egutierrez
aef8791151
- Added Appshell component with responsive navbar and main content area - Integrated ColorSchemeToggle for light/dark mode switching - Created Welcome component with styled title and introductory text - Developed ChatPage for LLM interaction with WebSocket support - Implemented Biblioteca for managing notes with rich text editor - Added LoginPage for user authentication with error handling - Introduced MessageList and MessageBubble components for chat messages - Styled components with CSS modules for consistent design
122 lines
4.1 KiB
Python
122 lines
4.1 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 domains.ArquitectureLayer.Mapper import Mapper_base
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from domains.ArquitectureLayer.Model import Model_base
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from domains.ArquitectureLayer.Repo import Repo_base
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from typing import Optional
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from domains.ConexionSql.Base_conexion import ConexionBase
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from domains.base import Base
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from domains.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, Model_base):
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__tablename__ = 'modelo_openai_configs'
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id = Column(String, primary_key=True)
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model = Column(String, nullable=False)
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temperature = Column(Float, default=0.7, nullable=False)
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top_p = Column(Float, default=1.0, nullable=False)
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top_k = Column(Integer, nullable=True)
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frecuencia_penalizacion = Column(Float, default=0.0, nullable=False)
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num_tokens_maximos = Column(Integer, default=512, nullable=False)
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use_legacy = Column(Boolean, default=False, nullable=False)
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# ----------------------
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# MAPPER
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# ----------------------
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class ModeloOpenAIConfigMapper(Mapper_base[ModeloOpenAI, ModeloOpenAIConfigModel]):
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@staticmethod
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def to_model(obj: ModeloOpenAI) -> ModeloOpenAIConfigModel:
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return ModeloOpenAIConfigModel(
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id=obj.id,
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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: Optional[object] = None) -> ModeloOpenAI:
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return ModeloOpenAI(
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id=model.id,
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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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@staticmethod
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def to_dict(obj: ModeloOpenAI) -> dict:
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return {
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"id": obj.id,
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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_dict(data: dict, cliente: Optional[object] = None) -> ModeloOpenAI:
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return ModeloOpenAI(
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id=data["id"],
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cliente=cliente,
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model=data["model"],
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temperature=data["temperature"],
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top_p=data["top_p"],
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top_k=data["top_k"],
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frecuencia_penalizacion=data["frecuencia_penalizacion"],
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num_tokens_maximos=data["num_tokens_maximos"],
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use_legacy=data["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(Repo_base[ModeloOpenAIConfigModel, ModeloOpenAI]):
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def __init__(self, conexion: ConexionBase, cliente: object):
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super().__init__(
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session=conexion.get_session(),
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modelo=ModeloOpenAIConfigModel,
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mapper=ModeloOpenAIConfigMapper
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)
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self.cliente = cliente # Necesario para construir el dominio con lógica
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def get_by_id(self, id_: str) -> ModeloOpenAI | None:
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model = self.session.get(self.Modelo, id_)
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return self.Mapper.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(self.Modelo).all()
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return [self.Mapper.from_model(m, self.cliente) for m in models] |