enter the question you want to ask or specify what information you need (for example, 'Tell me about ELE').

    Note:

    In the mapper, you can optionally override the following values you initially selected when you performed Step 3:

    • Compartment Id: Use this to specify a compartment Id to override the default compartment that was selected for the model to be deployed.
    • Api Format: Use this to override the default API format used for model serving.
    • Model Id: Use this to override the initially selected model identifier.

    Currently, the generative AI action version supports the following servingType: ON_DEMAND.

    You can also configure the mappings for other fields as needed in the mapper, including optionally tuning the parameters such as Top p, Temperature, and so on.

11. Exit the mapper.

    The generative AI action is now configured.

• Capabilities
• Prerequisites
• Invoke Oracle Cloud Infrastructure Language from an Integration

Capabilities

OCI Language is a cloud-based AI service that enables you to build intelligent applications by using REST APIs and SDKs to process unstructured text for language detection, text classification, recognition of named entities, key phrase extraction, sentiment analysis, text translation, and detection of personal identifiable information. OCI Language can identify more than 100 languages in text. It also automatically recognizes at least 18 entity types, including the names of organizations and products. It makes text analysis easier for large volumes of text data. Oracle Integration supports using OCI Language in an integration with the language action.

See AI Language.

Prerequisites

See Prerequisites for information on the prerequisites you must satisfy in the Oracle Cloud Infrastructure Console.

Invoke Oracle Cloud Infrastructure Language from an Integration

1. Add a Language action to an integration in either of the following ways:
   • On the side of the canvas, click Actions and drag the OCI Language action to the appropriate location.
   • Click at the location where you want to add the OCI Language action, then select OCI Language.
2. Enter a name and optional description.
3. Select the action you want to perform.

   Element

   Description

   Action

   Language detection: Detects languages based on the text provided, and includes a confidence score. OCI Language detects the language and returns the detected language along with a related confidence score (between 0 and 1). You can also specify a batch of records. Named entity recognition: Identifies common entities, people, places, locations, email, and so on. OCI Language extracts the entities in the text records. It returns the type/subtype and confidence score (between 0 and 1) for each entity. Key phrase extraction: Extracts an important set of phrases from a block of text. OCI Language extracts the key phrases from the text. For each key phrase, it returns a score (between 0 and 1) that highlights the importance of the key phrase in the context of the text. Sentiment analysis: Identifies the tone of the text and classifies the expressions in the text into positive, negative, neutral, or mixed polarity. OCI Language supports both aspect-based and sentence-based sentiment analysis. For example, opinions, appraisals, emotions, or attitudes toward a topic, person, or entity. After the analysis, it returns a confidence score for each of the classes (positive, negative, neutral, or mixed). Personal identifiable Information (PII): Identifies, classifies, and de-identifies private information in unstructured text. OCI Language helps identify and classify personal identifiable information such as name, age, address, email, telephone number, and so on. It returns the information that was identified and classified. Text classification: Identifies the document category and subcategory that the text belongs to. OCI Language analyzes the text and automatically classifies it into a set of predetermined categories and sub-categories. It returns this information for each record that was classified. Text translation: Translates text into the language of your choice. OCI Language translates the text you provide from the source language to the specified language. It returns the translated text.

4. Click Continue.
5. Select the following information, then click Continue.

   Element	Description
   Compartment	Select the Oracle Cloud Infrastructure compartment in which your Oracle Integration is installed.

6. On the Summary page, click Finish.
7. Open the mapper and complete the configuration.
8. Expand the topmost node in the Target section.
9. Within that node, expand Request Wrapper, expand Body, and then expand Documents.

10. Right-click Key, and select Create target node.

11. Click Design View in the Expression Builder.
12. In the Expression Builder, specify a value for Key.
13. In the Target section, within Documents, right-click Text, and select Create target node.
14. In the Expression Builder, specify the text on which you want to perform the OCI Language action you selected in Step 3.

    Note:

    You can optionally specify Compartment Id in the mapper to override the value you selected initially for Compartment in Step 5.

    
    
    

    If you selected the Sentiment analysis action in Step 3, then set the Level target element to SENTENCE in the expression builder for sentence level validation.

    
    
    

    If you selected the Text translation action in Step 3, then you need to specify the source language code using the Language Code target element and the target language code using the Target Language Code target element. For example, to translate text from English to French, set Language Code to en and set Target Language Code to fr.

    
    
    

15. Exit the mapper.

    The language action is now configured.

• Capabilities
• Prerequisites
• Invoke Oracle Cloud Infrastructure Generative AI Agents RAG from an Integration

Capabilities

OCI Generative AI Agents RAG is an AI service that provides answers to your questions or requests based on your enterprise data through a natural language interface. OCI Generative AI Agents RAG provides a way to optimize the responses from an LLM without modifying the underlying model itself; the responses are based on the latest data and can be specific to a particular organization or industry. This enables the generative AI system to provide more contextually appropriate answers to your queries as well as base those answers on the current data. It lets you query diverse enterprise knowledge bases and receive up-to-date information. Oracle Integration supports using OCI Generative AI Agents RAG in an integration with the generative AI agents RAG action.

Prerequisites

See Prerequisites for information on the prerequisites you must satisfy in the Oracle Cloud Infrastructure Console.

Invoke Oracle Cloud Infrastructure Generative AI Agents RAG from an Integration

1. Add a Generative AI Agents RAG action to an integration in either of the following ways:
   • On the side of the canvas, click Actions and drag the OCI Generative AI Agents RAG action to the appropriate location.
   • Click at the location where you want to add the OCI Generative AI Agents RAG action, then select OCI Generative AI Agents RAG.
2. Enter a name and optional description.
3. Select the action you want to perform.

   Element

   Description

   Action

   List agent endpoints: Lists the endpoints that you can use. Create session: Creates a session with the Generative AI Agents RAG service using the ID (knowledge base Id) obtained by performing the List agent endpoints action. Chat: Enables the chat functionality using the ID (knowledge base Id) obtained by performing the List agent endpoints action and the session Id obtained by performing the Create session action.

4. Click Continue.
5. Select the following information, then click Continue.

   Element	Description
   Region	Select the region you want to use. Note: Ensure you select the region in which OCI is provisioned for you.
   Compartment	This field is displayed only when you select the List agent endpoints action. Select the Oracle Cloud Infrastructure compartment in which your Oracle Integration is installed. Note: Ensure you select a compartment where an AI agent is available.

6. On the Summary page, click Finish.

7. Open the mapper and complete the configuration for Create session and Chat actions.

   For the Create session action, perform the following steps:

   1. Expand the response node in the Sources section and map Id (that is within Response Wrapper) to Ociagent Id within the request node in the Target section.
   2. Specify values for Description and Display Name using the Expression Builder.

      
      
      

   For the Chat action, perform the following steps:

   1. Map the User Prompt element from the Sources section to the User Message element in the Target section.
   2. Map the Id element from the Sources section to the Session Id element in the Target section.
   3. Map the Ociagent Id element from the Sources section to the Ociagent Id element in the Target section.
   4. Set the Should Stream target element to false in the expression builder.
      
      
      
      
8. Exit the mapper.

   The generative AI agents RAG action is now configured.

   When you activate and run the integration, the AI agent provides a response to your request/question based on the knowledge base you had provided.

• Capabilities
• Prerequisites
• Invoke Oracle Cloud Infrastructure Speech from an Integration

Capabilities

OCI Speech is an AI service that can transcribe speech to text. OCI Speech harnesses the power of spoken language by enabling you to easily convert audio files containing human speech into highly exact text transcriptions. It uses automatic speech recognition (ASR) technology to provide a grammatically correct transcription. It can handle low-fidelity media recordings and transcribe challenging recordings such as meetings or call center calls. Oracle Integration supports using OCI Speech in an integration with the speech action.

Prerequisites

See Prerequisites for information on the prerequisites you must satisfy in the Oracle Cloud Infrastructure Console.

Invoke Oracle Cloud Infrastructure Speech from an Integration

1. Add a Speech action to an integration in either of the following ways:
   • On the side of the canvas, click Actions and drag the OCI Speech action to the appropriate location.
   • Click at the location where you want to add the OCI Speech action, then select OCI Speech.
2. Enter a name and optional description.
3. Select the action you want to perform. You can create, update, or delete a transcription job. You can also get information about a transcription job or list the transcription jobs that are available in a compartment.

   Element

   Description

   Action

   Select a transcription job to perform. Create transcription job: If you select this action, Oracle Integration will accept a request payload containing details about compartment id, model, input location, and output location to create a transcription job. Compartment id can also be specified in the Compartment field when performing Step 5. Get transcription job: If you select this action, Oracle Integration will accept the transcription job id (of the transcription job) as a path parameter to retrieve the transcription job. List transcription jobs: If you select this action, Oracle Integration will accept the compartment id (that contains the transcription jobs) as a query parameter to return a list of transcription jobs available in the compartment. It can also be specified in the Compartment field when performing Step 5. Update transcription job: If you select this action, Oracle Integration will accept the transcription job id (of the transcription job you want to update) as a path parameter and the request payload with details such as display name, description, and so on that need to be modified. This action updates the specified transcription job with the new details you provide. Delete transcription job: If you select this action, Oracle Integration will accept the transcription job id (of the transcription job you want to delete) as a path parameter. This action deletes the specified transcription job (but it will not delete the output transcription file that is stored in the output location bucket in the object store).

4. Click Continue.
5. Select the following information, then click Continue.

   Element	Description
   Compartment	This field is displayed only when you select Create transcription job or List transcription jobs action in Step 3. Select the Oracle Cloud Infrastructure compartment in which your Oracle Integration is installed.
   Output bucket	This field is displayed only when you select Create transcription job action in Step 3. Select a bucket to store the text output generated by the OCI Speech action.

6. On the Summary page, click Finish.

7. Open the mapper and define the mappings between the source and target elements as needed for the selected transcription job action.

   Note:

   You can optionally specify Compartment Id and Bucket Name in the mapper to override the value you selected initially for Compartment and Output Bucket respectively (in Step 5).
   1. Perform the following source-to-target mappings for the Create transcription job action:
      • Map the source Is Punctuation Enabled to the target Is Punctuation Enabled.
      • Map the source Compartment Id to the target Compartment Id.
      • Map the source Display Name to the target Display Name.
      • Map the source Description to the target Description.
      • Map the source Domain to the target Domain.
      • Map the source Language Code to the target Language Code.
      • Map the source Model Type to the target Model Type.
      • Map the source Is Diarization Enabled to the target Is Diarization Enabled.
      • Map the source Location Type to the target Location Type.
      • Map the source Object Locations to the target Object Locations.
      • Map the source Namespace Name to the target Namespace Name.
      • Map the source Bucket Name to the target Bucket Name.
      • Map the source Prefix to the target Prefix.

      
      
      

   2. Perform the following source-to-target mapping for the Get transcription job action:
      • Map the source Transcription Job Id to the target Transcription Job Id.

      
      
      

   3. Perform the following source-to-target mapping for the List transcription jobs action:
      • Map the source Compartment Id to the target Compartment Id.

      You can optionally configure target elements such as Lifecycle State, Display Name, Id, Limit, Page, Sort Order, and Sort By.

      
      
      

   4. Perform the following source-to-target mappings for the Update transcription job action:
      • Map the source Transcription Job Id to the target Transcription Job Id.
      • Map the source Display Name to the target Display Name.
      • Map the source Description to the target Description.

      
      
      

   5. Perform the following source-to-target mapping for the Delete transcription job action:
      • Map the source Transcription Job Id to the target Transcription Job Id.

      
      
      

8. Exit the mapper.

   The speech action is now configured.

   Here's what happens when you activate and run the integration based on the transcription job action you selected in Step 3:

   • Create transcription job: Converts the speech you provided to text and the text output is stored in the output bucket you selected.
   • Get transcription job: Retrieves the transcription job with the specified Id.
   • List transcription jobs: Returns a list of transcription jobs available in the specified compartment.
   • Update transcription job: Updates the specified transcription job with the new details you provided.
   • Delete transcription job: Deletes the specified transcription job (but it will not delete the output transcription file that is stored in the output location bucket in the object store).