What Embedding means in practice
An embedding is a fixed-length vector — typically 384 to 1024 dimensions — produced by an embedding model from a piece of text. The geometric property that makes embeddings useful is that semantically similar inputs produce vectors close in the embedding space, so a similarity search becomes a nearest-neighbour lookup. In retrieval-augmented generation, document chunks are embedded once at ingestion time, query embeddings are computed at query time, and the nearest chunks are passed to the LLM as context. For sovereign deployments MindMap uses nomic-embed-text for English-primary corpora and BGE-M3 for multilingual workloads — both open-weights and locally deployable.
Related terms
Vector database →
A database optimised for storing high-dimensional vectors and serving nearest-neighbour queries — the storage layer that makes RAG fast.
Retrieval-Augmented Generation (RAG) →
A pattern where, instead of relying solely on the LLM's training, the system retrieves relevant documents from a knowledge base and includes them in the prompt as context.
Semantic search →
Search that matches on meaning rather than exact keywords, by comparing embeddings of the query and the documents.
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The Sovereign AI Playbook.
Enterprise generative AI that runs on your servers.
- 01Definition. The four non-negotiable properties of a sovereign deployment — and the three patterns that look sovereign but aren't.
- 02Why now. Three forces that converged in 2026: regulator pressure, the open-weights capability gap closing, and unit economics flipping.
- 03Reference architecture. The five-layer stack we deploy: applications → agent runtime → sovereign LLM → RAG + KB → data stores. With model and tooling choices for each.
- 04The regulators. SAMA · RBI · UK ICO · EU AI Act · DPDP · HIPAA — what each requires and how it maps to architecture.
All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.