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DataWeave is the MuleSoft programming language for data transformation and for defining expressions. Use DataWeave to process Mule event data, such as `payload`, `attributes`, and `vars`, in connector operations and other components. Develop DataWeave expressions and transformations in your Mule applications using the *Expression Field*, *Expression Builder*, or *Transformation Builder*, or by editing the configuration XML. Generate DataWeave transformations with AI help by providing input and output sample data, metadata, or both.
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DataWeave is the MuleSoft programming language for data transformation and for defining expressions. Use DataWeave to process Mule event data, such as `payload`, `attributes`, and `vars`, in connector operations and other components. Develop DataWeave expressions and transformations in your Mule applications using the *Expression Field*, *Expression Builder*, or *Transformation Builder*, or by editing the configuration XML. Generate DataWeave transformations with AI help by providing input and output sample data, metadata, or both. AI-powered auto mapping provides comprehensive error feedback for GenAI actions, including authorization, metadata, and request limits, ensuring a more reliable and guided mapping experience.
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image::int-dw-transform-ui.png["A configuration panel showing three numbered callouts: 1) fx button for Expression Field, 2) Expression Builder button, and 3) Transformation Builder button"]
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@@ -105,7 +105,7 @@ image:int-dw-fx-data-tab.png["Data tab for expression field"]
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image:int-dw-fx-data-tab-attributes.png["Data tab for expression field with mock attributes"]
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|===
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Automatically generated strings are displayed as mock values and are used to generate output for previews, such as in the <<tab-preview, Preview>> tab. The attribute metadata keys in the example come from the configuration of an `HTTP Listener` component in the flow.
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The IDE shows the automatically generated strings as mock values and uses them to generate output for previews, such as in the <<tab-preview, Preview>> tab. The attribute metadata keys in the example come from the configuration of an `HTTP Listener` component in the flow.
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The *Input/Output* tab shows the structure of data entering (*Input*) and exiting (*Output*) components. For example:
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For guidance, see <<open-fx-field>>.
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. List available functions:
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* To show a list of DataWeave functions from the Core module from an empty *fx* field, press Ctrl+Space in an _empty_ *fx* field. For example:
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* To show a list of DataWeave functions from the `Core` module from an empty *fx* field, press Ctrl+Space in an _empty_ *fx* field. For example:
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image::int-dw-fx-field-autocomplete.png["DataWeave autocomplete dropdown menu showing available Core module functions like abs, avg, ceil, and floor with their descriptions"]
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* To show functions from all xref:dataweave::dw-functions.adoc#dw_modules[DataWeave modules], such as `String`, `Array`, and `Core` modules:
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* To generate a DataWeave script using *Map and Transform with AI*, provide input and output samples. While optional, providing metadata helps AI understand the data structure and improves the accuracy of the transformation.
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* Sample data provides the context to generate functions or expressions for calculations, conditions, and other operations. You can provide Web Services Description Language (WSDL) files with sample data. See <<sample-data>> for supported data formats (MIME types) for sample data.
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* For automatic field-to-field mapping using *Map with AI*, provide both input and output metadata. *Map with AI* works only if no DataWeave script exists in the component.
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* For field-to-field mapping using *Map with AI*, provide both input and output metadata. Use *Map with AI* even when a DataWeave script is already present in the component. Click *Map with AI* to trigger it.
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* AI requires valid data to create the transformation script. Write it manually if needed.
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* AI chat history isn't available in *Transformation Builder*.
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* Avoid using real personally identifiable information (PII) in sample data or AI inputs. Provide input and output sample data that contains only fictional or masked data.
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* AI-assisted transformations provide comprehensive error feedback for GenAI actions. If you encounter errors related to authorization, metadata, or request limits, the error messages guide you to resolve the issue. See <<troubleshoot-ai-assisted-transformations>> for detailed troubleshooting steps.
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[[map-with-ai]]
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=== Map with AI
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The *Map with AI* feature automatically suggests a field-to-field mapping. This feature requires you to provide both input and output metadata for the component and triggers only when no DataWeave script is present. It performs auto-mapping based on the provided metadata.
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The *Map with AI* feature suggests a field-to-field mapping using AI-powered auto mapping. Click *Map with AI* to trigger it. Use it even when a DataWeave script is already present in the component. Provide both input and output metadata for the component. Mapping is performed based on the metadata you provide.
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*Map with AI* provides comprehensive error feedback for GenAI actions, including authorization errors, metadata issues, and request limits. If you encounter errors during the mapping process, see <<troubleshoot-ai-assisted-transformations>> for detailed guidance on resolving specific error types.
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If you don't have access to *Map with AI*, you must:
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image::int-map-with-ai.png["Transformation Builder showing 'Fetching metadata' status message with loading indicator for AI-assisted mapping functionality"]
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The mapping starts automatically considering the sample data provided.
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The mapping is applied to the XML file.
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. Click *Map with AI* to start the mapping. Provide both input and output metadata if not already provided.
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The *Preview* tab shows the output of the transformation.
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The mapping is applied to the XML file, and the *Preview* tab shows the output of the transformation.
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. After the mapping is complete, you must:
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.. Click *Apply Mapping* to apply suggested mapping to your project.
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.. Click *Revise Mapping* to get new suggestions from the AI.
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[[troubleshoot-ai-assisted-transformations]]
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=== Troubleshoot AI-Assisted Transformations
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If you have issues with the AI-assisted transformations, follow these steps:
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AI-assisted transformations in *Transformation Builder* provide comprehensive error feedback to help you resolve issues. Error messages indicate the type of error and provide guidance to resolve it.
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If you encounter errors when using AI-assisted transformations, identify the error type and follow the corresponding resolution steps:
* <<troubleshoot-general-ai-errors, General AI Errors>>
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[[troubleshoot-authorization-errors]]
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==== Authorization Errors
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Authorization errors occur when your Anypoint Platform account lacks the required permissions or when Einstein generative AI isn't properly configured for your organization.
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If you receive an authorization error:
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. Verify that you are logged in to Anypoint Platform from Anypoint Code Builder.
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For more information, see xref:start-acb.adoc#log-in-to-anypoint-platform-from-the-ide[Log in to Anypoint Platform from the IDE].
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. Contact your Anypoint Platform organization administrator to verify:
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* You have the *Mule Developer Generative AI User* permission assigned in Anypoint Platform access management.
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* Einstein generative AI is enabled for your organization. For more information, see xref:access-management::enabling-einstein.adoc[Enabling Einstein for Anypoint Platform].
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* You have accepted the terms and conditions for using Einstein generative AI.
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* Your Anypoint Platform organization is current and active.
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. If you are the organization administrator, verify that:
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* The Salesforce organization connected for requests has generative AI enabled.
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* The Salesforce organization has an xref:access-management::trusted-salesforce-org.adoc[established tenant relationship] with your Anypoint Platform organization, and the connection is enabled.
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* The Salesforce organization is valid and not expired or disabled.
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[[troubleshoot-metadata-errors]]
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==== Metadata Errors
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Metadata errors occur when the input or output metadata provided to *Map with AI* or *Map and Transform with AI* is invalid, missing, or improperly structured.
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If you receive a metadata error:
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. Verify that both input and output metadata are provided when using *Map with AI*.
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*Map with AI* requires both input and output metadata to perform automatic field-to-field mapping.
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. Check that your metadata structure is valid:
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* Ensure the metadata follows the expected schema format.
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* Verify that required fields are present in the metadata.
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* Confirm that data types match between input and output metadata where applicable.
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. Review the metadata source:
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* If metadata is generated from sample data, verify that the sample data structure is correct.
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* If metadata is imported from an API specification, verify the specification is valid and complete.
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* Refresh metadata by clicking *More Actions* in *Transformation Builder* and selecting *Refresh metadata*.
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. For *Map and Transform with AI*, verify that:
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* Input sample data is provided and valid.
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* Output sample data is provided and valid.
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* Sample data formats match the expected MIME types. See <<sample-data>> for supported formats.
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[[troubleshoot-request-limit-errors]]
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==== Request Limit Errors
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Request limit errors occur when you exceed the rate limits for GenAI API requests. These limits help ensure fair usage and system stability.
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If you receive a request limit error:
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. Wait before retrying your request:
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* The error message indicates when you can retry the request.
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* Avoid making multiple rapid requests in succession.
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. Reduce the frequency of AI-assisted transformation requests:
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* Batch multiple transformations when possible.
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* Use manual mapping for simple transformations to reduce API calls.
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. Contact your Anypoint Platform organization administrator if:
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* Request limits are consistently reached during normal usage.
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* You must understand your organization's specific rate limits.
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. Consider alternative approaches:
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* Use *Manually Adding Data Sources and Targets* for straightforward mappings. See <<manually-add-data-sources-targets>>.
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* Write DataWeave scripts manually for complex transformations that don't require AI assistance.
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[[troubleshoot-general-ai-errors]]
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==== General AI Errors
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If you encounter other errors with AI-assisted transformations:
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. Log in to Anypoint Platform and try again.
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. Ask your Anypoint Platform admin to verify that generative AI is enabled for your organization.
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. If you're logged in to Anypoint Platform, and your organization has access to Einstein generative AI, accept the terms and conditions for using Einstein generative AI.
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. Verify that your Anypoint Platform organization has access to Einstein generative AI.
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. Ensure you have accepted the terms and conditions for using Einstein generative AI.
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. Check your network connection and try again.
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. If the error persists, contact your Anypoint Platform organization administrator or MuleSoft support.
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