Embeddingsconcept
An embedding is a dense vector representation of text (or other data) such that semantic similarity corresponds to geometric proximity in vector space.
Embeddings make semantic similarity computable: once text is a vector, 'how related are these two passages?' becomes a distance calculation, enabling retrieval and clustering without symbolic rules.
Where it appears
- Knowledge Compiler embedding generator pass (Phase 4)
- SKCE foundation links from concepts to linear algebra
Descend
Prerequisites
Dependencies
Foundations
Historical evolution
From Word2Vec (2013) and GloVe to contextual transformers (BERT, 2018) and frontier text-embedding-3 models. The 2026-07-11 post notes a deterministic TF-IDF fallback enables the pipeline with no API key.
Implementation details
Upstream EmbeddingGeneratorPass uses OpenAI embeddings with a TF-IDF pseudo-embedding fallback; SKCE explains the math rather than producing live embeddings at runtime.
Sources
- [2026-07-11-knowledge-compiler-compiling-human-knowledge-into-static-semantic-artifacts] 2026-07-11-knowledge-compiler-compiling-human-knowledge-into-static-semantic-artifacts
- [2026-07-11-knowledge-compiler-compiling-human-knowledge-into-static-semantic-artifacts] 2026-07-11-knowledge-compiler-compiling-human-knowledge-into-static-semantic-artifacts