SAP BTP AI Best Practice Demo - Content Filtering - CAP
A CAP backend-sample and fullstack-sample that call SAP AI CoreSAP AI CoreSAP's AI service on BTP: the runtime for AI assets, and through the Generative AI Hub inside it the access to foundation models, the orchestration service with its templating, masking, filtering and grounding modules, the Prompt Registry and Document Grounding.sap official, btp with content filtering switched on and off, to compare the filtered and unfiltered response.
chatWithSupport takes a filterInput flag and generateParaphrase takes a filterOutput flag, both resolved through the SAP Cloud SDK for AI; the fullstack-sample adds a UI5 app under app/ask-agent plus its own approuter, and its README screenshots the filtered and unfiltered response for both actions. Its own README describes content filtering as evaluating and selecting input data so low-quality or irrelevant text does not reach the model, calling this out as especially relevant to real-time or sensitive use. Both bind to a SAP AI CoreSAP AI CoreSAP's AI service on BTP: the runtime for AI assets, and through the Generative AI Hub inside it the access to foundation models, the orchestration service with its templating, masking, filtering and grounding modules, the Prompt Registry and Document Grounding.sap official, btp service instance with cds bind.
Facts
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Who is behind it
In the atlas: maintains 222@SAP-samplesmaintainerLuís MarquesContributes to this
In the atlas: contributes to 8@luismarques-io1 contributor
Before choosing this
Conditions
PracticalFunctional boundary
Every CDS service in both samples is marked @requires: 'any' and left publicly accessible, which the README says simplifies the testing and deployment process, and it asks for proper authentication mechanisms to be applied in their place.
What it declares
Every artifact
- Type
- Sample
- Origin
- By SAP
- Use as
- A reference
- Runtime
- Node.js
As a sample
- Form
- A reference application
Connections
Checking model input and output against content rules
AI runtime on BTP with the Generative AI Hub
AI runtime on BTP with the Generative AI Hub
Download
README
quoted from SAP-samples/sap-btp-ai-best-practices under Apache-2.0, readCAP Implementations for Generative AI Access
This directory contains Cloud Application Programming (CAP) model implementations that demonstrate best practices for using content filtering with generative AI models using the SAP AI SDK. Content filtering is a critical process that ensures the quality and relevance of input data. It involves evaluating and selecting data to prevent low-quality or irrelevant inputs from affecting model performance. This process is essential for maintaining the accuracy and reliability of LLMs, especially when they are used for real-time applications or sensitive tasks.
Available Samples
- backend-sample: Standalone CAP backend service implementation
- fullstack-sample: Complete CAP application with UI5 frontend
Overview
These samples showcase how to:
- Connect to generative AI services from CAP applications
- Structure service layers for AI orchestration
- Implement proper error handling
- Deploy applications to SAP BTP
Getting Started
Each subdirectory contains a comprehensive README.md with detailed setup instructions, deployment guides, and usage examples.
Details
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Compatibility and provenance
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History in the atlas
- First seen.
For your agent
- Ref
artifact:sap-samples-btp-ai-bp-content-filtering-cap- JSON
- /artifacts/sap-samples-btp-ai-bp-content-filtering-cap.json
- MCP
- Connecting an agent