haive.core.schema.composer ========================== .. py:module:: haive.core.schema.composer .. autoapi-nested-parse:: 🎼 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: .. rubric:: 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! 🎼 COMPOSITION GALLERY ---------------------- **Available Composers**: - `SchemaComposer` - Core dynamic composition - `MultiAgentComposer` - Multi-agent coordination - `EngineComposerMixin` - Engine-specific composition - `TemplateComposer` - Template-based generation - `PerformanceComposer` - Performance-optimized schemas **Analysis Tools**: - `ComponentIntrospector` - Deep component analysis - `FieldOptimizer` - Intelligent field optimization - `CompatibilityAnalyzer` - Component compatibility checking - `PerformanceAnalyzer` - Schema performance analysis --- **Schema Composer: Where AI Components Become Perfect State Architectures** 🎼 Submodules ---------- .. toctree:: :maxdepth: 1 /autoapi/haive/core/schema/composer/engine/index /autoapi/haive/core/schema/composer/field/index /autoapi/haive/core/schema/composer/schema_composer/index