86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
import os
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from dotenv import load_dotenv
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from sqlalchemy import Column, Integer, String, Float, Boolean
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from backend.ConexionSql.Base_conexion import ConexionBase
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from backend.base import Base
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from llms.Modelos.Openai_model import ModeloOpenAI # Clase real de lógica
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# ----------------------
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# Cargar clave maestra
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# ----------------------
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from entrypoint import ENV_PATH
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load_dotenv(ENV_PATH)
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pssword = os.getenv('MASTER_PASSWORD')
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if pssword is None:
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raise ValueError("MASTER_PASSWORD no está definida en el archivo .env")
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# ----------------------
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# MODELO (SQLAlchemy)
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# ----------------------
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class ModeloOpenAIConfigModel(Base):
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__tablename__ = 'modelo_openai_configs'
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id = Column(Integer, primary_key=True)
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model = Column(String, nullable=False)
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temperature = Column(Float, default=0.7)
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top_p = Column(Float, default=1.0)
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top_k = Column(Integer, nullable=True)
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frecuencia_penalizacion = Column(Float, default=0.0)
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num_tokens_maximos = Column(Integer, default=512)
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use_legacy = Column(Boolean, default=False)
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# ----------------------
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# MAPPER
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# ----------------------
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class ModeloOpenAIConfigMapper:
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@staticmethod
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def to_dict(obj: ModeloOpenAI) -> dict:
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return {
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"model": obj.model,
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"temperature": obj.temperature,
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"top_p": obj.top_p,
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"top_k": obj.top_k,
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"frecuencia_penalizacion": obj.frecuencia_penalizacion,
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"num_tokens_maximos": obj.num_tokens_maximos,
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"use_legacy": obj.use_legacy
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}
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@staticmethod
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def from_model(model: ModeloOpenAIConfigModel, cliente: object) -> ModeloOpenAI:
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return ModeloOpenAI(
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cliente=cliente,
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model=model.model,
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temperature=model.temperature,
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top_p=model.top_p,
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top_k=model.top_k,
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frecuencia_penalizacion=model.frecuencia_penalizacion,
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num_tokens_maximos=model.num_tokens_maximos,
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use_legacy=model.use_legacy
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)
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# ----------------------
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# REPO
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# ----------------------
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class ModeloOpenAIConfigRepo:
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def __init__(self, conexion: ConexionBase, cliente: object):
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self.session = conexion.get_session()
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self.cliente = cliente # Necesario para crear ModeloOpenAI
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def add(self, config: ModeloOpenAI) -> int:
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data = ModeloOpenAIConfigMapper.to_dict(config)
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model = ModeloOpenAIConfigModel(**data)
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self.session.add(model)
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self.session.commit()
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return model.id
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def get_by_id(self, id_: int) -> ModeloOpenAI | None:
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model = self.session.get(ModeloOpenAIConfigModel, id_)
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return ModeloOpenAIConfigMapper.from_model(model, self.cliente) if model else None
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def get_all(self) -> list[ModeloOpenAI]:
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models = self.session.query(ModeloOpenAIConfigModel).all()
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return [ModeloOpenAIConfigMapper.from_model(m, self.cliente) for m in models] |