Internal Policy Assistant

import pandas as pd
from sentence_transformers import SentenceTransformer
import numpy as np

def search_policy(query):
    model = SentenceTransformer('all-MiniLM-L6-v2')
    policies = pd.read_sql("SELECT * FROM policies")
    q_vec = model.encode([query])[0]
    policy_vecs = np.array([model.encode([p['content']])[0] for _, p in policies.iterrows()])
    scores = model.similarity(q_vec, policy_vecs)[0]
    best_idx = np.argmax(scores)
    return policies.iloc[best_idx]['content'] if scores[best_idx] > 0.5 else 'No matching policy found'