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T3: Global Component Graph Design

Source: T3_ Global Component Graph Design.pdf Status: 🚧 Extraction in Progress Roadmap: Technical Architecture Roadmap

Overview​

The Global Component Graph enables discovery, relationship tracking, and intelligent composition of components across the entire ecosystem. This specification defines the graph structure, query language, and composition algorithms.

Graph Architecture​

Graph Structure​

Node Types

  • Component Nodes: Represent components
  • Version Nodes: Represent component versions
  • Interface Nodes: Represent component interfaces
  • Dependency Nodes: Represent dependencies
  • Category Nodes: Represent categories/tags

Edge Types

  • DEPENDS_ON: Component dependency
  • IMPLEMENTS: Interface implementation
  • VERSION_OF: Version relationship
  • COMPOSES: Composition relationship
  • SIMILAR_TO: Similarity relationship
  • USED_WITH: Usage relationship

Graph Schema​

pub struct ComponentGraph {
    nodes: HashMap<NodeId, GraphNode>,
    edges: HashMap<EdgeId, GraphEdge>,
    index: GraphIndex,
}

pub enum GraphNode {
    Component(ComponentNode),
    Version(VersionNode),
    Interface(InterfaceNode),
    Dependency(DependencyNode),
    Category(CategoryNode),
}

pub struct ComponentNode {
    pub id: ComponentId,
    pub metadata: ComponentMetadata,
    pub versions: Vec<Version>,
    pub interfaces: Vec<InterfaceId>,
    pub dependencies: Vec<DependencyId>,
}

Graph Database​

Storage Backend​

Options

  • Graph database (Neo4j, ArangoDB)
  • Relational database with graph extensions
  • Custom graph storage

Graph Storage Schema

-- Nodes table
CREATE TABLE nodes (
    id UUID PRIMARY KEY,
    type VARCHAR(50) NOT NULL,
    properties JSONB,
    created_at TIMESTAMP,
    updated_at TIMESTAMP
);

-- Edges table
CREATE TABLE edges (
    id UUID PRIMARY KEY,
    source_id UUID REFERENCES nodes(id),
    target_id UUID REFERENCES nodes(id),
    relationship_type VARCHAR(50),
    properties JSONB,
    created_at TIMESTAMP
);

-- Indexes
CREATE INDEX idx_nodes_type ON nodes(type);
CREATE INDEX idx_edges_source ON edges(source_id);
CREATE INDEX idx_edges_target ON edges(target_id);
CREATE INDEX idx_edges_type ON edges(relationship_type);

Graph Operations​

Node Operations

pub trait GraphDatabase {
    fn create_node(&mut self, node: GraphNode) -> Result<NodeId>;
    fn get_node(&self, id: &NodeId) -> Option<GraphNode>;
    fn update_node(&mut self, id: &NodeId, properties: Properties) -> Result<()>;
    fn delete_node(&mut self, id: &NodeId) -> Result<()>;
}

Edge Operations

pub trait GraphDatabase {
    fn create_edge(&mut self, edge: GraphEdge) -> Result<EdgeId>;
    fn get_edges(&self, node_id: &NodeId, direction: Direction) -> Vec<GraphEdge>;
    fn delete_edge(&mut self, id: &EdgeId) -> Result<()>;
}

Graph Query Language​

Query Syntax​

Basic Queries

// Find component by ID
MATCH (c:Component {id: "component-id"})
RETURN c

// Find all dependencies
MATCH (c:Component {id: "component-id"})-[:DEPENDS_ON]->(d:Component)
RETURN d

// Find components by tag
MATCH (c:Component)-[:HAS_TAG]->(t:Tag {name: "tag-name"})
RETURN c

Complex Queries

// Find compatible components
MATCH (c1:Component)-[:IMPLEMENTS]->(i:Interface)
MATCH (c2:Component)-[:REQUIRES]->(i)
WHERE c1.id <> c2.id
RETURN c1, c2, i

// Find composition paths
MATCH path = (start:Component {id: "start-id"})-[:COMPOSES*]->(end:Component {id: "end-id"})
RETURN path

Query API​

pub trait GraphQuery {
    fn query(&self, query: Query) -> Result<QueryResult>;
    fn execute_cypher(&self, cypher: &str) -> Result<QueryResult>;
}

pub struct Query {
    pub pattern: QueryPattern,
    pub filters: Vec<Filter>,
    pub projections: Vec<Projection>,
    pub ordering: Option<Ordering>,
    pub limit: Option<u32>,
}

Discovery System​

Search Interface​

Search Query

pub struct SearchQuery {
    pub text: Option<String>,
    pub tags: Vec<String>,
    pub category: Option<Category>,
    pub interface: Option<InterfaceId>,
    pub dependencies: Vec<ComponentId>,
    pub filters: Vec<SearchFilter>,
}

pub enum SearchFilter {
    Rating { min: f64 },
    Downloads { min: u64 },
    Updated { since: DateTime<Utc> },
    Compatible { with: ComponentId },
}

Search Implementation

pub trait ComponentDiscovery {
    fn search(&self, query: &SearchQuery) -> Vec<ComponentResult>;
    fn fuzzy_search(&self, text: &str) -> Vec<ComponentResult>;
    fn semantic_search(&self, description: &str) -> Vec<ComponentResult>;
}

Recommendation Engine​

Recommendation Algorithms

  • Collaborative filtering
  • Content-based filtering
  • Hybrid approaches
  • Graph-based recommendations

Recommendation API

pub trait RecommendationEngine {
    fn recommend(&self, context: &RecommendationContext) -> Vec<Recommendation>;
    fn recommend_similar(&self, component_id: &ComponentId) -> Vec<ComponentResult>;
    fn recommend_for_use_case(&self, use_case: &UseCase) -> Vec<ComponentResult>;
}

Similarity Matching​

Similarity Metrics

  • Interface similarity
  • Functionality similarity
  • Usage pattern similarity
  • Dependency similarity

Similarity Algorithm

pub trait SimilarityMatcher {
    fn calculate_similarity(&self, c1: &ComponentId, c2: &ComponentId) -> f64;
    fn find_similar(&self, component_id: &ComponentId, threshold: f64) -> Vec<SimilarComponent>;
}

Composition Engine​

Composition Algorithms​

Automatic Composition

pub trait CompositionEngine {
    fn compose(&self, goal: &CompositionGoal, available: &[ComponentId]) -> Result<Composition>;
    fn find_composition_path(&self, start: &ComponentId, end: &ComponentId) -> Option<CompositionPath>;
}

Composition Strategies

  1. Greedy: Select best components at each step
  2. Optimal: Find optimal composition (may be expensive)
  3. Heuristic: Use heuristics for faster composition
  4. Learning-based: Learn from past compositions

Dependency Resolution​

Resolution Algorithm

pub struct DependencyResolver {
    graph: ComponentGraph,
}

impl DependencyResolver {
    pub fn resolve(&self, component_id: &ComponentId) -> Result<ResolutionPlan> {
        // 1. Build dependency tree
        // 2. Detect conflicts
        // 3. Resolve conflicts
        // 4. Generate resolution plan
    }

    fn detect_conflicts(&self, dependencies: &[Dependency]) -> Vec<Conflict> {
        // Conflict detection logic
    }
}

Conflict Detection and Resolution​

Conflict Types

  • Version conflicts
  • Interface conflicts
  • Resource conflicts
  • Dependency conflicts

Resolution Strategies

  • Version selection (latest compatible)
  • Interface adaptation
  • Alternative component selection
  • Manual resolution required

Optimization Strategies​

Optimization Goals

  • Minimize component count
  • Maximize performance
  • Minimize resource usage
  • Maximize reliability

Optimization Algorithms

pub trait CompositionOptimizer {
    fn optimize(&self, composition: &Composition, goals: &[OptimizationGoal]) -> OptimizedComposition;
}

Graph Visualization​

Visualization Tools​

Graph Visualization API

pub trait GraphVisualizer {
    fn visualize(&self, graph: &ComponentGraph, layout: Layout) -> Visualization;
    fn visualize_subgraph(&self, node_ids: &[NodeId]) -> Visualization;
    fn export(&self, visualization: &Visualization, format: ExportFormat) -> Result<Vec<u8>>;
}

Layout Algorithms

  • Force-directed layout
  • Hierarchical layout
  • Circular layout
  • Custom layouts

Distributed Graph​

Federation Support​

Federation Architecture

pub struct FederatedGraph {
    local_graph: ComponentGraph,
    remote_graphs: Vec<RemoteGraph>,
    federation_protocol: FederationProtocol,
}

pub trait FederationProtocol {
    fn query_remote(&self, graph_id: &GraphId, query: &Query) -> Result<QueryResult>;
    fn sync(&self, graph_id: &GraphId) -> Result<SyncResult>;
}

Graph Synchronization​

Sync Strategies

  • Full sync
  • Incremental sync
  • Event-driven sync
  • On-demand sync

Performance Requirements​

Scalability Targets​

  • Nodes: Support millions of components
  • Edges: Support billions of relationships
  • Queries: Sub-second response time
  • Updates: Real-time graph updates

Optimization Techniques​

  • Graph indexing
  • Query caching
  • Result pagination
  • Lazy loading
  • Graph partitioning

Implementation Status​

📋 Planned​

  • Graph infrastructure
  • Graph query language
  • Relationship modeling
  • Graph visualization tools
  • Component search and filtering
  • Recommendation engine
  • Similarity matching
  • Usage analytics
  • Automatic composition algorithms
  • Dependency resolution
  • Conflict detection and resolution
  • Optimization strategies
  • Distributed graph support
  • Graph synchronization

Note: This specification is extracted from the OpenKor T3 document. Detailed graph algorithms may need manual review from the source PDF.