feat: Implement the initial IPSExtractor project structure, including application logic, GUI, core utilities, and documentation.

2026-02-23 09:24:06 -05:00
commit 69debb1c93
49 changed files with 7385 additions and 0 deletions
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+"""Estrategias de extracción de información desde imágenes de documentos.
+
+Define la interfaz `InvoiceExtractorStrategy` y una implementación basada en
+un endpoint compatible con OpenAI (por ejemplo, Ollama) para modelos
+multimodales como `qwen2.5vl`.
+"""
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import List, Dict, Any, Optional
+
+from .config import settings
+from .llm_client import LLMClient
+import logging
+import time
+
+
+class InvoiceExtractorStrategy(ABC):
+  """Interfaz para extraer datos a partir de páginas en base64 e instrucción."""
+  @abstractmethod
+  def extract(self, images_b64: List[str], instruction: str, history: Optional[List[Dict[str, Any]]] = None) -> Dict[str, Any]:
+    """Devuelve un dict con al menos la clave "raw" (texto de salida del modelo)."""
+    raise NotImplementedError
+
+  async def extract_async(self, images_b64: List[str], instruction: str, history: Optional[List[Dict[str, Any]]] = None) -> Dict[str, Any]:
+    """Versión asíncrona de extract. Por defecto lanza NotImplementedError."""
+    raise NotImplementedError
+
+
+class OllamaExtractor(InvoiceExtractorStrategy):
+  """Extractor contra un servidor Ollama/OpenAI-compatible.
+
+  Parámetros configurables: `api_key`, `base_url` y `model`.
+  """
+  def __init__(self, api_key: Optional[str] = None, base_url: Optional[str] = None, model: Optional[str] = None, llm_client: Optional[LLMClient] = None) -> None:
+    # Defaults desde settings si no se proveen
+    self.api_key = api_key or settings.api.api_key
+    self.base_url = base_url or settings.api.base_url
+    self.model = model or settings.api.model
+    self.llm = llm_client or LLMClient(api_key=self.api_key, base_url=self.base_url, default_model=self.model, request_timeout_s=settings.api.request_timeout_s)
+    self._logger = logging.getLogger(__name__)
+
+  def extract(self, images_b64: List[str], instruction: str, history: Optional[List[Dict[str, Any]]] = None) -> Dict[str, Any]:
+    """Construye el prompt multimodal y devuelve el texto crudo del modelo."""
+    content: List[Dict[str, Any]] = []
+    for image_b64 in images_b64:
+      content.append({
+        "type": "image_url",
+        "image_url": {"url": f"data:image/png;base64,{image_b64}"},
+      })
+    content.append({"type": "text", "text": instruction})
+    
+    messages = []
+    if history:
+        messages.extend(history)
+    messages.append({"role": "user", "content": content})
+
+    start = time.time()
+    try:
+      # Trazas DEBUG del request (sin exponer imágenes ni texto completo)
+      prompt_len = len(instruction) if isinstance(instruction, str) else 0
+      # Log INFO con metadatos efectivos de la llamada
+      self._logger.info(
+        "backend_call_start",
+        extra={
+          "base_url": self.base_url,
+          "model": self.model,
+          "timeout_s": settings.api.request_timeout_s,
+          "images": len(images_b64),
+          "prompt_len": prompt_len,
+          "history_len": len(history) if history else 0,
+          "dpi": '-', "pdf": '-', "radicado": '-',
+        },
+      )
+      self._logger.debug(
+        f"Llamada backend: images={len(images_b64)}, prompt_len={prompt_len}, history_len={len(history) if history else 0}",
+        extra={"dpi": '-', "pdf": '-', "radicado": '-'},
+      )
+      text, usage_dict = self.llm.chat_text(
+        messages=messages,
+        model=self.model,
+        timeout_s=settings.api.request_timeout_s,
+        component="extractor",
+      )
+      elapsed = time.time() - start
+      self._logger.info(
+        "Extracción completada",
+        extra={
+          "dpi": '-', "pdf": '-', "radicado": '-',
+          "base_url": self.base_url, "model": self.model,
+          "timeout_s": settings.api.request_timeout_s,
+          "elapsed_ms": int(elapsed * 1000),
+        },
+      )
+      self._logger.debug(
+        f"Backend OK (elapsed_ms={int(elapsed*1000)})",
+        extra={"dpi": '-', "pdf": '-', "radicado": '-'},
+      )
+      return {
+        "raw": text,
+        "usage": usage_dict,
+      }
+    except Exception:
+      elapsed = time.time() - start
+      self._logger.exception(
+        "Error en llamada al backend",
+        extra={
+          "dpi": '-', "pdf": '-', "radicado": '-',
+          "base_url": self.base_url, "model": self.model,
+          "timeout_s": settings.api.request_timeout_s,
+          "elapsed_ms": int(elapsed * 1000),
+        },
+      )
+      try:
+        self._logger.debug(
+          f"Backend error tras {int(elapsed*1000)} ms",
+          extra={"dpi": '-', "pdf": '-', "radicado": '-'},
+        )
+      except Exception:
+        pass
+      raise
+
+  async def extract_async(self, images_b64: List[str], instruction: str, history: Optional[List[Dict[str, Any]]] = None) -> Dict[str, Any]:
+    """Construye el prompt multimodal y devuelve el texto crudo del modelo de forma asíncrona."""
+    content: List[Dict[str, Any]] = []
+    for image_b64 in images_b64:
+      content.append({
+        "type": "image_url",
+        "image_url": {"url": f"data:image/png;base64,{image_b64}"},
+      })
+    content.append({"type": "text", "text": instruction})
+    
+    messages = []
+    if history:
+        messages.extend(history)
+    messages.append({"role": "user", "content": content})
+
+    start = time.time()
+    try:
+      # Trazas DEBUG del request (sin exponer imágenes ni texto completo)
+      prompt_len = len(instruction) if isinstance(instruction, str) else 0
+      # Log INFO con metadatos efectivos de la llamada
+      self._logger.info(
+        "backend_call_start",
+        extra={
+          "base_url": self.base_url,
+          "model": self.model,
+          "timeout_s": settings.api.request_timeout_s,
+          "images": len(images_b64),
+          "prompt_len": prompt_len,
+          "history_len": len(history) if history else 0,
+          "dpi": '-', "pdf": '-', "radicado": '-',
+        },
+      )
+      self._logger.debug(
+        f"Llamada backend: images={len(images_b64)}, prompt_len={prompt_len}, history_len={len(history) if history else 0}",
+        extra={"dpi": '-', "pdf": '-', "radicado": '-'},
+      )
+      text, usage_dict = await self.llm.chat_text_async(
+        messages=messages,
+        model=self.model,
+        timeout_s=settings.api.request_timeout_s,
+        component="extractor",
+      )
+      elapsed = time.time() - start
+      self._logger.info(
+        "Extracción completada",
+        extra={
+          "dpi": '-', "pdf": '-', "radicado": '-',
+          "base_url": self.base_url, "model": self.model,
+          "timeout_s": settings.api.request_timeout_s,
+          "elapsed_ms": int(elapsed * 1000),
+        },
+      )
+      self._logger.debug(
+        f"Backend OK (elapsed_ms={int(elapsed*1000)})",
+        extra={"dpi": '-', "pdf": '-', "radicado": '-'},
+      )
+      return {
+        "raw": text,
+        "usage": usage_dict,
+      }
+    except Exception:
+      elapsed = time.time() - start
+      self._logger.exception(
+        "Error en llamada al backend",
+        extra={
+          "dpi": '-', "pdf": '-', "radicado": '-',
+          "base_url": self.base_url, "model": self.model,
+          "timeout_s": settings.api.request_timeout_s,
+          "elapsed_ms": int(elapsed * 1000),
+        },
+      )
+      try:
+        self._logger.debug(
+          f"Backend error tras {int(elapsed*1000)} ms",
+          extra={"dpi": '-', "pdf": '-', "radicado": '-'},
+        )
+      except Exception:
+        pass
+      raise
+
+  def update_config(self, *, api_key: str | None = None, base_url: str | None = None, model: str | None = None) -> None:
+    """Actualiza la configuración del cliente y recrea la instancia interna."""
+    if api_key is not None:
+      self.api_key = api_key
+    if base_url is not None:
+      self.base_url = base_url
+    if model is not None:
+      self.model = model
+    # actualizar cliente LLM con config actual
+    self.llm.update_config(api_key=self.api_key, base_url=self.base_url, default_model=self.model, request_timeout_s=settings.api.request_timeout_s)