# KCG Indexing with The Graph Subgraph

To achieve decentralized, scalable, and performant indexing for the Knowledge Cache Graph (KCG), we propose leveraging The Graph Subgraph architecture as an alternative to centralized graph databases like Neo4j.

# Storage Layer: Arweave

All KCG entries (entities, relations, ontologies) are stored as immutable JSON-LD documents on Arweave.
Each entry is identified by its TXID and enriched with metadata tags (e.g., Type, Relation, Ontology).

# Index & Query Layer: The Graph Subgraph

A custom Subgraph mapping is created to parse Arweave transactions based on relevant tags.
The mapping script extracts entity and relation data from JSON-LD payloads.
Subgraph Entities include:
- KCGEntity: id, type, attributes.
- KCGRelation: source, target, relationType, context.
Relationships are maintained via content-addressed links (Arweave TXIDs).

# GraphQL API Interface

The Subgraph exposes a standardized GraphQL API for querying the KCG.

Example query:

query {
  kcgEntities(where: {type: "Person"}) {
    id
    attributes { key, value }
    relations { target { id }, relationType }
  }
}

This enables Tiny LLMs and DoD Agents to perform semantic searches efficiently.

# Sync & Update Flow

The Graph Indexers continuously monitor Arweave for new transactions matching KCG patterns.
Updates to the knowledge graph are reflected in real-time within the Subgraph index.

# Advantages Over Centralized Graph Databases

Aspect	The Graph Subgraph	Neo4j
Immutable Data Indexing	Native support for Arweave Data Sources	Requires manual synchronization
Query Interface	Decentralized GraphQL API	Proprietary Cypher Query
Scalability	Distributed Indexer Network	Requires federated custom deployment
Decentralization	Yes (hosted or decentralized subgraphs)	No (centralized unless custom federated)
Speed of Access	Low latency for indexed queries	Fast on dedicated infrastructure
Standards Compliance	OpenGraphQL Schema, Arweave Integration	Proprietary database format

# Architecture

Immutable Storage: Arweave for permanent knowledge storage.
Decentralized Index & Query: The Graph Subgraph for efficient search and retrieval.
Consumption Layer: Tiny LLMs and DoD Agents interact via GraphQL.
Optional Caching: Gateways can implement caching for ultra-low latency.

This approach ensures a fully decentralized, scalable, and performant knowledge indexing solution, aligning with the KCG+CAG principles of openness, permanence, and efficiency.