def semantic_search(query_text, n=5):
    # Get query embedding
    query_embedding = get_embedding(query_text)
    if not query_embedding:
        print("Failed to get query embedding")
        return []

    # Search for similar products using dot product and magnitude for cosine similarity
    results = graph.run("""
    MATCH (p:Product)
    WHERE p.description_embedding IS NOT NULL
    WITH p,
    reduce(dot = 0.0, i in range(0, size(p.description_embedding)-1) |
    dot + p.description_embedding[i] * $embedding[i]) /
    (sqrt(reduce(a = 0.0, i in range(0, size(p.description_embedding)-1) |
    a + p.description_embedding[i] * p.description_embedding[i])) *
    sqrt(reduce(b = 0.0, i in range(0, size($embedding)-1) |
    b + $embedding[i] * $embedding[i])))
    AS similarity
    WHERE similarity > 0
    RETURN
    p.name as name,
    p.description as description,
    p.price as price,
    similarity as score
    ORDER BY similarity DESC
    LIMIT $n
    """, parameters={'embedding': query_embedding, 'n': n}).data()
    return results

# Test the search with debug info
print("nTesting semantic search:")
results = semantic_search("Give me a set of cards", n=2)
print(f"nNumber of results: {len(results)}")
for r in results:
    print(f"nProduct: {r.get('name', 'No name')}")
    print(f"Price: ${r.get('price', 'N/A')}")
    print(f"Score: {r.get('score', 'N/A'):.3f}")
    desc = r.get('description', 'No description')
    print(f"Description: {desc}")