0-2-5-understand-colbert-maxsim-operator

name	0.2.5-understand-colbert_maxsim_operator
description	[0.2.5] Maximum similarity operator that for each query token finds the highest-scoring document token via cosine simi

understand-colbert_maxsim_operator

CALL NUMBER: deep_dense_retrieval.colbert_maxsim_operator DEFINITION: Maximum similarity operator that for each query token finds the highest-scoring document token via cosine similarity, then aggregates across query tokens to produce document relevance score.

Invoke this skill to understand colbert_maxsim_operator 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_token_embedding (d1): Dense contextualized vector representation produced per token by an encoder model, preserving token identity and positional context for late interaction scoring.
• colbert_document_encoder (d2): Encoder producing per-token document embeddings from document passages, typically run offline and indexed for fast retrieval against query embeddings.
• colbert_query_encoder (d2): Encoder producing per-token query embeddings from input query tokens, enabling efficient single-pass encoding with late interaction against document tokens.
• colbert_representation_rank (d3): Rank dimension of per-token embeddings determining expressiveness of token-level similarity computation in late interaction framework.

CONSUMERS (what needs this)

colbert_late_interaction, colbert_token_aggregation

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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.)