picarones/llm/openai_adapter.py

"""Adaptateur LLM — OpenAI (GPT-4o, GPT-4o-mini)."""

from __future__ import annotations

import logging
import os
from typing import Optional

from picarones.llm.base import (
    BaseLLMAdapter,
    log_http_error,
    normalize_llm_content,
)

logger = logging.getLogger(__name__)


class OpenAIAdapter(BaseLLMAdapter):
    """Adaptateur pour les modèles OpenAI (GPT-4o, GPT-4o-mini).

    Clé API via la variable d'environnement ``OPENAI_API_KEY``.

    Modes supportés : text_only, text_and_image, zero_shot.
    """

    api_key_env_var = "OPENAI_API_KEY"

    @property
    def name(self) -> str:
        return "openai"

    @property
    def default_model(self) -> str:
        return "gpt-4o"

    def __init__(
        self,
        model: Optional[str] = None,
        config: Optional[dict] = None,
    ) -> None:
        super().__init__(model, config)
        self._api_key = os.environ.get("OPENAI_API_KEY")

    def _call(self, prompt: str, image_b64: Optional[str] = None) -> str:
        if not self._api_key:
            raise RuntimeError(
                "Clé API OpenAI manquante — définissez la variable d'environnement OPENAI_API_KEY"
            )
        try:
            from openai import OpenAI
        except ImportError as exc:
            raise RuntimeError(
                "Le package 'openai' n'est pas installé. Lancez : pip install openai"
            ) from exc

        client = OpenAI(api_key=self._api_key)
        temperature = float(self.config.get("temperature", 0.0))
        max_tokens = int(self.config.get("max_tokens", 4096))

        if image_b64:
            content = [
                {"type": "text", "text": prompt},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/png;base64,{image_b64}"},
                },
            ]
        else:
            content = prompt  # type: ignore[assignment]

        try:
            response = client.chat.completions.create(
                model=self.model,
                messages=[{"role": "user", "content": content}],
                temperature=temperature,
                max_tokens=max_tokens,
            )
        except Exception as exc:
            log_http_error(
                "OpenAIAdapter", self.model, exc,
                env_var=self.api_key_env_var,
            )
            raise

        if not response.choices:
            logger.warning(
                "[OpenAIAdapter] response.choices vide (modèle=%s).", self.model,
            )
            return ""
        # Chantier 4 — propagation du fix Sprint 15 : le SDK OpenAI
        # peut retourner une ``list[ContentBlock]`` selon l'API
        # (Responses, structured outputs).  ``normalize_llm_content``
        # gère les deux cas (str et list).
        return normalize_llm_content(response.choices[0].message.content)