from VDBpy.indexing import VectorIndex
from VDBpy.query import VectorQuery

# Create a new vector index
index = VectorIndex()

# Add some vectors to the index
index.add_vector([1, 2, 3], 'vector1')
index.add_vector([4, 5, 6], 'vector2')

# Create a new vector query
query = VectorQuery(index)

# Execute the query
results = query.execute([2, 2, 2], k=2)
'''
# Execute the query using cosine similarity
results = query.execute([2,2,2], k=2, metric='cosine')
# Execute the query using Manhattan distance
results = query.execute([2,2,2], k=2, metric='manhattan')
# Execute the query using Jaccard similarity
results = query.execute([2,2,2], k=2, metric='jaccard')
'''

# Print the results
for id, similarity in results:
  print(f"ID: {id}, Similarity: {similarity}")