0-3-6-understand-mmr009

name	0.3.6-understand-mmr009
description	["0.3.6"]
Multi-modal index	index structure supporting heterogeneous chunk types — text vectors, image features, table

understand-mmr009

CALL NUMBER: deep_retrieval_augmented_.mmr009 DEFINITION: Multi-modal index: index structure supporting heterogeneous chunk types — text vectors, image features, table embeddings — with metadata routing for modality-aware retrieval.

Invoke this skill to understand mmr009 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_retrieval_augmented_

• mmr001 (d1): Modality type: classification of data representation forms a retrieval system handles — text, image, table, code, audio, video, or structured data.
• mmr002 (d1): Multi-modal embedding model: neural network architecture that projects content from different modalities into a shared vector space enabling cross-modal similarity search.
• mmr005 (d2): Non-text chunk processor: component that parses, extracts, and encodes non-text content from documents — handling image pixels, table cells, code syntax, or audio waveforms into retrievable representations.
• mmr012 (d2): Cross-modal similarity metric: distance or similarity measure applicable across modality boundaries — enabling comparison of text query vectors against image embeddings, for example.

CONSUMERS (what needs this)

mmr015, mmr026, mmr027, naive_rag

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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.)