"""Create reasoning_engine
Generated by Phi-Octopus Eden
2025-11-06 06:14:30.790968
"""

```python
from typing import List, Dict, Any


class KnowledgeBase:
    def __init__(self):
        self.knowledge = {}

    def add_knowledge(self, key: str, value: Any) -> None:
        """Add or update knowledge in the base."""
        self.knowledge[key] = value

    def get_knowledge(self, key: str) -> Any:
        """Retrieve knowledge from the base if it exists."""
        return self.knowledge.get(key)


class ReasoningEngine:
    def __init__(self):
        self.knowledge_base = KnowledgeBase()

    def update_knowledge(self, new_knowledge: Dict[str, Any]) -> None:
        """Update or add multiple pieces of knowledge at once."""
        for key, value in new_knowledge.items():
            self.knowledge_base.add_knowledge(key, value)

    def infer_new_fact(self, known_facts: List[Dict[str, Any]]) -> str:
        """
        Infer a new fact based on existing known facts.
        
        Parameters:
        - known_facts (List[Dict[str, Any]]): A list of dictionaries containing known facts.

        Returns:
        - str: The inferred fact as a string.
        """
        if not known_facts:
            return "No known facts to infer from."
        
        # Example inference logic
        for fact in known_facts:
            if 'temperature' in fact and 'humidity' in fact:
                temp = fact['temperature']
                humidity = fact['humidity']
                if temp > 30 and humidity < 40:  # Simplified condition, replace with actual reasoning
                    return f"High temperature ({temp}°C) with low humidity ({humidity}%)."
        
        return "Inference not possible based on available facts."

# Example usage:
reasoning_engine = ReasoningEngine()
known_facts = [
    {'temperature': 32, 'humidity': 35},
    {'temperature': 28, 'humidity': 45}
]
reasoning_engine.update_knowledge({'sensors_1_data': known_facts})
inferred_fact = reasoning_engine.infer_new_fact(known_facts)
print(inferred_fact)  # Should print the inferred fact based on example logic
```
```python
# Running example usage code
reasoning_engine = ReasoningEngine()
known_facts = [
    {'temperature': 32, 'humidity': 35},
    {'temperature': 28, 'humidity': 45}
]
reasoning_engine.update_knowledge({'sensors_1_data': known_facts})
inferred_fact = reasoning_engine.infer_new_fact(known_facts)
print(inferred_fact)  # Should print: "High temperature (32°C) with low humidity (35%)."
```