| name | 0.2.6-understand-colbert_token_embedding |
| description | [0.2.6] Dense contextualized vector representation produced per token by an encoder model, preserving token identity a |
understand-colbert_token_embedding
CALL NUMBER: deep_dense_retrieval.colbert_token_embedding
DEFINITION: Dense contextualized vector representation produced per token by an encoder model, preserving token identity and positional context for late interaction scoring.
Invoke this skill to understand colbert_token_embedding down to its primitives. The RELATIVE ROOT below is the least-fixed-point closure of everything it bundles from — the full import cone, grouped by the lib each prim comes from. Projected from a prover-typed KB (MAP/SWI-Prolog consistency gate): every reference below resolves.
THE RELATIVE ROOT (the import cone, by lib)
from deep_dense_retrieval
- colbert_document_encoder (d1): Encoder producing per-token document embeddings from document passages, typically run offline and indexed for fast retrieval against query embeddings.
- colbert_query_encoder (d1): Encoder producing per-token query embeddings from input query tokens, enabling efficient single-pass encoding with late interaction against document tokens.
- colbert_representation_rank (d2): Rank dimension of per-token embeddings determining expressiveness of token-level similarity computation in late interaction framework.
CONSUMERS (what needs this)
colbert_maxsim_operator
Projected from the retrieval augmented generation architecture patterns KB (207 concepts / 225 relations) — consistency-typed by MAP; the facet list after the colon IS the cross-lib dependency web.
(leaf — this is an actual skill.)
