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Sun 18
CAP in the age of AI #6 - vector embeddingspublishedVector embeddings put to work in the incident managementcapire/incidents-appThe Incident Management reference application — the one SAP's own tutorial paths build, step by step, and the model cds add incidents-sample drops into a project.sap official, cap sample, answering whether this problem has been seen before. An
embedding element of type Vector is calculated and stored from the vector_embedding function, so the database refreshes it whenever the text behind it changes, and cosine_similarity compares it with a freshly embedded string; because a title says little, a scheduled action first summarises each incident's conversation through a model on 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 and the embedding is taken from that summary. The similar-incidents list is a view joining incidents to themselves, with order and limit set in a before handler on the expanded query, and the vector element excluded from the service, since OData knows nothing about vectors — on SAP HANA CloudSAP HANA CloudThe managed database under most of this: CAP's default target, the store Datasphere and Analytics Cloud sit on, and a service with plans and components.sap official, btp the function is native, while SQLite needs packages added to the project.LLM integrationCAP in the age of AI #7 - vector embeddings wrap-uppublishedThe same embeddings model taken from SQLite to SAP HANA CloudSAP HANA CloudThe managed database under most of this: CAP's default target, the store Datasphere and Analytics Cloud sit on, and a service with plans and components.sap official, btp, then handed to an agent. Locally it is @cap-js/ai@cap-js/aiOut-of-the-box value-help recommendations powered by SAP RPT-1, SAP AI Core as a standard CAP service, and an experimental local embedding model behind SQLite's vector functions.sap official, cap that adds the vector functions SQLite lacks and downloads an all-MiniLM L6v2 ONNX model on first start; on HANA natural language processing has to be switched on, the model name is a mandatory argument, the vectors it returns are 768 dimensions, and dimensions must match on both sides of a comparison. HANA itself can be given 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 as a remote source for other embedding models, which a
CDS.ql expression has to inject because the postfix notation only takes what the CDS model holds, and the hour ends with a search function over cosine similarity exposed over @cap-js/mcp@cap-js/mcpA CAP plugin that serves an @mcp-annotated service over MCP with three generated tools, describe, query and call_action, and writes its endpoint into the local agent's configuration in development.sap official, cap, so an agent asked about low output from solar panels calls semantic search instead of writing the query itself.LLM integrationSat 17
Agentic CAP: Build MCP servers and enterprise AI agentsexplains revisedDoor scope derived from the artifacts this piece also explains, so a door page lists it only where it holds (docs/concepts/content-on-doors.md).Serve your application over MCP
AI-native North Star architecture — Platform Layerexplains revisedDoor scope proposed from the title and summary, so a door page lists the piece only where it holds (docs/concepts/content-on-doors.md).Agent tooling
Document Groundingexplains revisedDoor scope derived from the artifacts this piece also explains, so a door page lists it only where it holds (docs/concepts/content-on-doors.md).Retrieval-augmented generation
SAP IAM integration with SAP Cloud Identity Servicesexplains revisedDoor scope proposed from the title and summary, so a door page lists the piece only where it holds (docs/concepts/content-on-doors.md).Authentication
Sun 11
Agentic CAP: Build MCP servers and enterprise AI agentspublishedCAP applications become AI agents over the Model Context Protocol and the Agent2Agent protocol, which is the serve your application over MCPServe your application over MCPExposing selected application data and business operations to an AI agent through the Model Context Protocol. An application may also integrate remote services through service integration, connecting its exposed contract with agent access to your systems. Development assistance belongs to agent access to your code.SAP and community, narrow by design concern, and 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 is the LLM integrationLLM integrationCalling a language model from application code with the tenant, destination and credential handling already solved instead using an SDK call with a hand-rolled key.SAP and community, narrow by design through which AI is embedded in a CAP app, with recommendations from the SAP-RPT-1 model as the abstract's example. The abstract names the two protocols and the one model and does not say which packages carry them. A two-hour TechEd 2026 session in a hands-on room (Tuesday 27 October, Berlin), presented by Daniel HutzelDaniel Hutzelgave 3 talks, contributed to 32 entriessap official, Daniel SchlachterDaniel Schlachtercreated 9 pieces, contributed to 10 entriessap official, Marc Huber and Sami Lechner, written here from SAP's abstract; the recording is not yet published.Serve your application over MCP
Fri 9
AI-native North Star architecture — Platform Layerparts revisedFilled from SAP's own pages at the maintainer's request (docs/concepts/product-doors.md was the occasion; the AI-native door reads these designs). Every part names what the graph holds; what it lacks is listed as candidates in the curation note.Agent tooling