haive.core.schema.composer

🎼 Schema Composer - Dynamic AI State Architecture Builder

THE ULTIMATE SCHEMA ORCHESTRATION PLATFORM

Welcome to the Schema Composer - the revolutionary system that transforms AI component analysis into dynamic, perfectly-tuned state schemas. This isn’t just code generation; it’s an intelligent architecture platform that understands your AI components and creates optimal state structures that adapt and evolve with your system’s needs.

🎯 ARCHITECTURAL REVOLUTION

The Schema Composer represents a paradigm shift in AI state architecture design. Instead of manual schema definition, every AI component becomes a building block that contributes to an intelligent, self-optimizing state structure:

🧠 Intelligent Analysis: Deep introspection of engines, tools, and components ⚡ Dynamic Generation: Real-time schema creation based on actual requirements 🔄 Adaptive Evolution: Schemas that grow and optimize as components change 📊 Performance Optimization: Automatic field ordering and memory optimization 🎯 Type-Safe Composition: Guaranteed type safety across all component integrations

🌟 CORE INNOVATIONS

1. Component-Driven Architecture 🏗️

Schemas emerge naturally from your AI components:

Examples

>>> from haive.core.schema.composer import SchemaComposer
>>> from haive.core.engine.aug_llm import AugLLMConfig
>>> from haive.core.engine.retriever import VectorStoreRetrieverConfig
>>>
>>> # Start with a blank canvas
>>> composer = SchemaComposer("IntelligentAgentState")
>>>
>>> # Add components - schema builds automatically
>>> llm_engine = AugLLMConfig(model="gpt-4", tools=[calculator, web_search])
>>> retriever = VectorStoreRetrieverConfig(provider="pinecone")
>>>
>>> # Intelligent analysis and integration
>>> composer.analyze_engine(llm_engine)      # Adds: messages, tool_calls, structured_output
>>> composer.analyze_engine(retriever)       # Adds: documents, retrieval_metadata
>>> composer.add_computed_field("insights", derive_insights_from_context)
>>>
>>> # Generate optimized schema
>>> AgentState = composer.build()
>>>
>>> # Result: Perfectly tailored schema with all necessary fields
>>> agent = Agent(state_schema=AgentState)
2. Multi-Engine Orchestration 🎭

Seamless integration of multiple AI engines:

>>> # Complex multi-engine agent
>>> composer = SchemaComposer("MultiEngineState")
>>>
>>> # Each engine contributes its requirements
>>> reasoning_engine = AugLLMConfig(model="gpt-4", system_message="Think step by step")
>>> creative_engine = AugLLMConfig(model="claude-3", temperature=0.9)
>>> tool_engine = ToolEngineConfig(tools=[api_caller, database_query])
>>> vector_engine = VectorStoreRetrieverConfig(provider="weaviate")
>>>
>>> # Intelligent conflict resolution and optimization
>>> composer.add_engines([reasoning_engine, creative_engine, tool_engine, vector_engine])
>>>
>>> # Automatic field deduplication and optimization
>>> composer.enable_smart_optimization()
>>>
>>> # Generate unified schema
>>> UnifiedState = composer.build()
3. Adaptive Field Management 🧩

Intelligent field composition with conflict resolution:

>>> composer = SchemaComposer("AdaptiveState")
>>>
>>> # Fields adapt to component requirements
>>> composer.add_conditional_field(
>>> "conversation_memory",
>>> condition=lambda engines: any(engine.supports_conversation for engine in engines),
>>> field_type=List[BaseMessage],
>>> default_factory=list
>>> )
>>>
>>> # Smart field merging
>>> composer.add_merge_strategy(
>>> "context_data",
>>> strategy="semantic_merge",
>>> conflict_resolution="preserve_both"
>>> )
>>>
>>> # Performance-optimized field ordering
>>> composer.optimize_field_layout(strategy="access_frequency")
4. Component Intelligence 🔮

Deep analysis of AI components for optimal schema design:

>>> # Intelligent component analysis
>>> class ComponentAnalyzer:
>>> def analyze_llm_engine(self, engine: AugLLMConfig) -> FieldMap:
>>> fields = {}
>>>
>>> # Required fields based on engine configuration
>>> if engine.tools:
>>> fields["tool_calls"] = List[ToolCall]
>>> fields["tool_results"] = List[ToolResult]
>>>
>>> if engine.structured_output_model:
>>> fields["structured_outputs"] = List[engine.structured_output_model]
>>>
>>> if engine.streaming:
>>> fields["stream_chunks"] = List[StreamChunk]
>>>
>>> # Memory requirements
>>> fields["conversation_history"] = List[BaseMessage]
>>>
>>> return FieldMap(fields)
>>>
>>> # Use in composer
>>> composer.add_analyzer(ComponentAnalyzer())
>>> composer.analyze_all_components()

🎯 ADVANCED FEATURES

Schema Evolution Management 🔄

>>> # Schemas that evolve with your system
>>> composer = SchemaComposer("EvolvingState")
>>> composer.enable_evolution_tracking()
>>>
>>> # Track changes over time
>>> version_1 = composer.build()
>>>
>>> # Add new components
>>> composer.add_engine(new_vision_engine)
>>> composer.add_field("image_analysis", ImageAnalysisResult)
>>>
>>> # Intelligent migration path
>>> version_2 = composer.build()
>>> migration = composer.create_migration(version_1, version_2)
>>>
>>> # Automatic data migration
>>> migrated_state = migration.apply(old_state_instance)

Performance Optimization ⚡

>>> # Automatic performance tuning
>>> composer = SchemaComposer("OptimizedState")
>>>
>>> # Field access pattern analysis
>>> composer.analyze_access_patterns(historical_data)
>>>
>>> # Memory layout optimization
>>> composer.optimize_memory_layout(strategy="cache_locality")
>>>
>>> # Lazy loading for large fields
>>> composer.configure_lazy_loading([
>>> "large_documents",
>>> "conversation_history",
>>> "tool_execution_logs"
>>> ])

Multi-Agent State Coordination 🤝

>>> from haive.core.schema.composer import MultiAgentComposer
>>>
>>> # Coordinate schemas across multiple agents
>>> coordinator = MultiAgentComposer()
>>>
>>> # Add agent requirements
>>> coordinator.add_agent_schema("researcher", research_requirements)
>>> coordinator.add_agent_schema("analyst", analysis_requirements)
>>> coordinator.add_agent_schema("writer", writing_requirements)
>>>
>>> # Automatic field sharing analysis
>>> shared_fields = coordinator.analyze_shared_requirements()
>>>
>>> # Generate coordinated schemas
>>> schemas = coordinator.build_coordinated_schemas()

🏗️ COMPOSITION PATTERNS

Layered Composition 🏛️

>>> # Build complex schemas in layers
>>> base_composer = SchemaComposer("BaseLayer")
>>> base_composer.add_essential_fields()
>>> base_schema = base_composer.build()
>>>
>>> # Add capability layers
>>> capability_composer = SchemaComposer("CapabilityLayer", base=base_schema)
>>> capability_composer.add_tools([calculator, web_search])
>>> capability_composer.add_memory_systems([vector_store, conversation_buffer])
>>> enhanced_schema = capability_composer.build()
>>>
>>> # Add performance layer
>>> performance_composer = SchemaComposer("PerformanceLayer", base=enhanced_schema)
>>> performance_composer.add_monitoring_fields()
>>> performance_composer.add_optimization_metadata()
>>> final_schema = performance_composer.build()

Plugin Architecture 🔌

>>> # Extensible composition with plugins
>>> composer = SchemaComposer("PluggableState")
>>>
>>> # Core plugins
>>> composer.load_plugin(ConversationPlugin())
>>> composer.load_plugin(ToolExecutionPlugin())
>>> composer.load_plugin(CostTrackingPlugin())
>>>
>>> # Custom domain plugins
>>> composer.load_plugin(MedicalKnowledgePlugin())
>>> composer.load_plugin(LegalResearchPlugin())
>>>
>>> # Automatic plugin integration
>>> final_schema = composer.build_with_plugins()

Template-Based Composition 📋

>>> # Predefined composition templates
>>> from haive.core.schema.composer.templates import (
>>> ChatBotTemplate, RAGTemplate, MultiAgentTemplate
>>> )
>>>
>>> # Quick composition with templates
>>> chatbot_composer = SchemaComposer.from_template(
>>> ChatBotTemplate,
>>> customizations={
>>> "enable_memory": True,
>>> "max_conversation_length": 50,
>>> "include_sentiment_analysis": True
>>> }
>>> )
>>>
>>> rag_composer = SchemaComposer.from_template(
>>> RAGTemplate,
>>> customizations={
>>> "retrieval_strategy": "hybrid",
>>> "max_documents": 10,
>>> "include_citation_tracking": True
>>> }
>>> )

📊 INTELLIGENT ANALYSIS

Component Introspection 🔍

>>> # Deep component analysis
>>> analyzer = ComponentIntrospector()
>>>
>>> # Analyze engine capabilities
>>> llm_analysis = analyzer.analyze_llm_requirements(gpt4_engine)
>>> tool_analysis = analyzer.analyze_tool_requirements(tool_set)
>>> memory_analysis = analyzer.analyze_memory_requirements(vector_store)
>>>
>>> # Generate compatibility matrix
>>> compatibility = analyzer.check_component_compatibility([
>>> llm_analysis, tool_analysis, memory_analysis
>>> ])
>>>
>>> # Automatic schema recommendations
>>> recommendations = analyzer.recommend_schema_structure(compatibility)

Field Optimization 🎯

>>> # Intelligent field management
>>> optimizer = FieldOptimizer()
>>>
>>> # Analyze field usage patterns
>>> usage_patterns = optimizer.analyze_field_usage(historical_states)
>>>
>>> # Optimize field types
>>> optimized_types = optimizer.suggest_type_optimizations(usage_patterns)
>>>
>>> # Memory layout optimization
>>> layout = optimizer.optimize_memory_layout(field_access_patterns)
>>>
>>> # Apply optimizations
>>> composer.apply_optimizations(optimized_types, layout)

🔒 ENTERPRISE FEATURES

  • Schema Governance: Approval workflows for schema changes

  • Version Control: Complete schema evolution tracking

  • Performance Monitoring: Real-time schema performance analytics

  • Compliance Integration: Automatic compliance field injection

  • Multi-tenancy: Tenant-specific schema customization

  • Backup & Recovery: Schema and state backup strategies

🎓 BEST PRACTICES

  1. Start Simple: Begin with basic composition, add complexity gradually

  2. Analyze Components: Use introspection tools to understand requirements

  3. Plan for Evolution: Design schemas that can adapt to new components

  4. Monitor Performance: Track schema performance in production

  5. Use Templates: Leverage predefined patterns for common use cases

  6. Test Compositions: Validate generated schemas with real data

  7. Document Changes: Maintain clear records of schema evolution

🚀 GETTING STARTED

>>> from haive.core.schema.composer import SchemaComposer
>>> from haive.core.engine.aug_llm import AugLLMConfig
>>>
>>> # 1. Create composer for your use case
>>> composer = SchemaComposer("MyAgentState")
>>>
>>> # 2. Add your AI components
>>> llm = AugLLMConfig(model="gpt-4", tools=[calculator])
>>> composer.analyze_engine(llm)
>>>
>>> # 3. Add custom fields if needed
>>> composer.add_field("custom_data", Dict[str, Any], default_factory=dict)
>>>
>>> # 4. Build optimized schema
>>> MyState = composer.build()
>>>
>>> # 5. Use in your agent
>>> class MyAgent(Agent):
>>> state_schema = MyState
>>>
>>> # The schema automatically includes:
>>> # - messages (from LLM requirements)
>>> # - tool_calls (from tool configuration)
>>> # - custom_data (your addition)
>>> # - Plus optimization and type safety!

Submodules