import faiss
import numpy as np

# Define the knowledge graph schema
entities = ['Alice', 'Bob', 'Charlie']
relationships = [('Alice', 'friend', 'Bob'), ('Alice', 'friend', 'Charlie')]

# Create the database schema
db = faiss.Database('knowledge_graph.db')

db.create_table('entities', entities)
db.create_table('relationships', relationships)

# Implement the knowledge graph embedding
model = Word2Vec(sentences=['Alice is friends with Bob and Charlie'], dim=100)

# Store the knowledge graph in the database
for entity in entities:
    db.insert('entities', entity)
for relationship in relationships:
    db.insert('relationships', relationship)

# Implement the LLM
llm = LanguageModel(model)

# Integrate the knowledge graph embedding with the LLM
def get_entity_vector(entity):
    entity_vector = np.array(db.get('entities', entity))
    return entity_vector

def get_relationship_vector(relationship):
    relationship_vector = np.array(db.get('relationships', relationship))
    return relationship_vector

llm.add_entity_vector_fn(get_entity_vector)
llm.add_relationship_vector_fn(get_relationship_vector)

# Test the system
llm.process('Alice is friends with Bob and Charlie')