import os
from typing import List, Dict, Any
import time
import numpy as np
from openai import OpenAI
from config import MODEL_NAME, OPENAI_API_KEY_ENV, BATCH_SIZE

def get_client() -> OpenAI:
    api_key = os.getenv(OPENAI_API_KEY_ENV)
    if not api_key:
        raise RuntimeError(f"Missing OpenAI API key env var: {OPENAI_API_KEY_ENV}")
    return OpenAI(api_key=api_key)

def embed_texts(texts: List[str], client: OpenAI = None, model: str = MODEL_NAME, batch_size: int = BATCH_SIZE) -> List[List[float]]:
    """Embed a list of texts in batches. Returns list of vectors (list[float])."""
    if client is None:
        client = get_client()

    vectors: List[List[float]] = []
    total = len(texts)
    for i in range(0, total, batch_size):
        batch = texts[i:i+batch_size]
        # Retry loop (simple backoff); you can replace with tenacity if desired.
        for attempt in range(5):
            try:
                resp = client.embeddings.create(model=model, input=batch)
                for item in resp.data:
                    vectors.append(item.embedding)
                break
            except Exception as e:
                wait = 2 ** attempt
                print(f"[WARN] Embedding batch {i//batch_size+1}/{(total+batch_size-1)//batch_size} failed: {e}. Retrying in {wait}s...")
                time.sleep(wait)
    return vectors