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docs/sap-ai-core/consume-generic-secrets-in-executions-or-deployments-185a324.md

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# Consume Generic Secrets in Executions or Deployments
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Generic secrets at resource-group level can be attached to containers in executions or deployments. They can either be mounted as a volume or attached as an environment variable. The following examples illustrate how to consume a generic secret in a container by declaring it in the template. Note that only generic secrets can be attached to containers in this way. System secrets cannot be consumed in a template.
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Generic secrets at resource-group level can be attached to containers in executions or deployments. They can either be mounted as a volume or attached as an environment variable. The following examples illustrate how to consume a generic secret in a container by declaring it in the template. Note that only generic secrets can be attached to containers in this way. System secrets can't be consumed in a template.
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<a name="loio185a3245692542a78bfeff87220410c6__section_mjm_b1l_4rb"/>
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## Consume a Generic Secret as an Environment Variable
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Generic secrets can be attached to containers using either `envFrom.secretRef` or `env.valueFrom.secretKeyRef`, as shown below.
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Generic secrets can be attached to containers using either `envFrom.secretRef` or `env.valueFrom.secretKeyRef`:
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- Using `envFrom.secretRef`:
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```
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spec:
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containers:
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- name: my-kserve-container
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image: centaur
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envFrom:
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- secretRef:
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name: MY_GENERIC_SECRET
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containers:
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- name: my-kserve-container
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image: centaur
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envFrom:
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- secretRef:
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name: MY_GENERIC_SECRET
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```
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If your secret contains invalid characters, such as hyphens \(-\), this method will result in error. You will need to map your secret to a valid variable name using `env.valueFrom.secretKeyRef`.
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If your secret contains invalid characters, such as hyphens \(-\), this method results in error. In this case, map your secret to a valid variable name using `env.valueFrom.secretKeyRef`.
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- Using `env.valueFrom.secretKeyRef`:
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```
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spec:
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containers:
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- name: kserve-container
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image: centaur
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env:
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- name: MY_GENERIC_SECRET
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valueFrom:
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secretKeyRef:
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name: my-generic-secret
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key: some-credential
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containers:
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- name: kserve-container
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image: centaur
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env:
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- name: MY_GENERIC_SECRET
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valueFrom:
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secretKeyRef:
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name: my-generic-secret
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key: some-credential
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```
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## Consume a Generic Secret as a Volume Mount
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Generic secrets can also be mounted to containers as volumes, as shown below.
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Generic secrets can also be mounted to containers as volumes:
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```
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spec:
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containers:
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- name: kserve-container
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image: centaur
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volumeMounts:
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- name: my-generic-secret
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mountPath: "/etc/my-generic-secret"
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readOnly: true
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volumes:
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- name: my-generic-secret
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secret:
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secretName: my-generic-secret
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containers:
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- name: kserve-container
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image: centaur
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volumeMounts:
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- name: my-generic-secret
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mountPath: "/etc/my-generic-secret"
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readOnly: true
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volumes:
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- name: my-generic-secret
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secret:
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secretName: my-generic-secret
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```
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## Additional Information
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Secret names can be included as parameters in the templates and supplied via AI API configurations, as shown below.
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Secret names can be included as parameters in the templates and supplied via AI API configurations:
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```
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envFrom:
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- secretRef:
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name: "{{inputs.parameters.secretName}}"
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name: "{{inputs.parameters.secretName}}"
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```
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docs/sap-ai-core/create-a-prompt-template-declarative-815def5.md

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- The `additional_fields` field is unstructured and can be used to store metadata or configuration objects.
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Your template syncs automatically. After a few minutes, you'll be able to verify your template by sending a GET request to the endpoint `{{apiurl}}/lm/promptTemplates`.
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Your template syncs automatically. After a few minutes, you'll be able to verify your template by sending a GET request to the endpoint `{{apiurl}}/v2/lm/promptTemplates`.
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> ### Sample Code:
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> ```

docs/sap-ai-core/create-a-prompt-template-imperative-92453a7.md

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## Context
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> ### Recommendation:
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> The imperative API supports full CRUD operations and is recommended for refining prompt templates in design time use cases. Iterations are tracked and can be viewed in the history endpoint.
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> The imperative API supports full CRUD operations and is recommended for refining prompt templates in design-time use cases. Iterations are tracked and can be viewed in the history endpoint.
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You can create a reusable prompt for a specific use case, including placeholders that are filled later.
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## Procedure
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Create a prompt template by sending a POST request to endpoint <code><code>{{apiurl}}/lm/promptTemplates</code></code>.
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Create a prompt template by sending a POST request to endpoint <code><code>{{apiurl}}/v2/lm/promptTemplates</code></code>.
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> ### Sample Code:
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> ```
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> curl -X POST "{{apiurl}}/lm/promptTemplates"\
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> curl -X POST "{{apiurl}}/v2/lm/promptTemplates"\
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> --data '{
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## Next Steps
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You can iterate over the prompt template, and make changes to it.
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You can iterate over the prompt template and make changes to it.
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You can save your changes as a new version. The latest iteration is always the head version and is marked with `isVersionHead: true`.
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> ### Tip:
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> You'll need to update the version number when you want to create a new version, and the version entry must be semver compliant.
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> You'll need to update the version number when you want to create a new version, and the version entry must be compliant with semantic versioning \(SemVer\).
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You can check the change history for the template. For more information, see [Get Prompt Template History](get-prompt-template-history-dc204cf.md).
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docs/sap-ai-core/delete-a-prompt-template-f7566e3.md

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## Procedure
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Send a DELETE request to the endpoint `{{apiurl}}/lm/promptTemplates/{{promptTemplateId}}` and include the template ID of the prompt you want to delete.
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Send a DELETE request to the endpoint `{{apiurl}}/v2/lm/promptTemplates/{{promptTemplateId}}` and include the template ID of the prompt you want to delete.
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> ### Output Code:
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> ```

docs/sap-ai-core/deploy-models-dd16e8e.md

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<a name="concept_z3s_f3h_vcc__d94e1165"/>
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## Optional Parameters
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The duration of a deployment can be limited using the `ttl` parameter. It takes an integer for quantity, and a single letter to specify units of time. Only minutes \(`m`\), hours \(`h`\) and days \(`d`\), are supported, and values must be natural numbers. For example, `"ttl": "5h"` gives the deployment a duration of 5 hours. `4.5h` and `4h30m` are not valid inputs. If no value is passed, the duration of the deployment if indefinite. Once the duration expires, the deployment is stopped and deleted.
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<a name="concept_z3s_f3h_vcc__d94e1196"/>
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docs/sap-ai-core/export-a-prompt-template-3acef9b.md

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## Procedure
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docs/sap-ai-core/get-a-prompt-template-bc8cead.md

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## Procedure
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> curl -X GET "{{apiurl}}/v2/lm/promptTemplates?name=example-prompt-template&scenario=categorization&version=0.0.1" \
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> ### Sample Code:
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docs/sap-ai-core/get-prompt-template-history-dc204cf.md

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Send a GET request to endpoint `{{apiurl}}/v2/lm/scenarios/{{scenarioId}}/promptTemplates/{{promptTemplateName}}/versions/{{versionId}}/history` and include the name, scenario, and version of your prompt template.
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docs/sap-ai-core/import-a-prompt-template-b122148.md

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> -File "file=@/path/to/your/file.yaml"

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