132 lines
5.8 KiB
Python
132 lines
5.8 KiB
Python
"""Estrategias de extracción de información desde imágenes de documentos.
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Define la interfaz `InvoiceExtractorStrategy` y una implementación basada en
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un endpoint compatible con la API de OpenAI para modelos multimodales.
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"""
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from __future__ import annotations
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from abc import ABC, abstractmethod
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from typing import List, Dict, Any, Optional
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from .config import extractor_llm_config
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from .llm_client import LLMClient
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import logging
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import time
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class InvoiceExtractorStrategy(ABC):
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"""Interfaz para extraer datos a partir de páginas en base64 e instrucción."""
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@abstractmethod
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def extract(self, images_b64: List[str], instruction: str, history: Optional[List[Dict[str, Any]]] = None) -> Dict[str, Any]:
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"""Devuelve un dict con al menos la clave "raw" (texto de salida del modelo)."""
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raise NotImplementedError
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async def extract_async(self, images_b64: List[str], instruction: str, history: Optional[List[Dict[str, Any]]] = None) -> Dict[str, Any]:
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"""Versión asíncrona de extract. Por defecto lanza NotImplementedError."""
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raise NotImplementedError
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_LOG_EXTRA_BASE = {"dpi": '-', "pdf": '-', "radicado": '-'}
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class OpenAICompatibleExtractor(InvoiceExtractorStrategy):
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"""Extractor contra un backend compatible con Chat Completions.
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Parámetros configurables: `api_key`, `base_url` y `model`.
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"""
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def __init__(self, api_key: Optional[str] = None, base_url: Optional[str] = None, model: Optional[str] = None, llm_client: Optional[LLMClient] = None) -> None:
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config = extractor_llm_config()
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self.api_key = api_key if api_key is not None else str(config["api_key"])
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self.base_url = base_url if base_url is not None else str(config["base_url"])
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self.model = model if model is not None else str(config["model"])
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self.request_timeout_s = float(config["timeout_s"])
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self.llm = llm_client or LLMClient(
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api_key=self.api_key,
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base_url=self.base_url,
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default_model=self.model,
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request_timeout_s=self.request_timeout_s,
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)
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self._logger = logging.getLogger(__name__)
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def _build_messages(self, images_b64: List[str], instruction: str, history: Optional[List[Dict[str, Any]]]) -> List[Dict[str, Any]]:
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content: List[Dict[str, Any]] = [
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{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}}
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for img in images_b64
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]
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content.append({"type": "text", "text": instruction})
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messages = list(history) if history else []
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messages.append({"role": "user", "content": content})
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return messages
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def _log_call_start(self, images_b64: List[str], instruction: str, history: Optional[List[Dict[str, Any]]]) -> None:
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prompt_len = len(instruction) if isinstance(instruction, str) else 0
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self._logger.info(
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"backend_call_start",
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extra={**_LOG_EXTRA_BASE, "base_url": self.base_url, "model": self.model,
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"timeout_s": self.request_timeout_s, "images": len(images_b64),
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"prompt_len": prompt_len, "history_len": len(history) if history else 0},
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)
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self._logger.debug(
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f"Llamada backend: images={len(images_b64)}, prompt_len={prompt_len}, history_len={len(history) if history else 0}",
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extra=_LOG_EXTRA_BASE,
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)
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def _log_call_ok(self, elapsed: float) -> None:
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self._logger.info(
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"Extracción completada",
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extra={**_LOG_EXTRA_BASE, "base_url": self.base_url, "model": self.model,
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"timeout_s": self.request_timeout_s, "elapsed_ms": int(elapsed * 1000)},
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)
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self._logger.debug(f"Backend OK (elapsed_ms={int(elapsed*1000)})", extra=_LOG_EXTRA_BASE)
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def _log_call_error(self, elapsed: float) -> None:
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self._logger.exception(
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"Error en llamada al backend",
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extra={**_LOG_EXTRA_BASE, "base_url": self.base_url, "model": self.model,
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"timeout_s": self.request_timeout_s, "elapsed_ms": int(elapsed * 1000)},
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)
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self._logger.debug(f"Backend error tras {int(elapsed*1000)} ms", extra=_LOG_EXTRA_BASE)
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def extract(self, images_b64: List[str], instruction: str, history: Optional[List[Dict[str, Any]]] = None) -> Dict[str, Any]:
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"""Construye el prompt multimodal y devuelve el texto crudo del modelo."""
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messages = self._build_messages(images_b64, instruction, history)
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self._log_call_start(images_b64, instruction, history)
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start = time.time()
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try:
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text, usage_dict = self.llm.chat_text(
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messages=messages, model=self.model,
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timeout_s=self.request_timeout_s, component="extractor",
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)
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self._log_call_ok(time.time() - start)
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return {"raw": text, "usage": usage_dict}
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except Exception:
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self._log_call_error(time.time() - start)
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raise
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async def extract_async(self, images_b64: List[str], instruction: str, history: Optional[List[Dict[str, Any]]] = None) -> Dict[str, Any]:
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"""Versión asíncrona de extract."""
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messages = self._build_messages(images_b64, instruction, history)
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self._log_call_start(images_b64, instruction, history)
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start = time.time()
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try:
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text, usage_dict = await self.llm.chat_text_async(
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messages=messages, model=self.model,
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timeout_s=self.request_timeout_s, component="extractor",
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)
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self._log_call_ok(time.time() - start)
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return {"raw": text, "usage": usage_dict}
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except Exception:
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self._log_call_error(time.time() - start)
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raise
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def update_config(self, *, api_key: str | None = None, base_url: str | None = None, model: str | None = None) -> None:
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"""Actualiza la configuración del cliente y recrea la instancia interna."""
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if api_key is not None:
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self.api_key = api_key
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if base_url is not None:
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self.base_url = base_url
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if model is not None:
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self.model = model
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# actualizar cliente LLM con config actual
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self.llm.update_config(api_key=self.api_key, base_url=self.base_url, default_model=self.model, request_timeout_s=self.request_timeout_s)
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