haive.core.utils

🛠️ Utils Module - Intelligent Utility Toolkit Revolution

THE SWISS ARMY KNIFE OF AI DEVELOPMENT EXCELLENCE

Welcome to the Utils Module - the revolutionary utility intelligence platform that transforms mundane utility functions into a sophisticated development acceleration system. This isn’t just another utils library; it’s an intelligent toolkit consciousness that anticipates developer needs, automates repetitive tasks, and enhances productivity through smart automation and adaptive helpers.

⚡ REVOLUTIONARY UTILITY INTELLIGENCE

The Utils Module represents a paradigm shift from static utility functions to intelligent, adaptive development accelerators that evolve with your workflow:

🧠 Intelligent Tool Discovery: Auto-discovery and introspection of framework components 🔄 Adaptive Pydantic Integration: Smart model manipulation with type safety ⚡ OpenAI-Compliant Naming: Intelligent name sanitization and validation 📊 Development Workflow Enhancement: Productivity-boosting utilities and helpers 🎯 Framework Integration: Seamless integration utilities for all Haive components

🌟 CORE UTILITY INNOVATIONS

1. Intelligent Pydantic Utilities 🚀

Revolutionary model manipulation that thinks and adapts:

Examples

>>> from haive.core.utils import pydantic_utils
>>> from haive.core.utils.pydantic_utils import PydanticModelAnalyzer
>>>
>>> # Intelligent model serialization with context awareness
>>> class AgentConfig(BaseModel):
>>> name: str
>>> temperature: float
>>> tools: List[str]
>>> metadata: Dict[str, Any]
>>>
>>> config = AgentConfig(
>>> name="intelligent_agent",
>>> temperature=0.7,
>>> tools=["calculator", "web_search"],
>>> metadata={"version": "2.0", "type": "research"}
>>> )
>>>
>>> # Smart serialization with intelligent filtering
>>> serialized = pydantic_utils.model_to_dict(
>>> config,
>>> exclude_sensitive=True,
>>> include_computed=True,
>>> optimization_mode="api_ready"
>>> )
>>>
>>> # Intelligent model analysis and optimization
>>> analyzer = PydanticModelAnalyzer()
>>> analysis = analyzer.analyze_model_structure(AgentConfig)
>>>
>>> print(f"Model complexity: {analysis.complexity_score}")
>>> print(f"Serialization efficiency: {analysis.serialization_efficiency}")
>>> print(f"Optimization suggestions: {analysis.optimization_suggestions}")
>>>
>>> # Auto-optimize model for specific use cases
>>> optimized_model = analyzer.optimize_for_use_case(
>>> AgentConfig,
>>> use_case="high_frequency_api",
>>> performance_target="sub_millisecond"
>>> )
>>>
>>> # Intelligent model merging and transformation
>>> merged_config = pydantic_utils.intelligent_merge_models(
>>> base_model=config,
>>> override_model=production_overrides,
>>> merge_strategy="performance_optimized",
>>> conflict_resolution="weighted_priority"
>>> )

For complete examples and advanced patterns, see the documentation.

Submodules