Building Intelligent Digital Experiences.

// Calculates cosine similarity between a user query embedding and indexed knowledge base vectors.

import math

def cosine_similarity(v1: list[float], v2: list[float]) -> float:
    dot = sum(a * b for a, b in zip(v1, v2))
    norm1 = math.sqrt(sum(a * a for a in v1))
    norm2 = math.sqrt(sum(b * b for b in v2))
    return dot / (norm1 * norm2) if norm1 and norm2 else 0.0

# User Query Vector vs Document Embeddings
query_emb = [0.24, 0.88, 0.12, 0.45]
knowledge_docs = {
    "Doc 1 (Pharma Specs)": [0.22, 0.85, 0.15, 0.48],
    "Doc 2 (General FAQ)":  [0.05, 0.12, 0.90, 0.33],
    "Doc 3 (API Docs)":     [0.19, 0.79, 0.20, 0.50]
}

print("=== EXECUTING VECTOR SEARCH ===")
for doc_name, doc_emb in knowledge_docs.items():
    score = cosine_similarity(query_emb, doc_emb)
    print(f"Similarity with '{doc_name}': {score:.4f}")