SAP BTP AI Best Practice Demo - Vector Query (RAG) - CAP

A CAP backend-sample and fullstack-sample that run a similarity query over the vectors in SAP HANA CloudSAP HANA CloudserviceThe 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 and pass the closest science-data rows to the model as context.

The service exposes three actions: uploadScienceData, which creates the embeddings for an uploaded CSV, scienceData, which lists the stored rows, and queryScienceData, which returns the rows closest to a query string. Its README sets out the reasoning first: a vector store keeps documents in vectorized form, similarity metrics retrieve the most semantically relevant ones, and those are passed to the model as context during inference, which is what retrieval-augmented generation names. The rows live in SAP HANA CloudSAP HANA CloudserviceThe 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, whose HDI container the sample builds with npm run build:db before a run, beside the cds bind for its SAP AI CoreSAP AI CoreserviceSAP'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 instance. Where the embedding sampleSAP BTP AI Best Practice Demo - Vector Embedding (RAG) - CAPsampleA CAP backend-sample and fullstack-sample that parse an uploaded CSV of science data, embed each row through SAP AI Core and store the vectors in SAP HANA Cloud's vector store.sap official, cap stops at writing the vectors, this one reads them back; its fullstack-sample shows the same service behind a UI, in three screenshots.

Who is behind it

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