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
32 lines
1.3 KiB
Python
32 lines
1.3 KiB
Python
from typing import List
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from domains.Llms.Embedders.Base_Embedder import EmbedderABC # Asegúrate de que EmbedderABC esté en este módulo
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from domains.ApiKeys.openai_apikey import OpenAICredencial
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from domains.ConexionApis.OpenAi_conexion import OpenAICliente
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from domains.Security.GenerarIDs import GeneradorIDUnico
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class OpenAIEmbedder(EmbedderABC):
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def __init__(self, credencial: OpenAICredencial,
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model: str,
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id: str = None):
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self.model = model
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self.client = OpenAICliente(credencial)
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self._dimension = None # Lazy loading
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self.id = id if id is not None else GeneradorIDUnico("OAMB").generar()
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def encoder(self, text: str) -> List[float]:
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"""
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Genera los embeddings para un texto dado utilizando el modelo de OpenAI.
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"""
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response = self.client.embedding(model=self.model, input=text)
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embedding = response.data[0].embedding
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if self._dimension is None:
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self._dimension = len(embedding)
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return embedding
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def dimension_number(self) -> int:
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"""
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Devuelve la dimensión del modelo de embedding, generando un embedding si no se ha calculado aún.
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"""
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if self._dimension is None:
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_ = self.encoder("dimension_check")
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return self._dimension |