Delete utils.py

utils.py

@@ -1,42 +0,0 @@
-import openai
-import numpy as np
-import re
-from typing import List, Tuple
-from config import EMBED_MODEL
-
-def get_embedding(text: str) -> List[float]:
-    """Generate embedding for a given text."""
-    text_strip = text.replace("\n", " ").strip()
-    response = openai.embeddings.create(input=[text_strip], model=EMBED_MODEL)
-    return response.data[0].embedding
-
-def cosine_similarity(a: List[float], b: List[float]) -> float:
-    """Calculate cosine similarity between two vectors."""
-    a = np.array(a)
-    b = np.array(b)
-    if np.linalg.norm(a) == 0 or np.linalg.norm(b) == 0:
-        return 0.0
-    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
-
-def clean_time(time_str: str) -> str:
-    """Clean up time string."""
-    if not time_str:
-        return ""
-    
-    time_match = re.search(r'(\d{1,2}):?(\d{0,2})\s*(AM|PM)', time_str, re.IGNORECASE)
-    if time_match:
-        hour = time_match.group(1)
-        minute = time_match.group(2) or "00"
-        ampm = time_match.group(3).upper()
-        return f"{hour}:{minute} {ampm}"
-    
-    return time_str.strip()
-
-def find_top_k_matches(user_embedding, dataset, k=3):
-    """Find top k matching entries from a dataset."""
-    scored = []
-    for entry_id, text, emb in dataset:
-        score = cosine_similarity(user_embedding, emb)
-        scored.append((score, entry_id, text))
-    scored.sort(reverse=True)
-    return scored[:k]