Concept card

Embeddings

As of 2026-07-23

Edited by Cédric-Olivier Mâry, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is Embeddings?

Sentence embeddings power RAG by matching meaning, not syntax — but the trade-off is stark: a large model (3,072d, $0.13/M) versus small (1,536d, $0.02/M) versus a local 384d model, and mixing models in one index silently breaks retrieval.

What it is

An embedding is a dense real-valued vector that encodes the semantic content of a token, sentence, or document in a high-dimensional space, such that semantically similar inputs cluster geometrically. Embeddings are the bridge between discrete text and the continuous mathematics of neural networks.

The problem they solve is the inability of exact-match search to find 'net revenue' when the user asks 'total income' — embeddings encode meaning, not syntax, enabling semantic retrieval at scale.

Why it matters

Matryoshka Representation Learning compresses a 3,072d vector down to a usable 256d with only ~5% NDCG@10 loss — a concrete storage-cost lever.
SAP HANA Cloud's vector engine returns top-k results under 10ms at 1M documents with native 1,536d cosine similarity — a real performance baseline to quote.
Model incompatibility across an index is an unforced error: embeddings from different models occupy incompatible vector spaces.

Key points

Sentence embeddings (not word embeddings) are the correct unit for RAG over SAP documents — one vector per paragraph, not per token.
Cosine similarity, not Euclidean distance, is the standard metric for embedding similarity; HANA Cloud uses cosine by default.
Matryoshka embeddings allow truncation from 3,072d to 256d with only ~5% NDCG@10 loss — large storage savings.
Never mix embeddings from different models in the same index; the vector spaces are not aligned.
HANA Cloud vector engine: 1,536d cosine, top-k < 10ms at 1M docs — no external vector DB required for most SAP RAG workloads.
Embeddings is mastered only when it changes a named buyer decision.
Start with the semantic contract and control model before demonstrating the tool.
Use current SAP, analyst, study, KG, and news signals as evidence, not decoration.
Separate verified facts from directional trends and modeled assumptions.
Define owner, metric, threshold, support path, and rollback before scaling.

Terms used on this page

Embedding: A dense real-valued vector representing a text unit in a learned semantic space.
Cosine similarity: cos(θ) = (A·B)/(|A||B|): measures angle between two vectors; 1.0 = identical direction, 0 = orthogonal, −1 = opposite.
Matryoshka Representation Learning (MRL): Training technique that makes the first k dimensions of a large embedding usable as a standalone smaller embedding, enabling flexible dimensionality.
RAG: Retrieval-Augmented Generation: architecture that embeds a document corpus, retrieves the top-k relevant chunks at query time, and appends them to the LLM prompt.
Vector index: An approximate nearest-neighbour (ANN) index (e.g. HNSW, IVF) that enables sub-linear search over large embedding collections.
Decision owner: The accountable person who accepts the trade-off and funds the next action.
Semantic contract: The shared definition of business terms, metrics, entities, and access rules used by tools and teams.
Control plane: The layer that applies policy, access, lineage, monitoring, and escalation across the operating model.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks.

Open in the app →