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