store wip

store-py/store.py

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+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')