sap-tutorials · rbrainey · Mar 5, 2025 · Feb 28, 2025 · Feb 28, 2025
diff --git a/tutorials/ai-core-genaihub-prompt-registry/ai-core-genaihub-prompt-registry.md b/tutorials/ai-core-genaihub-prompt-registry/ai-core-genaihub-prompt-registry.md
@@ -0,0 +1,173 @@
+---
+parser: v2
+auto_validation: true
+time: 45
+primary_tag: software-product>sap-business-technology-platform
+tags: [ tutorial>beginner, topic>artificial-intelligence, topic>machine-learning, software-product>sap-business-technology-platform ]
+author_name: Smita Naik
+author_profile: https://github.com/I321506
+---
+
+# Leveraging Prompt Registry for Seamless Orchestration
+<!-- description -->  In this tutorial, we will explore the Prompt Registry feature within Generative AI Hub, focusing on how to efficiently manage and utilize prompt templates in orchestration. You will learn how to register, sync, and integrate prompt templates into the orchestration workflow, ensuring dynamic and structured interactions with AI models.
+
+## You will learn
+- How to register and sync a prompt template in Generative AI Hub.
+- How to consume the registered template within an orchestration workflow.
+- How to apply Grounding techniques, Data Masking, and Content Filtering to refine responses.
+
+## Prerequisites
+- Setup Environment:
+Ensure your instance and AI Core credentials are properly configured according to the steps provided in the initial tutorial
+- Orchestration Deployment:
+Ensure at least one orchestration deployment is ready to be consumed during this process. 
+- Refer to [this tutorial understand the basic consumption of GenAI models using orchestration.](https://developers.sap.com/tutorials/ai-core-orchestration-consumption.html)
+- Basic Knowledge:
+Familiarity with the orchestration workflow is recommended
+
+### Prompt Registry
+
+[OPTION BEGIN [AI Launchpad]]
+
+A **Prompt Registry** is a centralized system for storing, managing, and versioning prompt templates used in AI-driven applications. It allows developers and teams to reuse, modify, and track changes in prompts efficiently. This is particularly useful in large-scale AI projects where prompts need to be standardized, refined, and deployed across different models or scenarios. 
+
+**Why Use a Prompt Registry?** 
+
+•	**Consistency** – Ensures uniform prompts across different use cases.
+•	**Version Control** – Tracks prompt iterations and allows rollback if needed.
+•	**Collaboration** – Enables teams to work on prompt engineering collaboratively.
+•	**Automation** – Integrates prompts seamlessly into AI workflows and CI/CD pipelines.
+
+There are two key approaches to managing prompts in a **Prompt Registry**:
+
+1.	**Imperative API (Direct API Control for Dynamic Prompt Management)**: The Imperative API allows you to create, update, and manage prompt templates dynamically via API calls. This approach is best suited for interactive design-time use cases, where you need to iteratively refine prompts and track their versions. Each change is explicitly made via CRUD operations, and you can manage versions manually.
+2.	**Declarative API (Git-based Sync for Automated Prompt Management)**: The Declarative API, on the other hand, integrates with SAP AI Core applications and is ideal for CI/CD pipelines. Instead of managing templates through direct API interactions, you define them as YAML files in a Git repository. The system automatically syncs these templates, ensuring that updates are seamlessly reflected in the prompt registry without manual intervention.
+
+Next, let's dive into the Declarative approach to creating a prompt template. 
+
+[OPTION END]
+
+### Create a Prompt Template (Declarative)  
+
+[OPTION BEGIN [AI Launchpad]]
+
+- The declarative approach allows you to manage prompt templates using Git repositories, ensuring automatic synchronization with the Prompt Registry. Instead of making API calls to create and update templates manually, you define them in YAML files, commit them to Git, and let the system handle synchronization. 
+
+- Create a prompt template and push it to your git repository.  The file must be named in the following format: “<template-name>.prompttemplate.ai.sap.yaml”. 
+
+- YAML File Structure: Copy the below code
+
+```YAML
+
+name: simple
+version: 0.0.1
+scenario: my-scenario
+spec:
+  template:
+    - role: "system"
+      content: "{{ ?instruction }}"
+    - role: "user"
+      content: "Take {{ ?user_input }} from here"
+    - role: "assistant"
+      content: "Output received: {{ ?grounding_output_variable }}"
+  defaults:
+    instruction: "default instruction"
+    user_input: "default user input"
+    grounding_output_variable: "default output"
+  additionalFields:
+    isDev: true
+    validations:
+      required: true
+    blockedModels:
+      - name: "gpt-4"
+        versions: "gpt-4-vision"
+      - name: "gpt-4o"
+        versions: "*"
+
+```
+![img](img/image001.png)
+**Note** - The defaults and additionalFields fields are optional.  The additionalFields field is unstructured and can be used to store metadata or configuration objects. Refer to the screenshot above for reference. 
+
+- Once the YAML file is created and pushed to Git, the system automatically syncs it with the Prompt Registry. 
+
+### Onboarding Github and Application on AI core 
+
+[OPTION BEGIN [AI Launchpad]]
+
+- Select on your SAP AI Core connection under Workspaces app in the SAP AI Launchpad. Under the **Git Repositories** section in **AI Core Administration app**, click **Add**. 
+
+![img](img/image003.png)
+
+**Note:** If you don’t see the AI Core Administration app, check that you had selected your SAP AI Core connection from the Workspaces app. If it is still not visible then ask your SAP AI Launchpad administrator to assign roles to you so that you can access the app. 
+
+- Enter your GitHub repository details (created in the previous step) in the dialog box that appears, and click **Add**.
+
+Use the following information as reference:
+
+-	**URL**: Paste the URL of your GitHub repository and add the suffix /workflows.
+-	**Username**: Your GitHub username.
+-	**Password**: Paste your GitHub Personal Access Token
+
+![img](img/image005.png)
+
+**Note:** Password does not gets validated at time of Adding Github Repository its just meant to save Github Creds to AI core. Passwords gets validated at time of creating Application or when Application refreshes connection to AI core.
+
+You will see your GitHub onboarding completed in a few seconds. As a next steps we will enable an application on AI core.
+
+- Go to your **SAP AI Launchpad**.In the **AI Core Administration app**, click **Applications > Create**.
+
+![img](img/image007.png)
+
+- Using the reference below as a guide, specify the details of your application. This form will create your application on your SAP AI Launchpad.
+
+- Use the following information for reference:
+    - 	**Application Name**: An identifier of your choice.
+    - 	**Repository URL**: Your GitHub account URL and repository suffix. This helps you select the credentials to access the repository.
+    - 	**Path**: The folder in your GitHub where your workflow is located. For this tutorial it is LearningScenarios.
+    - 	**Revision**: The is the unique ID of your GitHub commit. Set this to HEAD to have it automatically refer to the latest commit.
+
+**Click on the application you created, then select 'Sync' to synchronize your changes.**
+![img](img/image009.png)
+
+After synchronization, navigate to **ML Operations > Scenarios** in the **SAP AI Core Launchpad** and verify your scenario by checking the name specified in your YAML file.
+
+![img](img/image025.png)
+
+[OPTION END]
+
+### Verifying and Consuming the Prompt Template
+
+[OPTION BEGIN [AI Launchpad]]
+
+Once the template is synced to the **AI Core Launchpad**, follow these steps to integrate it into your orchestration:
+
+- Navigate to Generative AI Hub and select the Template tab.
+![img](img/image026.png)
+
+- Choose your desired template and click Select. The Templating section will update accordingly.
+![img](img/image027.png)
+
+- Go to the Grounding section and set the input variable to user_input and In the Data Repositories section, click the + icon and select the required pipeline.
+![img](img/image028.png)
+
+- Configure Data Masking by selecting the sensitive information categories (e.g., Name, Organization) that need to be masked. sensitive information.
+![img](img/image029.png)
+
+- Set Input Filtering thresholds for content moderation categories such as Hate, Self-Harm, Sexual, and Violence. Adjust the settings to Allow Safe and Low / Block Medium and High as needed.
+![img](img/image030.png)
+
+- Select Model Configuration by choosing the appropriate model for orchestration.
+![img](img/image031.png)
+
+- Set Output Filtering using the same threshold settings as input filtering to ensure consistency in moderated responses.
+![img](img/image032.png)
+
+- Once all configurations are complete, click Test to validate your orchestration workflow.
+  - Instruction: "Provide a brief explanation of SAP AI Core and its key functionalities."
+  - User Input: "What are the main capabilities of SAP AI Core?"
+
+- After entering these values, execute the test to verify the response. The system should return relevant details based on your configured prompt template and filtering settings.
+![img](img/image033.png)
+
+[OPTION END]
+
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diff --git a/tutorials/ai-core-orchestration-grounding/ai-core-orchestration-Grounding.md b/tutorials/ai-core-orchestration-grounding/ai-core-orchestration-Grounding.md
@@ -511,7 +511,9 @@ secret.json()
 
 [OPTION END]
 
-### 7. Prepare knowledge base and verification
+### 7. Prepare knowledge base(data repository) and verification
+
+#### 7.a Using Pipeline API [Option-1]
 
 [OPTION BEGIN [Bruno]]
 
@@ -551,72 +553,14 @@ This request checks the current status of a specific pipeline, such as whether i
 
 Once the pipeline is successfully created, documents uploaded in SharePoint are converted into vectors via APIs. The conversion process can be validated upon successful pipeline execution.
 
-#### Verifying Vector Processing (Optional)
-
-These steps help inspect vector collections and documents to confirm successful processing.
-
- • **get_all_collections** – Lists all existing vector collections for validation.
-
-![img](img/image034.png)
-
- • **get_collection_creation_status_by_id** – Checks whether the collection was created successfully.
-
-![img](img/image035.png)
-
- • **get_collection_by_id** – Retrieves detailed information about a specific collection
-
-![img](img/image036.png)
-
- • **get_all_documents_by_collection_id** – Lists all stored documents to ensure correct ingestion.
-
-![img](img/image037.png)
-
- • **get_documents_by_id** – Fetches specific documents within a collection for debugging
-
-![img](img/image038.png)
-
- • **get_collection_deletion_status_by_id** – Confirms successful deletion of a collection if needed.
-
-![img](img/image039.png)
-
-#### Ensuring Data Retrieval for AI Processing (Optional)
-
-These steps confirm that data repositories are accessible for AI-powered searches and retrieval.
-
- • **dataRrepositories** – Lists all available repositories to ensure accessibility.
-
-![img](img/image040.png)
-
- • **dataRepositories by id** – Fetches details of a specific repository for targeted debugging.
-
-![img](img/image041.png)
-
-[OPTION END]
-
-[OPTION BEGIN [AI Launchpad]]
-
-Grounding is a crucial step in orchestration that ensures responses are enriched with relevant and accurate data from predefined sources. This section explains how grounding works in the AI Launchpad.
-
-**Input Variables**
-
-Input variables are parameters sent to the grounding service to facilitate data retrieval. These variables can be referenced in the template definition, allowing dynamic data incorporation based on user inputs.
-
-**Output Variable**
-
-The output variable holds the retrieved data from the grounding service. This data can then be utilized in the template definition to generate contextual and informed responses.
-
-**Selected Sources**
-
-You can specify the repositories from which the grounding module retrieves information. If no specific repositories are selected, grounding will include all available sources by default. Selecting relevant sources ensures precise and domain-specific data retrieval for improved orchestration outcomes.
-
-![img](img/image047.png)
-
 [OPTION END]
 
 [OPTION BEGIN [SAP Cloud SDK]]
 
 we are creating a document-grounding pipeline using SAP AI Core. The pipeline is configured to integrate with Microsoft SharePoint as a data source, enabling AI-driven document processing. This setup allows seamless ingestion of documents from a specified SharePoint site, ensuring efficient data retrieval and processing.
 
+**Note:** At present, pipeline creation is not supported in the grounding feature of Cloud SDK, as we have utilized API requests to establish the pipeline.
+
 ```javascript
 
 const jsonData = {
@@ -672,6 +616,8 @@ createPipeline().then(pipelineId => {
 
 we are creating a document-grounding pipeline using SAP AI Core. The pipeline is configured to integrate with Microsoft SharePoint as a data source, enabling AI-driven document processing. This setup allows seamless ingestion of documents from a specified SharePoint site, ensuring efficient data retrieval and processing.
 
+**Note:** At present, pipeline creation is not supported in the grounding feature of Gen AI SDK, as we have utilized API requests to establish the pipeline.
+
 ```python
 
 json_data = {
@@ -702,6 +648,70 @@ pipeline.json()['pipelineId']
 
 [OPTION END]
 
+#### 7.b Using Vector API [Option-2]
+
+[OPTION BEGIN [Bruno]]
+
+#### Create collection
+
+• Expand 06_vector and select create_collections request.
+
+• Replace value <collection_name> with the required collection name.
+
+• The metadata can be an array of key-value pairs to have some additional information.
+
+**Note:** Currently supported modelName is only text-embedding-ada-002-v2.
+
+![img](img/image066.png)
+
+#### Create documents
+
+• Click on the create_document request and replace the path parameter with the valid collection ID.
+
+• Replace value <metadata_key> with the required key of metadata and put corresponding values to it.  Multiple metadata can be specified here.
+
+• Replace values <chunk_1>, <chunk_2> with the required chunk of the documents. Add multiple chunks   based on the requirement. The metadata can also be specified for each chunk.
+
+![img](img/image067.png)
+
+#### Verifying Vector Processing (Optional)
+
+These steps help inspect vector collections and documents to confirm successful processing.
+
+ • **get_all_collections** – Lists all existing vector collections for validation.
+
+![img](img/image034.png)
+
+ • **get_collection_creation_status_by_id** – Checks whether the collection was created successfully.
+
+![img](img/image035.png)
+
+ • **get_collection_by_id** – Retrieves detailed information about a specific collection
+
+![img](img/image036.png)
+
+ • **get_all_documents_by_collection_id** – Lists all stored documents to ensure correct ingestion.
+
+![img](img/image037.png)
+
+ • **get_documents_by_id** – Fetches specific documents within a collection for debugging
+
+![img](img/image038.png)
+
+ • **get_collection_deletion_status_by_id** – Confirms successful deletion of a collection if needed.
+
+![img](img/image039.png)
+
+ • **dataRepositories** - List all the collection of Data repositories 
+
+![img](img/image040.png)
+
+ • **dataRepositories by id** – Fetches details of a specific repository for targeted debugging.
+
+![img](img/image041.png)
+
+[OPTION END]
+
 ### 8. Ensuring Accurate Responses with Grounding
 
 In the previous steps, we have completed the data preparation for grounding. Before initiating model inference or orchestration, ensure that there is an active orchestration deployment (**scenario ID: orchestration**). To verify the available orchestration deployments and their status, use the **get_deployment** API under the **"Deployments"** section in the **Bruno collection**. Additionally, update the **orchestration_service_url** in the environment. 
@@ -710,6 +720,8 @@ In the previous steps, we have completed the data preparation for grounding. Bef
 
 [OPTION BEGIN [Bruno]] 
 
+#### Data Retrieval via Orchestration service [Option-1]
+
 This step uses the orchestration service to query grounded documents and retrieve content based on a specified prompt and grounding query. It integrates document grounding configurations and filters to provide precise results.
 
 •	Expand **05_orchestration** and select **completion request**
@@ -720,6 +732,16 @@ This step uses the orchestration service to query grounded documents and retriev
 
 ![img](img/image052.png)
 
+#### Data Retrieval using Vector APIs [Option-2]
+
+• Expand the 07_retrieval Click on the retrieval_vector request.
+
+• Replace value <query> with the required query to which the vector search has to be done.
+
+• Replace value <data_repository_id1> with the id of the data repository using which the vector  search has to be performed. Multiple data repository ids can be specified here.
+
+![img](img/image068.png)
+
 #### Responses Without External Knowledge Base
 
 ![img](img/image053.png)
@@ -728,6 +750,22 @@ This step uses the orchestration service to query grounded documents and retriev
 
 [OPTION BEGIN [AI Launchpad]]
 
+Grounding is a crucial step in orchestration that ensures responses are enriched with relevant and accurate data from predefined sources. This section explains how grounding works in the AI Launchpad.
+
+**Input Variables**
+
+Input variables are parameters sent to the grounding service to facilitate data retrieval. These variables can be referenced in the template definition, allowing dynamic data incorporation based on user inputs.
+
+**Output Variable**
+
+The output variable holds the retrieved data from the grounding service. This data can then be utilized in the template definition to generate contextual and informed responses.
+
+**Selected Sources**
+
+You can specify the repositories from which the grounding module retrieves information. If no specific repositories are selected, grounding will include all available sources by default. Selecting relevant sources ensures precise and domain-specific data retrieval for improved orchestration outcomes.
+
+![img](img/image047.png)
+
 **Templating**
 
 Templating enables you to define structured prompts and system messages for the generative AI model. Using placeholders like {{?groundingRequest}} and {{?groundingOutput}}, you can dynamically customize inputs for grounding-based data retrieval. These placeholders must follow naming rules and can have default values for testing. If no default value is set, the workflow prompts for input during execution.
@@ -948,6 +986,8 @@ else:
 
 ![img](img/image064.png)
 
+![img](img/image065.png)
+
 [OPTION END]
 
 ### Conclusion

diff --git a/tutorials/ai-core-orchestration-grounding/img/image065.png b/tutorials/ai-core-orchestration-grounding/img/image065.png
diff --git a/tutorials/ai-core-orchestration-grounding/img/image066.png b/tutorials/ai-core-orchestration-grounding/img/image066.png
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