Primera version de chunkeo completo crud

backend/app/services/embedding_service.py

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+"""
+Servicio de embeddings usando Azure OpenAI.
+Genera embeddings para chunks de texto usando text-embedding-3-large (3072 dimensiones).
+"""
+import logging
+from typing import List
+from openai import AzureOpenAI
+from ..core.config import settings
+
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingService:
+    """Servicio para generar embeddings usando Azure OpenAI"""
+
+    def __init__(self):
+        """Inicializa el cliente de Azure OpenAI"""
+        try:
+            self.client = AzureOpenAI(
+                api_key=settings.AZURE_OPENAI_API_KEY,
+                api_version=settings.AZURE_OPENAI_API_VERSION,
+                azure_endpoint=settings.AZURE_OPENAI_ENDPOINT
+            )
+            self.model = settings.AZURE_OPENAI_EMBEDDING_DEPLOYMENT
+            self.embedding_dimension = 3072
+            logger.info(f"EmbeddingService inicializado con modelo {self.model}")
+        except Exception as e:
+            logger.error(f"Error inicializando EmbeddingService: {e}")
+            raise
+
+    async def generate_embedding(self, text: str) -> List[float]:
+        """
+        Genera un embedding para un texto individual.
+
+        Args:
+            text: Texto para generar embedding
+
+        Returns:
+            Vector de embedding (3072 dimensiones)
+
+        Raises:
+            Exception: Si hay error al generar el embedding
+        """
+        try:
+            response = self.client.embeddings.create(
+                input=[text],
+                model=self.model
+            )
+            embedding = response.data[0].embedding
+
+            if len(embedding) != self.embedding_dimension:
+                raise ValueError(
+                    f"Dimensión incorrecta: esperada {self.embedding_dimension}, "
+                    f"obtenida {len(embedding)}"
+                )
+
+            return embedding
+
+        except Exception as e:
+            logger.error(f"Error generando embedding: {e}")
+            raise
+
+    async def generate_embeddings_batch(
+        self,
+        texts: List[str],
+        batch_size: int = 100
+    ) -> List[List[float]]:
+        """
+        Genera embeddings para múltiples textos en lotes.
+
+        Args:
+            texts: Lista de textos para generar embeddings
+            batch_size: Tamaño del lote para procesamiento (default: 100)
+
+        Returns:
+            Lista de vectores de embeddings
+
+        Raises:
+            Exception: Si hay error al generar los embeddings
+        """
+        try:
+            embeddings = []
+
+            for i in range(0, len(texts), batch_size):
+                batch = texts[i:i + batch_size]
+                logger.info(f"Procesando lote {i//batch_size + 1}/{(len(texts)-1)//batch_size + 1}")
+
+                response = self.client.embeddings.create(
+                    input=batch,
+                    model=self.model
+                )
+
+                batch_embeddings = [item.embedding for item in response.data]
+
+                # Validar dimensiones
+                for idx, emb in enumerate(batch_embeddings):
+                    if len(emb) != self.embedding_dimension:
+                        raise ValueError(
+                            f"Dimensión incorrecta en índice {i + idx}: "
+                            f"esperada {self.embedding_dimension}, obtenida {len(emb)}"
+                        )
+
+                embeddings.extend(batch_embeddings)
+
+            logger.info(f"Generados {len(embeddings)} embeddings exitosamente")
+            return embeddings
+
+        except Exception as e:
+            logger.error(f"Error generando embeddings en lote: {e}")
+            raise
+
+
+# Instancia global singleton
+_embedding_service: EmbeddingService | None = None
+
+
+def get_embedding_service() -> EmbeddingService:
+    """
+    Obtiene la instancia singleton del servicio de embeddings.
+
+    Returns:
+        Instancia de EmbeddingService
+    """
+    global _embedding_service
+    if _embedding_service is None:
+        _embedding_service = EmbeddingService()
+    return _embedding_service