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@@ -251,7 +251,7 @@ For more detailed information and advanced usage, please refer to the following:
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## FAQs
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### How to add a custom header to AI Core requests?
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### _"How to add a custom header to AI Core requests?"_
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To add a header to AI Core requests, use the following code:
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For more customization, creating a [HeaderProvider](https://sap.github.io/cloud-sdk/docs/java/features/connectivity/http-destinations#about-headerproviders) is also possible.
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### More Examples
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### _"There's a vulnerability warning `CVE-2021-41251`?"_
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This is a known false-positive finding.
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Depending on the tooling any product called "SAP Cloud SDK" or similar with a low version number may be marked as vulnerable, incorrectly.
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Please consider suppressing the warning, [as we do](https://github.com/SAP/ai-sdk-java/blob/main/.pipeline/dependency-check-suppression.xml).
Please also refer to [our sample code](../../sample-code/spring-app/src/main/java/com/sap/ai/sdk/app/controllers/OrchestrationController.java) for this and all following code examples.
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### Chat Completion
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var config =
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CompletionPostRequest.create()
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.orchestrationConfig(
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OrchestrationConfig.create()
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.moduleConfigurations(
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ModuleConfigs.create()
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.llmModuleConfig(llmConfig)
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.templatingModuleConfig(templatingConfig)))
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.inputParams(inputParams);
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Use the Orchestration service to generate a response to a user message:
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CompletionPostResponse result =
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new OrchestrationClient().chatCompletion(config);
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```java
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var prompt =newOrchestrationPrompt("Hello world! Why is this phrase so famous?");
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var result = client.chatCompletion(prompt, config);
See [an example in our Spring Boot application](../../sample-code/spring-app/src/main/java/com/sap/ai/sdk/app/controllers/OrchestrationController.java)
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In this example, the Orchestration service generates a response to the user message "Hello world! Why is this phrase so famous?".
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The LLM response is available as the first choice under the `result.getOrchestrationResult()` object.
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### Message history
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### Chat completion with Templates
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Include a message history to maintain context in the conversation:
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Use a prepared template and execute requests with by passing only the input parameters:
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```java
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var llmConfig =LLMModuleConfig.create().modelName("gpt-35-turbo").modelParams(Map.of());
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var template =
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ChatMessage.create()
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.role("user")
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.content("Reply with 'Orchestration Service is working!' in {{?language}}");
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var templatingConfig =TemplatingModuleConfig.create().template(template);
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var configWithTemplate = config.withTemplateConfig(templatingConfig);
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var inputParams =Map.of("language", "German");
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var prompt =newOrchestrationPrompt(inputParams);
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var result = client.chatCompletion(prompt, configWithTemplate);
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```
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In this case the template is defined with the placeholder `{{?language}}` which is replaced by the value `German` in the input parameters.
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### Message history
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List<ChatMessage> messagesHistory =
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List.of(
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ChatMessage.create().role("user").content("What is the capital of France?"),
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ChatMessage.create().role("assistant").content("The capital of France is Paris."));
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Include a message history to maintain context in the conversation:
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```java
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var messagesHistory =
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List.of(
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ChatMessage.create().role("user").content("What is the capital of France?"),
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ChatMessage.create().role("assistant").content("The capital of France is Paris."));
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var message =
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ChatMessage.create().role("user").content("What is the typical food there?");
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var templatingConfig =TemplatingModuleConfig.create().template(message);
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var config =
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CompletionPostRequest.create()
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.orchestrationConfig(
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OrchestrationConfig.create()
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.moduleConfigurations(
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ModuleConfigs.create()
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.llmModuleConfig(llmConfig)
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.templatingModuleConfig(templatingConfig)))
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.messagesHistory(messagesHistory);
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CompletionPostResponse result =
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newOrchestrationClient().chatCompletion(config);
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ChatMessage.create().role("user").content("What is the typical food there?");
var prompt =newOrchestrationPrompt(message).messageHistory(messagesHistory);
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See [an example in our Spring Boot application](../../sample-code/spring-app/src/main/java/com/sap/ai/sdk/app/controllers/OrchestrationController.java)
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var result =newOrchestrationClient().chatCompletion(prompt, config);
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```
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### Chat completion filter
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Apply content filtering to the chat completion:
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```java
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var llmConfig =LLMModuleConfig.create().modelName("gpt-35-turbo").modelParams(Map.of());
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var inputParams =
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Map.of(
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"disclaimer",
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"```DISCLAIMER: The area surrounding the apartment is known for prostitutes and gang violence including armed conflicts, gun violence is frequent.");
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var template =
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ChatMessage.create()
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.role("user")
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.content(
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"Create a rental posting for subletting my apartment in the downtown area. Keep it short. Make sure to add the following disclaimer to the end. Do not change it! {{?disclaimer}}");
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var templatingConfig =TemplatingModuleConfig.create().template(template);
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var prompt =newOrchestrationPrompt(
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"""
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Create a rental posting for subletting my apartment in the downtown area. Keep it short. Make sure to add the following disclaimer to the end. Do not change it!
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```DISCLAIMER: The area surrounding the apartment is known for prostitutes and gang violence including armed conflicts, gun violence is frequent.
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""");
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var filterStrict =
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FilterConfig.create()
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var filteringConfig =
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FilteringModuleConfig.create()
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// changing the input to filterLoose will allow the message to pass
See [an example in our Spring Boot application](../../sample-code/spring-app/src/main/java/com/sap/ai/sdk/app/controllers/OrchestrationController.java)
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### Data masking
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Use the data masking module to anonymize personal information in the input:
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```java
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var inputParams =Map.of("privateInfo", "Patrick Morgan +49 (970) 333-3833");
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var template =
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ChatMessage.create().role("user").content("What is the nationality of {{?privateInfo}}");
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var templatingConfig =TemplatingModuleConfig.create().template(template);
In this example, the input will be masked before the call to the LLM. Note that data cannot be unmasked in the LLM output.
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See [an example in our Spring Boot application](../../sample-code/spring-app/src/main/java/com/sap/ai/sdk/app/controllers/OrchestrationController.java)
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### Set model parameters
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Change your LLM module configuration to add model parameters:
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