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
96 lines
3.0 KiB
Python
96 lines
3.0 KiB
Python
import os
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from dotenv import load_dotenv
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from sqlalchemy import Column, String
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from sqlalchemy import Column, String, ForeignKey
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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 domains.ConexionSql.Base_conexion import ConexionBase
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from domains.base import Base
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from domains.Security.GenerarIDs import GeneradorIDUnico
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from domains.Llms.Embedders.Openai_embedder import OpenAIEmbedder
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from domains.ApiKeys.openai_apikey import OpenAICredencial
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# ----------------------
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# Cargar configuración desde .env si se requiere
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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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# ----------------------
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# MODELO (SQLAlchemy)
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# ----------------------
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class OpenAIEmbedderModel(Base, Model_base):
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__tablename__ = "openai_embedders"
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id = Column(String, primary_key=True)
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api_key_id = Column(String, ForeignKey("openai_credenciales.id"), nullable=False)
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model = Column(String, nullable=False)
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# ----------------------
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# MAPPER
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# ----------------------
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class OpenAIEmbedderMapper(Mapper_base[OpenAIEmbedder, OpenAIEmbedderModel]):
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@staticmethod
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def to_model(obj: OpenAIEmbedder) -> OpenAIEmbedderModel:
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return OpenAIEmbedderModel(
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id=obj.id,
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api_key_id=obj.client.credencial.id,
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model=obj.model
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)
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@staticmethod
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def from_model(model: OpenAIEmbedderModel, credencial: OpenAICredencial) -> OpenAIEmbedder:
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return OpenAIEmbedder(
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id=model.id,
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credencial=credencial,
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model=model.model
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)
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@staticmethod
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def to_dict(obj: OpenAIEmbedder) -> dict:
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return {
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"id": obj.id,
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"api_key_id": obj.client.credencial.id,
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"model": obj.model
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}
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@staticmethod
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def from_dict(data: dict, credencial: OpenAICredencial) -> OpenAIEmbedder:
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return OpenAIEmbedder(
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id=data["id"],
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credencial=credencial,
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model=data["model"]
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)
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# ----------------------
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# REPO
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# ----------------------
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class OpenAIEmbedderRepo(Repo_base[OpenAIEmbedderModel, OpenAIEmbedder]):
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def __init__(self, conexion: ConexionBase):
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super().__init__(
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session=conexion.get_session(),
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modelo=OpenAIEmbedderModel,
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mapper=OpenAIEmbedderMapper
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)
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def get_by_id(self, id_: str, credencial: OpenAICredencial) -> OpenAIEmbedder | None:
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model = self.session.get(self.Modelo, id_)
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return self.Mapper.from_model(model, credencial) if model else None
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def get_all(self, credencial_loader: callable) -> list[OpenAIEmbedder]:
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"""
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:param credencial_loader: función que recibe un api_key_id y devuelve una instancia de OpenAICredencial
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"""
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models = self.session.query(self.Modelo).all()
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return [
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self.Mapper.from_model(m, credencial_loader(m.api_key_id))
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for m in models
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] |