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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] |