Mireya Cueto Garrido
946f16a633
Introduce a full Vite/React UI for exams, auth, materials, images, generation, and export. Adapt backend for Sinbad2IA chat API, bcrypt passwords, CORS on port 5173, and schema migrations.
74 lines
2.6 KiB
Python
74 lines
2.6 KiB
Python
import httpx
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from app.core.config import Settings
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from app.core.errors import LLMUnavailableError
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class LLMClient:
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"""Cliente para el API de chat de Sinbad2IA (Ollama-compatible en UJA)."""
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def __init__(self, settings: Settings) -> None:
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self.settings = settings
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def _chat_url(self) -> str:
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base = self.settings.llm_base_url.rstrip("/")
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if base.endswith("/api/chat"):
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return base
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return f"{base}/api/chat"
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async def generate(self, prompt: str) -> str:
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payload = {
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"model": self.settings.llm_model,
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"messages": [
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{
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"role": "user",
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"content": (
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"Genera preguntas de examen en formato JSON válido para importar en Moodle. "
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"Responde únicamente con el JSON solicitado, sin texto adicional ni bloques markdown.\n\n"
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f"{prompt}"
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),
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},
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],
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"stream": False,
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}
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headers = {"Content-Type": "application/json"}
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if self.settings.llm_api_key:
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headers["Authorization"] = f"Bearer {self.settings.llm_api_key}"
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try:
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async with httpx.AsyncClient(timeout=self.settings.llm_timeout_seconds) as client:
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response = await client.post(self._chat_url(), json=payload, headers=headers)
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response.raise_for_status()
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except httpx.HTTPError as exc:
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raise LLMUnavailableError("LLM request failed") from exc
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content = _extract_assistant_content(response.json())
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if not content.strip():
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raise LLMUnavailableError("LLM returned empty content")
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return content
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def _extract_assistant_content(data: object) -> str:
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"""Soporta respuesta Sinbad2IA/Ollama (`message.content`) y OpenAI (`choices`)."""
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if not isinstance(data, dict):
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raise LLMUnavailableError("LLM response is not a JSON object")
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message = data.get("message")
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if isinstance(message, dict):
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content = message.get("content")
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if isinstance(content, str) and content.strip():
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return content
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choices = data.get("choices")
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if isinstance(choices, list) and choices:
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first = choices[0]
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if isinstance(first, dict):
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msg = first.get("message")
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if isinstance(msg, dict):
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content = msg.get("content")
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if isinstance(content, str) and content.strip():
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return content
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raise LLMUnavailableError("LLM response did not include message content")
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