Embeddings, Vector Search, Graphs and Semantic Layers

Definition

Embeddings, Vector Search, Graphs and Semantic Layers

Embeddings are dense numeric representations that place semantically related items near each other in vector space. Vector search uses those representations for approximate semantic retrieval, while graphs and semantic layers encode entities, relationships, business meaning and constraints that pure vector similarity may miss. Robust AI systems often combine these techniques with metadata filters, ranking, provenance and evaluation.

Why It Matters

Embeddings represent content as numerical vectors; vector search retrieves items with similar representations. It is useful for semantic similarity, but exact search, filters, SQL, graphs or hybrid retrieval may be better for other query types.

Knowledge graphs and semantic layers complement vector retrieval. A knowledge graph makes entities and relationships explicit. A semantic layer gives consistent business meaning to structured data and metrics.

Failure mode

The recurring risk is treating this concept as a label rather than an engineering decision with trade-offs, evidence needs and operational consequences.

Evaluation check

Ask what observable evidence would show that this concept has been applied well in a real system, repository or learning exercise.

Related Concepts

• Data modelling and data engineering
• Grounding and RAG

Further study: OpenAI, Embeddings, and Stanford CS224W materials on graph machine learning.