"""Create reasoning_engine
Generated by Phi-Octopus Eden
2025-11-08 14:46:25.679527
"""

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

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

    def add_fact(self, fact: str) -> None:
        """Add a new fact to the knowledge base."""
        if fact not in self.knowledge:
            self.knowledge[fact] = []

    def query_fact(self, fact: str) -> List[str]:
        """Query facts related to a given topic."""
        return self.knowledge.get(fact, [])


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

    def add_knowledge(self, knowledge: Dict[str, List[str]]) -> None:
        """
        Add multiple pieces of knowledge at once.
        
        :param knowledge: A dictionary where the key is a topic and value is a list
                          of related facts.
        """
        for topic, facts in knowledge.items():
            for fact in facts:
                self.kb.add_fact(fact)

    def infer_relationships(self) -> Dict[str, List[Tuple[str, str]]]:
        """
        Infer relationships between different pieces of knowledge based on common topics.

        :return: A dictionary where the key is a topic and value is a list of tuples
                 containing related facts.
        """
        inferred_relations = {}
        for fact1 in self.kb.knowledge.keys():
            related_facts = []
            for fact2 in self.kb.knowledge.keys():
                if fact1 != fact2:
                    common_topics = set(fact1.split()) & set(fact2.split())
                    if common_topics:
                        related_facts.append((fact1, fact2))
            if related_facts:
                inferred_relations[fact1] = related_facts
        return inferred_relations


# Example usage

reasoning_engine = ReasoningEngine()
knowledge_base_data = {
    "AI": ["machine learning", "natural language processing"],
    "Machine Learning": ["supervised learning", "unsupervised learning"],
    "Natural Language Processing": ["language modeling", "text analysis"]
}

reasoning_engine.add_knowledge(knowledge_base_data)
inferred_relationships = reasoning_engine.infer_relationships()

for topic, relationships in inferred_relationships.items():
    print(f"Inferred relationships for {topic}:")
    for relation in relationships:
        print(f"  {relation[0]} -> {relation[1]}")
```