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Multimodal

Maturity: Experimental

These components provide structural scaffolding for multi-modal pipelines but do not include trained models or real cross-modal alignment. They operate on metadata and text representations of other modalities.

Two components providing structural patterns for cross-modal orchestration.

Overview

The Multimodal cluster provides architectural patterns for handling inputs from multiple modalities. multimodal orchestrates cross-modal queries by routing to modality-specific components. adapt_trm can compare and store numeric feature sequences once upstream components have produced them, but it is not a trained transformer or cross-modal embedding model.

Current limitations: no embedded multi-modal models are bundled. Cross-modal "reasoning" operates on text descriptions or supplied feature vectors of non-text modalities rather than raw pixel/audio data. For production image, audio, or shared embedding tasks, use a purpose-built external model or adapter, then let G6 orchestrate, verify, persist, and retrieve the resulting features.

Components

Component Description MCP Tools
multimodal Cross-modal reasoning and orchestration --
adapt_trm Deterministic sequence embedding, grid transforms, persistence, search, and similarity over supplied numeric features 25

Architecture

graph TD
    MM[multimodal] --> TRM[adapt_trm]
    TRM --> TEXT[Numeric text features]
    TRM --> IMG[Numeric image features]
    TRM --> AUDIO[Numeric audio features]
    MM --> CTX[Context & Retrieval]
    MM --> CREAT[Creative & Media]
    AGENTS[Agents & LLM] --> MM

Key Patterns

Modality Abstraction. Components operate on typed inputs that tag data with its modality type. adapt_trm does not select modality-specific encoders; upstream image, audio, and text components must produce numeric features before TRM comparison or storage.

Local Deterministic Utilities. adapt_trm is pure Python for its core and MCP paths, with optional Torch helpers for tensor acceleration. It is useful for lightweight sequence embeddings, grid comparisons, persistent examples, nearest-neighbor lookup, and clustering, not for learned semantic alignment.

Cross-Modal Queries. Cross-modal retrieval requires an external or separately trained embedding model that maps different modalities into a shared space. G6 can orchestrate that model, validate preprocessing, store features, and retrieve nearest neighbours, but adapt_trm alone does not create the shared semantic space.