Embeddings and vector search on HANA Cloud from Analytic Data
As of 2026-11-07
Teaches vector search on SAP HANA Cloud specifically for structured analytic data — master records, transaction summaries, catalog rows — as distinct from document RAG. Grounds every claim in SAP's documented Vector Engine: VECTOR_EMBEDDING's three arguments, the SAP_NEB.20240715 built-in model (768 dimensions, NLP-feature prerequisite), REAL_VECTOR/HALF_VECTOR storage costs (4/2 bytes per dimension, 1-65,000 dimension range), COSINE_SIMILARITY and L2DISTANCE, and the documented pattern of joining vector-search output back to relational business data in one query. Covers exact versus HNSW approximate search as a measured recall trade-off, not a default; shows that JustAsk's own model-selection step is the identical embed-then-compare mechanism one layer up the stack; and introduces the Knowledge Graph Engine as the complementary tool for relationship, not resemblance, questions (hybrid grounding, concept “Hybrid Grounding on HANA Cloud — Vector Engine + Knowledge Graph Engine”). Three exercises build an embedded sentence, size a vector column, and choose a search strategy with justification. Builds on “AI & LLM Fundamentals for SAP Consultants — Transformers, Tokens, Embeddings, Context”, “AI-Ready Data — Quality, Semantics and Lineage”, “The Semantic Layer as LLM Context” and “Natural-Language Query over SAP Data — Text-to-SQL Patterns and Limits”.
What you will learn
- State VECTOR_EMBEDDING's documented syntax (text, text type, model/version) and the NLP-feature prerequisite for SAP's built-in model
- Calculate a REAL_VECTOR or HALF_VECTOR column's memory footprint from row count and dimension using the documented formula
- Construct a coherent embedded sentence from structured analytic columns, deliberately excluding non-semantic fields
- Choose between exact and approximate (HNSW) vector search based on scale and a measured recall trade-off, not assumption
- Explain how JustAsk's own model-selection step is the same embed-then-compare mechanism taught in this module, and when a Knowledge Graph Engine traversal is needed instead of or alongside vector search
Module overview
Who this is for. “HANA Cloud Vector Engine & Knowledge Graph Engine for RAG” covered the HANA Cloud Vector Engine for document RAG. This module covers a different, narrower case a consultant meets constantly once “The Semantic Layer as LLM Context” and “Natural-Language Query over SAP Data — Text-to-SQL Patterns and Limits” are in place: turning rows of structured analytic data — master records, transaction summaries, catalog entries — into vectors, and joining the results of a similarity search back to the governed business data that gave them meaning. It builds on embeddings fundamentals, AI-ready data and “Embeddings and vector search on HANA Cloud from Analytic Data” sits after “Natural-Language Query over SAP Data — Text-to-SQL Patterns and Limits” because it is the retrieval mechanism text-to-SQL and Just Ask cannot replace: finding the similar, not the exact.
Prerequisites
- AI & LLM Fundamentals or working knowledge of embeddings and similarity, and HANA Cloud Vector Engine & Knowledge Graph Engine if available
- AI-Ready Data or understanding of data-quality checks before grounding
- “The Semantic Layer as LLM Context” and “Natural-Language Query over SAP Data — Text-to-SQL Patterns and Limits”, or working knowledge of JustAsk's semantic-search-then-query pipeline
- SQL proficiency in SAP HANA Cloud, including table design and joins
Outcomes
- Design a vector-enabled table for structured analytic data that joins cleanly back to its governed source.
- Produce a memory-footprint estimate for a proposed vector column before committing to it in a client proposal.
- Choose and justify exact versus approximate search with a measured recall comparison, not a default assumption.
- Explain to a client, precisely, the difference between a similarity search and a knowledge-graph relationship query, and when both are needed together.
Full module with the Consultant plan — €19.90/month. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.