# RDF/Turtle format
@prefix : <http://example.org/ontology#> .

:Person a owl:Class ;
    rdfs:label "Person" .

:Organization a owl:Class ;
    rdfs:label "Organization" .

:worksFor a owl:ObjectProperty ;
    rdfs:domain :Person ;
    rdfs:range :Organization ;
    rdfs:label "works for" .

graph_database: 'Neo4j Community' # or Enterprise for production
vector_integration: 'Pinecone' # For hybrid search
embeddings: 'text-embedding-3-large' # OpenAI
etl: 'Apache Airflow' # For data pipelines

// Create constraints for uniqueness
CREATE CONSTRAINT person_id IF NOT EXISTS
FOR (p:Person) REQUIRE p.id IS UNIQUE;

CREATE CONSTRAINT org_name IF NOT EXISTS
FOR (o:Organization) REQUIRE o.name IS UNIQUE;

// Create indexes for performance
CREATE INDEX entity_search IF NOT EXISTS
FOR (e:Entity) ON (e.name, e.type);

CREATE INDEX relationship_type IF NOT EXISTS
FOR ()-[r:RELATED_TO]-() ON (r.type, r.confidence);

class EntityExtractionPipeline:
    def __init__(self):
        self.ner_model = load_ner_model()  # spaCy, Hugging Face
        self.entity_linker = EntityLinker()
        self.deduplicator = EntityDeduplicator()

    def process_text(self, text: str) -> List[Entity]:
        # 1. Extract named entities
        entities = self.ner_model.extract(text)

        # 2. Link to existing entities (entity resolution)
        linked_entities = self.entity_linker.link(entities)

        # 3. Deduplicate and resolve conflicts
        resolved_entities = self.deduplicator.resolve(linked_entities)

        return resolved_entities

class RelationshipExtractor:
    def extract_relationships(self, entities: List[Entity],
                            text: str) -> List[Relationship]:
        relationships = []

        # Use dependency parsing or LLM for extraction
        doc = self.nlp(text)
        for sent in doc.sents:
            rels = self.extract_from_sentence(sent, entities)
            relationships.extend(rels)

        # Validate against ontology
        valid_relationships = self.validate_relationships(relationships)
        return valid_relationships

def extract_with_llm(text: str) -> List[Relationship]:
    prompt = f"""
    Extract entities and relationships from this text:
    {text}

    Format: (Entity1, Relationship, Entity2, Confidence)
    Only extract factual relationships.
    """

    response = llm.generate(prompt)
    relationships = parse_llm_response(response)
    return relationships

class HybridKnowledgeSystem:
    def __init__(self):
        self.graph_db = Neo4jConnection()
        self.vector_db = PineconeClient()
        self.embedding_model = OpenAIEmbeddings()

    def store_entity(self, entity: Entity):
        # Store structured data in graph
        self.graph_db.create_node(entity)

        # Store embeddings in vector database
        embedding = self.embedding_model.embed(entity.description)
        self.vector_db.upsert(
            id=entity.id,
            values=embedding,
            metadata=entity.metadata
        )

    def hybrid_search(self, query: str, top_k: int = 10) -> SearchResults:
        # 1. Vector similarity search
        query_embedding = self.embedding_model.embed(query)
        vector_results = self.vector_db.query(
            vector=query_embedding,
            top_k=100
        )

        # 2. Graph traversal from vector results
        entity_ids = [r.id for r in vector_results.matches]
        graph_results = self.graph_db.get_subgraph(entity_ids, max_hops=2)

        # 3. Merge and rank results
        merged = self.merge_results(vector_results, graph_results)
        return merged[:top_k]

MATCH (e:Entity {id: $entity_id})
RETURN e

MATCH (source:Entity {id: $entity_id})-[r]-(target)
RETURN source, r, target
LIMIT 20

MATCH path = shortestPath(
  (source:Person {id: $source_id})-[*..5]-(target:Person {id: $target_id})
)
RETURN path

MATCH (p:Person {name: $name})-[:WORKS_FOR]->(o:Organization)-[:LOCATED_IN]->(l:Location)
RETURN p.name, o.name, l.city

// Find people similar to this person based on shared organizations
MATCH (p1:Person {id: $person_id})-[:WORKS_FOR]->(o:Organization)<-[:WORKS_FOR]-(p2:Person)
WHERE p1 <> p2
RETURN p2, COUNT(o) AS shared_orgs
ORDER BY shared_orgs DESC
LIMIT 10

class KnowledgeGraphAPI:
    def __init__(self, graph_db):
        self.graph = graph_db

    def find_entity(self, entity_name: str) -> Entity:
        """Find entity by name with fuzzy matching"""
        query = """
        MATCH (e:Entity)
        WHERE e.name CONTAINS $name
        RETURN e
        ORDER BY apoc.text.levenshtein(e.name, $name)
        LIMIT 1
        """
        return self.graph.run(query, name=entity_name).single()

    def find_relationships(self, entity_id: str,
                         relationship_type: str = None,
                         max_hops: int = 2) -> List[Relationship]:
        """Find relationships within specified hops"""
        query = f"""
        MATCH (source:Entity {{id: $entity_id}})
        MATCH path = (source)-[r*1..{max_hops}]-(target)
        RETURN path, relationships(path) AS rels
        LIMIT 100
        """
        return self.graph.run(query, entity_id=entity_id).data()

    def get_subgraph(self, entity_ids: List[str],
                    max_hops: int = 2) -> Subgraph:
        """Get connected subgraph for multiple entities"""
        query = f"""
        MATCH (e:Entity)
        WHERE e.id IN $entity_ids
        CALL apoc.path.subgraphAll(e, {{maxLevel: {max_hops}}})
        YIELD nodes, relationships
        RETURN nodes, relationships
        """
        return self.graph.run(query, entity_ids=entity_ids).data()

class KnowledgeGraphRAG:
    def __init__(self, kg_api, llm_client):
        self.kg = kg_api
        self.llm = llm_client

    def retrieve_context(self, query: str) -> str:
        # Extract entities from query
        entities = self.extract_entities_from_query(query)

        # Retrieve relevant subgraph
        subgraph = self.kg.get_subgraph(
            [e.id for e in entities],
            max_hops=2
        )

        # Format subgraph for LLM
        context = self.format_subgraph_for_llm(subgraph)
        return context

    def generate_with_grounding(self, query: str) -> GroundedResponse:
        context = self.retrieve_context(query)

        prompt = f"""
        Context from knowledge graph:
        {context}

        User query: {query}

        Answer based only on the provided context. Include source entities.
        """

        response = self.llm.generate(prompt)

        return GroundedResponse(
            response=response,
            sources=self.extract_sources(context),
            confidence=self.calculate_confidence(response, context)
        )

class HallucinationDetector:
    def __init__(self, knowledge_graph):
        self.kg = knowledge_graph

    def verify_claim(self, claim: str) -> VerificationResult:
        # Parse claim into (subject, predicate, object)
        parsed_claim = self.parse_claim(claim)

        # Query knowledge graph for evidence
        evidence = self.kg.find_evidence(
            parsed_claim.subject,
            parsed_claim.predicate,
            parsed_claim.object
        )

        if evidence:
            return VerificationResult(
                is_supported=True,
                evidence=evidence,
                confidence=evidence.confidence
            )

        # Check for contradictory evidence
        contradiction = self.kg.find_contradiction(parsed_claim)

        return VerificationResult(
            is_supported=False,
            is_contradicted=bool(contradiction),
            contradiction=contradiction
        )