39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import faiss
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import numpy as np
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# Define the knowledge graph schema
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entities = ['Alice', 'Bob', 'Charlie']
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relationships = [('Alice', 'friend', 'Bob'), ('Alice', 'friend', 'Charlie')]
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# Create the database schema
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db = faiss.Database('knowledge_graph.db')
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db.create_table('entities', entities)
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db.create_table('relationships', relationships)
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# Implement the knowledge graph embedding
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model = Word2Vec(sentences=['Alice is friends with Bob and Charlie'], dim=100)
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# Store the knowledge graph in the database
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for entity in entities:
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db.insert('entities', entity)
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for relationship in relationships:
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db.insert('relationships', relationship)
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# Implement the LLM
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llm = LanguageModel(model)
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# Integrate the knowledge graph embedding with the LLM
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def get_entity_vector(entity):
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entity_vector = np.array(db.get('entities', entity))
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return entity_vector
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def get_relationship_vector(relationship):
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relationship_vector = np.array(db.get('relationships', relationship))
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return relationship_vector
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llm.add_entity_vector_fn(get_entity_vector)
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llm.add_relationship_vector_fn(get_relationship_vector)
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# Test the system
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llm.process('Alice is friends with Bob and Charlie') |