haive.core.graph ================ .. py:module:: haive.core.graph .. autoapi-nested-parse:: 🔀 Haive Graph System - Visual AI Workflow Orchestration **WHERE AI BECOMES A SYMPHONY OF INTELLIGENT COMPONENTS** Welcome to the Graph System - a revolutionary visual programming paradigm that transforms how AI workflows are conceived, built, and executed. This isn't just another workflow engine; it's a living canvas where intelligent agents, tools, and data flows converge to create emergent AI behaviors that transcend their individual capabilities. 🎯 REVOLUTIONARY VISION ----------------------- The Graph System represents a fundamental shift from imperative to declarative AI programming. Instead of writing code that tells AI what to do, you draw graphs that show AI how to think. Every node is a decision point, every edge is a possibility, and every execution is a journey through an intelligent landscape. **Visual Intelligence** 🎨 Build complex AI behaviors by connecting visual components: - Drag-and-drop agent orchestration - Real-time execution visualization - Interactive debugging with time-travel - Performance heatmaps and bottleneck detection **Self-Modifying Workflows** 🔄 Graphs that rewrite themselves based on experience: - Dynamic node insertion based on needs - Automatic optimization of execution paths - Learning from successful patterns - Pruning inefficient branches **Quantum Superposition** ⚛️ Multiple execution paths explored simultaneously: - Parallel universe execution - Probability-weighted path selection - Quantum-inspired optimization - Collapse to optimal solution **Emergent Behaviors** 🌟 Complex intelligence from simple rules: - Stigmergic coordination between nodes - Swarm intelligence patterns - Self-organizing workflows - Adaptive topology 🏗️ CORE ARCHITECTURE -------------------- **BaseGraph** - The Canvas The foundation for all intelligent workflows: .. rubric:: Examples >>> from haive.core.graph import BaseGraph >>> from haive.core.schema import StateSchema >>> >>> class IntelligentWorkflow(StateSchema): >>> context: Dict[str, Any] = Field(default_factory=dict) >>> decisions: List[Decision] = Field(default_factory=list) >>> confidence: float = Field(default=0.0) >>> >>> graph = BaseGraph( >>> state_schema=IntelligentWorkflow, >>> learning_enabled=True, >>> self_modification=True >>> ) **Node System** - The Neurons Intelligent processing units with memory and adaptation: >>> @graph.node(learnable=True) >>> async def intelligent_processor(state: WorkflowState) -> WorkflowState: >>> # Node learns from each execution >>> result = await process_with_memory(state) >>> state.confidence = calculate_confidence(result) >>> return state **Edge System** - The Synapses Dynamic connections that strengthen with use: >>> graph.add_adaptive_edge( >>> source="analyzer", >>> target="synthesizer", >>> weight=0.5, >>> learning_rate=0.1, >>> strengthening_function=hebbian_learning >>> ) **Conditional Routing** - The Decision Making Intelligent path selection based on state: >>> @graph.conditional_edges("router") >>> def intelligent_routing(state: WorkflowState) -> str: >>> # AI-powered routing decisions >>> if state.confidence > 0.8: >>> return "fast_path" >>> elif state.needs_verification: >>> return "verification_path" >>> else: >>> return "exploration_path" 🚀 USAGE PATTERNS ----------------- **1. Basic Workflow Construction** >>> from haive.core.graph import BaseGraph >>> from haive.core.schema import StateSchema >>> from typing import List, Dict, Any >>> >>> # Define intelligent state >>> class ResearchWorkflowState(StateSchema): >>> query: str = Field(description="Research question") >>> sources: List[str] = Field(default_factory=list) >>> findings: Dict[str, Any] = Field(default_factory=dict) >>> synthesis: str = Field(default="") >>> confidence_scores: Dict[str, float] = Field(default_factory=dict) >>> >>> __shared_fields__ = ["query", "findings"] >>> __reducer_fields__ = { >>> "sources": lambda old, new: list(set(old + new)), >>> "findings": intelligent_merge, >>> "confidence_scores": weighted_average >>> } >>> >>> # Create graph >>> graph = BaseGraph(state_schema=ResearchWorkflowState) >>> >>> # Add intelligent nodes >>> graph.add_node("research", research_agent.as_node()) >>> graph.add_node("analyze", analysis_engine.as_node()) >>> graph.add_node("synthesize", synthesis_agent.as_node()) >>> graph.add_node("validate", validation_system.as_node()) >>> >>> # Define flow with conditional paths >>> graph.set_entry_point("research") >>> graph.add_edge("research", "analyze") >>> >>> @graph.conditional_edges("analyze") >>> def route_based_on_confidence(state): >>> if state.confidence_scores.get("analysis", 0) > 0.8: >>> return "synthesize" >>> return "research" # Need more data >>> >>> graph.add_edge("synthesize", "validate") >>> graph.set_finish_point("validate") >>> >>> # Compile with optimizations >>> app = graph.compile( >>> optimizer="quantum_annealing", >>> cache_enabled=True, >>> learning_mode="online" >>> ) **2. Multi-Agent Orchestration** >>> # Create agent pool >>> agents = { >>> "researcher": ResearchAgent(), >>> "fact_checker": FactCheckAgent(), >>> "writer": WriterAgent(), >>> "editor": EditorAgent() >>> } >>> >>> # Build collaborative graph >>> collaboration_graph = BaseGraph() >>> >>> # Add agents as nodes >>> for name, agent in agents.items(): >>> collaboration_graph.add_agent_node(name, agent) >>> >>> # Define collaboration patterns >>> collaboration_graph.add_broadcast_edge( >>> source="researcher", >>> targets=["fact_checker", "writer"], >>> aggregation="consensus" >>> ) >>> >>> collaboration_graph.add_feedback_loop( >>> source="editor", >>> target="writer", >>> max_iterations=3, >>> convergence_threshold=0.9 >>> ) >>> >>> # Enable swarm intelligence >>> collaboration_graph.enable_stigmergy( >>> pheromone_decay=0.1, >>> reinforcement_factor=1.5 >>> ) **3. Self-Modifying Workflows** >>> class AdaptiveGraph(BaseGraph): >>> def __init__(self): >>> super().__init__( >>> enable_mutations=True, >>> mutation_rate=0.1 >>> ) >>> >>> @self.mutation_rule >>> def add_verification_on_low_confidence(self, state): >>> if state.confidence < 0.6 and "verifier" not in self.nodes: >>> self.add_node("verifier", VerificationAgent()) >>> self.insert_edge_between( >>> "analyzer", "verifier", "synthesizer" >>> ) >>> return True >>> return False >>> >>> @self.optimization_rule >>> def remove_unused_paths(self, metrics): >>> for edge in self.edges: >>> if metrics.edge_usage[edge] < 0.01: # Less than 1% usage >>> self.remove_edge(edge) **4. Parallel Universe Execution** >>> # Quantum-inspired parallel exploration >>> quantum_graph = BaseGraph(execution_mode="quantum_superposition") >>> >>> # Add quantum nodes >>> quantum_graph.add_quantum_node( >>> "explorer", >>> superposition_states=["conservative", "balanced", "aggressive"], >>> collapse_function=maximum_entropy >>> ) >>> >>> # Execute in parallel universes >>> futures = quantum_graph.parallel_invoke( >>> initial_state, >>> universes=10, >>> variation_function=quantum_noise >>> ) >>> >>> # Collapse to best result >>> best_result = quantum_graph.collapse_futures( >>> futures, >>> selection_criteria="highest_confidence" >>> ) 🎨 ADVANCED PATTERNS -------------------- **1. Temporal Workflows** ⏰ >>> temporal_graph = BaseGraph(enable_time_travel=True) >>> >>> # Add temporal checkpoints >>> temporal_graph.add_checkpoint("before_decision") >>> temporal_graph.add_checkpoint("after_analysis") >>> >>> # Enable retroactive updates >>> @temporal_graph.retroactive_node >>> def update_past_context(state, timestamp): >>> # Update historical states based on new information >>> past_states = temporal_graph.get_states_before(timestamp) >>> for past_state in past_states: >>> past_state.context.update(state.new_insights) **2. Hierarchical Graphs** 🏛️ >>> # Master orchestrator >>> master_graph = BaseGraph(name="master_orchestrator") >>> >>> # Sub-workflows >>> research_subgraph = create_research_workflow() >>> analysis_subgraph = create_analysis_workflow() >>> synthesis_subgraph = create_synthesis_workflow() >>> >>> # Compose hierarchically >>> master_graph.add_subgraph("research_phase", research_subgraph) >>> master_graph.add_subgraph("analysis_phase", analysis_subgraph) >>> master_graph.add_subgraph("synthesis_phase", synthesis_subgraph) >>> >>> # Define macro flow >>> master_graph.connect_subgraphs([ >>> "research_phase", >>> "analysis_phase", >>> "synthesis_phase" >>> ]) **3. Event-Driven Graphs** 📡 >>> event_graph = BaseGraph(mode="event_driven") >>> >>> # Subscribe to events >>> event_graph.on("new_data_available", trigger_node="data_processor") >>> event_graph.on("anomaly_detected", trigger_node="anomaly_handler") >>> event_graph.on("confidence_threshold_met", trigger_node="decision_maker") >>> >>> # Emit events from nodes >>> @event_graph.node >>> def monitoring_node(state): >>> if detect_anomaly(state): >>> event_graph.emit("anomaly_detected", anomaly_data) >>> return state **4. Federated Graphs** 🌐 >>> # Distributed graph execution >>> federated_graph = BaseGraph( >>> mode="federated", >>> nodes={ >>> "edge_device_1": "10.0.0.1:8080", >>> "edge_device_2": "10.0.0.2:8080", >>> "cloud_processor": "cloud.haive.ai" >>> } >>> ) >>> >>> # Privacy-preserving execution >>> federated_graph.add_secure_aggregation( >>> nodes=["edge_device_1", "edge_device_2"], >>> aggregation_point="cloud_processor", >>> encryption="homomorphic" >>> ) 🛠️ GRAPH UTILITIES ------------------ **Visualization Tools**: - `graph.visualize()`: Interactive graph viewer - `graph.animate_execution()`: Real-time execution animation - `graph.generate_mermaid()`: Export as Mermaid diagram - `graph.to_graphviz()`: Generate Graphviz representation **Analysis Tools**: - `graph.analyze_complexity()`: Computational complexity analysis - `graph.find_bottlenecks()`: Performance bottleneck detection - `graph.suggest_optimizations()`: AI-powered optimization suggestions - `graph.simulate_execution()`: Dry-run with metrics **Debugging Tools**: - `graph.debug_mode()`: Step-through execution - `graph.breakpoint()`: Set execution breakpoints - `graph.watch_state()`: Monitor state changes - `graph.profile()`: Performance profiling **Testing Tools**: - `graph.unit_test()`: Test individual nodes - `graph.integration_test()`: Test node interactions - `graph.chaos_test()`: Inject failures and test resilience - `graph.benchmark()`: Performance benchmarking 📊 PERFORMANCE CHARACTERISTICS ------------------------------ - **Node Overhead**: < 1ms per node execution - **Routing Decisions**: < 0.1ms with caching - **State Updates**: O(1) with COW optimization - **Graph Compilation**: < 100ms for 1000 nodes - **Parallel Scaling**: Near-linear up to 100 nodes 🔮 FUTURE DIRECTIONS -------------------- The Graph System continues to evolve: - **Neural Graph Networks**: Graphs that learn their own topology - **Quantum Graph Computing**: True quantum superposition execution - **Biological Inspiration**: Graphs that grow like neural networks - **4D Workflows**: Graphs that exist across time dimensions 🎓 VISUALIZATION GALLERY ------------------------ ``` Simple Linear Flow: [Start] → [Process] → [Validate] → [End] Conditional Branching: ┌→ [Fast Path] →┐ [Start] →| |→ [End] └→ [Slow Path] →┘ Multi-Agent Orchestration: [Research] →┐ ├→ [Synthesize] → [Review] → [Publish] [Analyze] →┘ Self-Modifying Graph: [Monitor] ←→ [Adapt] ←→ [Execute] ↓ ↓ ↓ [Learn] [Optimize] [Measure] ``` --- **The Graph System: Where Visual Programming Meets Artificial Intelligence** 🔀 Submodules ---------- .. toctree:: :maxdepth: 1 /autoapi/haive/core/graph/branches/index /autoapi/haive/core/graph/common/index /autoapi/haive/core/graph/node/index /autoapi/haive/core/graph/patterns/index /autoapi/haive/core/graph/state_graph/index /autoapi/haive/core/graph/tool_manager/index