"""
Utilities module: LLM client wrapper and shared helpers.
"""
import os
import openai
from typing import List
from openai import AzureOpenAI
from langchain_openai import AzureOpenAIEmbeddings

try:
    from src.utils import logger
except ImportError:
    import structlog
    logger = structlog.get_logger()

class LLMClient:
    """
    Simple wrapper around OpenAI (or any other) LLM API.
    Reads API key from environment and exposes `generate(prompt)`.
    """
    @staticmethod
    def generate(prompt: str, model: str = None, max_tokens: int = 512, **kwargs) -> str:
        azure_api_key = os.getenv('AZURE_API_KEY')
        azure_endpoint = os.getenv('AZURE_ENDPOINT')
        azure_api_version = os.getenv('AZURE_API_VERSION')
        openai_model_name = model or os.getenv('OPENAI_MODEL', 'gpt-4o')

        if not (azure_api_key or azure_endpoint or azure_api_version or openai_model_name):
            logger.error('OPENAI_API_KEY is not set')
            raise EnvironmentError('Missing OPENAI_API_KEY')
        client = AzureOpenAI(
                api_key=azure_api_key,
                azure_endpoint=azure_endpoint,
                api_version=azure_api_version
            )
        try:
            resp = client.chat.completions.create(
                model=openai_model_name,
                messages=[{"role": "system", "content": "You are a helpful assistant."},
                          {"role": "user", "content": prompt}],
                max_tokens=max_tokens,
                temperature=0.0,
                **kwargs
            )
            text = resp.choices[0].message.content.strip()
            return text
        except Exception as e:
            logger.exception('LLM generation failed')
            raise


class OpenAIEmbedder:
    """
    Wrapper around OpenAI Embeddings API. 
    Usage: embedder = OpenAIEmbedder(model_name)
           embs = embedder.embed([str1, str2, ...])
    """
    def __init__(self, model_name: str):
        self.model = model_name
        openai.api_key = os.getenv("OPENAI_API_KEY")

    def embed(self, texts: List[str]) -> List[List[float]]:
        embeddings = AzureOpenAIEmbeddings(model=self.model)
        resp = embeddings.embed_documents(texts)
        # return list of embedding vectors
        return resp